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Refonaa, J., Bandaru Suhas, B. V. S. Bhaskar, S. L. JanyShabu, S. Dhamodaran, Sardar Maran, Maria Anu, and M. Lakshmi. "Fall Detection and Daily Living Activity Recognition Logic Regression." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3520–25. http://dx.doi.org/10.1166/jctn.2020.9223.
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It is a must to bring the fall detection system in to use with the increasing number of elder people in the world, because the most of them tend live voluntarily and at risk of injuries. Falls are dangerous in a few cases and could even lead to deadly injuries. A very robust fall detection system must be built in order to counter this problem. Here, we establish fall detection and recognition of daily live behavior through machine learning system. In order to detect different types of activities, including the detection of falls and day to-day activities, We use 2 shared archives for the accelerating and lateral speed data during this development. Logistic regression is used to determine motions such as drop, walk, climb, sit, stand and lie bases on the accelerating data and data on angular velocities. More specifically, the triaxial acceleration average value is used to achieve fall detection accuracy.
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Belmonte-Fernández, Óscar, Antonio Caballer-Miedes, Eris Chinellato, Raúl Montoliu, Emilio Sansano-Sansano, and Rubén García-Vidal. "Anomaly Detection in Activities of Daily Living with Linear Drift." Cognitive Computation 12, no. 6 (July 1, 2020): 1233–51. http://dx.doi.org/10.1007/s12559-020-09740-6.
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Howedi, Aadel, Ahmad Lotfi, and Amir Pourabdollah. "Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living." Entropy 21, no. 4 (April 19, 2019): 416. http://dx.doi.org/10.3390/e21040416.
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Human Activity Recognition (HAR) is the process of automatically detecting human actions from the data collected from different types of sensors. Research related to HAR has devoted particular attention to monitoring and recognizing the human activities of a single occupant in a home environment, in which it is assumed that only one person is present at any given time. Recognition of the activities is then used to identify any abnormalities within the routine activities of daily living. Despite the assumption in the published literature, living environments are commonly occupied by more than one person and/or accompanied by pet animals. In this paper, a novel method based on different entropy measures, including Approximate Entropy (ApEn), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn), is explored to detect and identify a visitor in a home environment. The research has mainly focused on when another individual visits the main occupier, and it is, therefore, not possible to distinguish between their movement activities. The goal of this research is to assess whether entropy measures can be used to detect and identify the visitor in a home environment. Once the presence of the main occupier is distinguished from others, the existing activity recognition and abnormality detection processes could be applied for the main occupier. The proposed method is tested and validated using two different datasets. The results obtained from the experiments show that the proposed method could be used to detect and identify a visitor in a home environment with a high degree of accuracy based on the data collected from the occupancy sensors.
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Maunder, David, Julien Epps, Eliathamby Ambikairajah, and Branko Celler. "Robust Sounds of Activities of Daily Living Classification in Two-Channel Audio-Based Telemonitoring." International Journal of Telemedicine and Applications 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/696813.
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Despite recent advances in the area of home telemonitoring, the challenge of automatically detecting the sound signatures of activities of daily living of an elderly patient using nonintrusive and reliable methods remains. This paper investigates the classification of eight typical sounds of daily life from arbitrarily positioned two-microphone sensors under realistic noisy conditions. In particular, the role of several source separation and sound activity detection methods is considered. Evaluations on a new four-microphone database collected under four realistic noise conditions reveal that effective sound activity detection can produce significant gains in classification accuracy and that further gains can be made using source separation methods based on independent component analysis. Encouragingly, the results show that recognition accuracies in the range 70%–100% can be consistently obtained using different microphone-pair positions, under all but the most severe noise conditions.
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Iseda, Hikoto, Keiichi Yasumoto, Akira Uchiyama, and Teruo Higashino. "Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags." Sensors 24, no. 11 (May 21, 2024): 3277. http://dx.doi.org/10.3390/s24113277.
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To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3–5 m.
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Pires, Ivan Miguel, Gonçalo Marques, Nuno M. Garcia, Nuno Pombo, Francisco Flórez-Revuelta, Susanna Spinsante, Maria Canavarro Teixeira, and Eftim Zdravevski. "Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices." Electronics 8, no. 12 (December 7, 2019): 1499. http://dx.doi.org/10.3390/electronics8121499.
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The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.
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Javeed, Madiha, Naif Al Mudawi, Abdulwahab Alazeb, Sultan Almakdi, Saud S. Alotaibi, Samia Allaoua Chelloug, and Ahmad Jalal. "Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework." Sensors 23, no. 18 (September 16, 2023): 7927. http://dx.doi.org/10.3390/s23187927.
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Smart home monitoring systems via internet of things (IoT) are required for taking care of elders at home. They provide the flexibility of monitoring elders remotely for their families and caregivers. Activities of daily living are an efficient way to effectively monitor elderly people at home and patients at caregiving facilities. The monitoring of such actions depends largely on IoT-based devices, either wireless or installed at different places. This paper proposes an effective and robust layered architecture using multisensory devices to recognize the activities of daily living from anywhere. Multimodality refers to the sensory devices of multiple types working together to achieve the objective of remote monitoring. Therefore, the proposed multimodal-based approach includes IoT devices, such as wearable inertial sensors and videos recorded during daily routines, fused together. The data from these multi-sensors have to be processed through a pre-processing layer through different stages, such as data filtration, segmentation, landmark detection, and 2D stick model. In next layer called the features processing, we have extracted, fused, and optimized different features from multimodal sensors. The final layer, called classification, has been utilized to recognize the activities of daily living via a deep learning technique known as convolutional neural network. It is observed from the proposed IoT-based multimodal layered system’s results that an acceptable mean accuracy rate of 84.14% has been achieved.
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Lee, Cheolhwan, Ah Hyun Yuh, and Soon Ju Kang. "Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition." Sensors 24, no. 19 (October 4, 2024): 6435. http://dx.doi.org/10.3390/s24196435.
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To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound.
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Bhattacharya, Sarnab, Rebecca Adaimi, and Edison Thomaz. "Leveraging Sound and Wrist Motion to Detect Activities of Daily Living with Commodity Smartwatches." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (July 4, 2022): 1–28. http://dx.doi.org/10.1145/3534582.
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Automatically recognizing a broad spectrum of human activities is key to realizing many compelling applications in health, personal assistance, human-computer interaction and smart environments. However, in real-world settings, approaches to human action perception have been largely constrained to detecting mobility states, e.g., walking, running, standing. In this work, we explore the use of inertial-acoustic sensing provided by off-the-shelf commodity smartwatches for detecting activities of daily living (ADLs). We conduct a semi-naturalistic study with a diverse set of 15 participants in their own homes and show that acoustic and inertial sensor data can be combined to recognize 23 activities such as writing, cooking, and cleaning with high accuracy. We further conduct a completely in-the-wild study with 5 participants to better evaluate the feasibility of our system in practical unconstrained scenarios. We comprehensively studied various baseline machine learning and deep learning models with three different fusion strategies, demonstrating the benefit of combining inertial and acoustic data for ADL recognition. Our analysis underscores the feasibility of high-performing recognition of daily activities using inertial-acoustic data from practical off-the-shelf wrist-worn devices while also uncovering challenges faced in unconstrained settings. We encourage researchers to use our public dataset to further push the boundary of ADL recognition in-the-wild.
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Haghi, Mostafa, Arman Ershadi, and Thomas M. Deserno. "Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking." Sensors 23, no. 4 (February 12, 2023): 2066. http://dx.doi.org/10.3390/s23042066.
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The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
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Siong Jun, Sai, Hafiz Rashidi Ramli, Azura Che Soh, Noor Ain Kamsani, Raja Kamil Raja Ahmad, Siti Anom Ahmad, and Asnor Juraiza Ishak. "Development of fall detection and activity recognition using threshold based method and neural network." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1338. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1338-1347.
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Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
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Quero, Javier, Claire Orr, Shuai Zang, Chris Nugent, Alberto Salguero, and Macarena Espinilla. "Real-time Recognition of Interleaved Activities Based on Ensemble Classifier of Long Short-Term Memory with Fuzzy Temporal Windows." Proceedings 2, no. 19 (October 26, 2018): 1225. http://dx.doi.org/10.3390/proceedings2191225.
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In this paper, we present a methodology for Real-Time Activity Recognition of Interleaved Activities based on Fuzzy Logic and Recurrent Neural Networks. Firstly, we propose a representation of binary-sensor activations based on multiple Fuzzy Temporal Windows. Secondly, an ensemble of activity-based classifiers for balanced training and selection of relevant sensors is proposed. Each classifier is configured as a Long Short-Term Memory with self-reliant detection of interleaved activities. The proposed approach was evaluated using well-known interleaved binary-sensor datasets comprised of activities of daily living.
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Senyurek, Volkan, Masudul Imtiaz, Prajakta Belsare, Stephen Tiffany, and Edward Sazonov. "Electromyogram in Cigarette Smoking Activity Recognition." Signals 2, no. 1 (February 9, 2021): 87–97. http://dx.doi.org/10.3390/signals2010008.
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In this study, information from surface electromyogram (sEMG) signals was used to recognize cigarette smoking. The sEMG signals collected from lower arm were used in two different ways: (1) as an individual predictor of smoking activity and (2) as an additional sensor/modality along with the inertial measurement unit (IMU) to augment recognition performance. A convolutional and a recurrent neural network were utilized to recognize smoking-related hand gestures. The model was developed and evaluated with leave-one-subject-out (LOSO) cross-validation on a dataset from 16 subjects who performed ten activities of daily living including smoking. The results show that smoking detection using only sEMG signal achieved an F1-score of 75% in person-independent cross-validation. The combination of sEMG and IMU improved reached the F1-score of 84%, while IMU alone sensor modality was 81%. The study showed that using only sEMG signals would not provide superior cigarette smoking detection performance relative to IMU signals. However, sEMG improved smoking detection results when combined with IMU signals without using an additional device.
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Syed, Abbas Shah, Daniel Sierra-Sosa, Anup Kumar, and Adel Elmaghraby. "A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition." Sensors 22, no. 7 (March 26, 2022): 2547. http://dx.doi.org/10.3390/s22072547.
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Activity and Fall detection have been a topic of keen interest in the field of ambient assisted living system research. Such systems make use of different sensing mechanisms to monitor human motion and aim to ascertain the activity being performed for health monitoring and other purposes. Towards this end, in addition to activity recognition, fall detection is an especially important task as falls can lead to injuries and sometimes even death. This work presents a fall detection and activity recognition system that not only considers various activities of daily living but also considers detection of falls while taking into consideration the direction and severity. Inertial Measurement Unit (accelerometer and gyroscope) data from the SisFall dataset is first windowed into non-overlapping segments of duration 3 s. After suitable data augmentation, it is then passed on to a Convolutional Neural Network (CNN) for feature extraction with an eXtreme Gradient Boosting (XGB) last stage for classification into the various output classes. The experiments show that the gradient boosted CNN performs better than other comparable techniques, achieving an unweighted average recall of 88%.
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Saeed, Umer, Syed Yaseen Shah, Syed Aziz Shah, Jawad Ahmad, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan, Akram Alomainy, and Qammer H. Abbasi. "Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living." Electronics 10, no. 18 (September 12, 2021): 2237. http://dx.doi.org/10.3390/electronics10182237.
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Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.
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Zaoui, Chaimae, Faouzia Benabbou, Abdelaziz Ettaoufik, and Khadija Sabiri. "Human Activity Recognition Using Convolutional Autoencoder and Advanced Preprocessing." International Journal of Online and Biomedical Engineering (iJOE) 20, no. 04 (March 4, 2024): 144–59. http://dx.doi.org/10.3991/ijoe.v20i04.43623.
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E-health systems rely on information and communication technology to support and improve various aspects of health services, delivery, and management. The success of artificial intelligence techniques has led to the emergence of a variety of systems designed to address a wide range of healthcare issues. In particular, gathering data on patient activity and behavior has enabled the development of reliable predictive systems for detecting chronic diseases and forecasting their progression. Human activity detection is a vast and emerging field, and various datasets have been collected for training different machine learning and deep learning (DL) models. The University of Milano Bicocca smartphone-based human activity recognition (UniMiB-SHAR) dataset is widely used for analyzing and recognizing human actions, including walking, running, and other daily activities. However, the autoencoder (AE) technique trained on this dataset yields poor performance. This paper aims to enhance the performance of AEs on the challenging UniMiB-SHAR dataset by introducing a convolutional AE model and employing novel preprocessing techniques, including normalization, magnitude, principal component analysis (PCA), and balancing methods such as SMOTEEN and ADASYNE. The experimental results demonstrate that the proposed AE model achieved successful performance, surpassing the state-of-the-art methods, with accuracies of 96.56% for activities of daily living (ADL), 98.86% for Fall, and 88.47% for the full dataset.
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Dedabrishvili, Mariam, Natia Mamaiashvili, and Ioseb Matiashvili. "Fall Detection System based on iOS Smartphone Sensors." Journal of Technical Science and Technologies 8, no. 1 (April 30, 2024): 35–44. http://dx.doi.org/10.31578/jtst.v8i1.153.
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Activities of daily living (ADLs) are a crucial aspect of human life, especially in remote health monitoring and fall detection. As smartphones have become an integral part of our daily routines, with their ability to perform complex calculations, connect to the internet, and incorporate various sensors, researchers have been inspired to explore human activity recognition systems. This paper focuses on accelerometer and gyroscope data from iOS-based smartphones. We developed a data collection app to record fall types (e.g., Falling Right, Falling Left) and fall-like activities (e.g., Sitting Fast, Jumping). Volunteers carried smartphones naturally in their pockets during experiments, presenting a challenge of noise but enhancing user comfort. We applied different Machine Learning algorithms (Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbor, XGBoost, LightGBM, and Neural Networks) to analyze the collected dataset. In contrast to typical studies, our approach replicated real-world smartphone usage. The paper presents and analyzes promising results from the study. Furthermore, we implemented the trained model as a real-time mobile application for potential users. This research illustrates the potential of smartphones in fall detection and opens the way for user-friendly solutions in remote health monitoring.
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Gayathri, K. S., K. S. Easwarakumar, and Susan Elias. "Fuzzy Ontology Based Activity Recognition for Assistive Health Care Using Smart Home." International Journal of Intelligent Information Technologies 16, no. 1 (January 2020): 17–31. http://dx.doi.org/10.4018/ijiit.2020010102.
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Assistive health care system is a viable solution for elderly care to offer independent living. Such health care systems are feasible through smart homes, which are intended to enhance the living quality of the occupant. Activities of daily living (ADL) are considered in the design of a smart home and are extended to abnormality detection in the case of health care. Abnormality in occupant behavior is the deviation of ongoing activity with that of the built activity model. Generally, supervised machine learning strategies or knowledge engineering strategies are employed in the process of activity modeling. Supervised machine learning approaches incur overheads in annotating the dataset, while the knowledge modeling approaches incur overhead by being dependent on the domain expert for occupant specific knowledge. The proposed approach on the other hand, employs an unsupervised machine learning strategy to readily extract knowledge from unlabelled data using contextual pattern clustering and subsequently represents it as ontology activity model. Ontology offers enhanced activity recognition through its semantically clear representation and reasoning, it has restriction in handling temporal data. Hence, this article in addition to unsupervised modeling focuses at enabling temporal reasoning within ontology using fuzzy logic. The proposed fuzzy ontology activity recognition (FOAR) framework represents an activity model as a fuzzy temporal ontology. Fuzzy SWRL rules modeled within ontology aid activity recognition and abnormality detection for health care. The experimental results show that the proposed FOAR has better performance in abnormality detection than that of the existing systems.
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Narkhede, Arsh, Hayden Gowing, Tod Vandenberg, Steven Phan, Jason Wong, and Andrew Chan. "Automated Detection of In-Home Activities with Ultra-Wideband Sensors." Sensors 24, no. 14 (July 20, 2024): 4706. http://dx.doi.org/10.3390/s24144706.
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As Canada’s population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring.
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Ghayvat, Hemant, Muhammad Awais, Sharnil Pandya, Hao Ren, Saeed Akbarzadeh, Subhas Chandra Mukhopadhyay, Chen Chen, Prosanta Gope, Arpita Chouhan, and Wei Chen. "Smart Aging System: Uncovering the Hidden Wellness Parameter for Well-Being Monitoring and Anomaly Detection." Sensors 19, no. 4 (February 13, 2019): 766. http://dx.doi.org/10.3390/s19040766.
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Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).
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Wu, Jiaxuan, Yunfei Feng, and Carl K. Chang. "Sound of Daily Living Identification Based on Hierarchical Situation Audition." Sensors 23, no. 7 (April 4, 2023): 3726. http://dx.doi.org/10.3390/s23073726.
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One of the key objectives in developing IoT applications is to automatically detect and identify human activities of daily living (ADLs). Mobile phone users are becoming more accepting of sharing data captured by various built-in sensors. Sounds detected by smartphones are processed in this work. We present a hierarchical identification system to recognize ADLs by detecting and identifying certain sounds taking place in a complex audio situation (AS). Three major categories of sound are discriminated in terms of signal duration. These are persistent background noise (PBN), non-impulsive long sounds (NILS), and impulsive sound (IS). We first analyze audio signals in a situation-aware manner and then map the sounds of daily living (SDLs) to ADLs. A new hierarchical audible event (AE) recognition approach is proposed that classifies atomic audible actions (AAs), then computes pre-classified portions of atomic AAs energy in one AE session, and finally marks the maximum-likelihood ADL label as the outcome. Our experiments demonstrate that the proposed hierarchical methodology is effective in recognizing SDLs and, thus, also in detecting ADLs with a remarkable performance for other known baseline systems.
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Vavoulas, George, Matthew Pediaditis, Charikleia Chatzaki, Emmanouil G. Spanakis, and Manolis Tsiknakis. "The MobiFall Dataset." International Journal of Monitoring and Surveillance Technologies Research 2, no. 1 (January 2014): 44–56. http://dx.doi.org/10.4018/ijmstr.2014010103.
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Fall detection is receiving significant attention in the field of preventive medicine, wellness management and assisted living, especially for the elderly. As a result, several fall detection systems are reported in the research literature or exist as commercial systems. Most of them use accelerometers and/ or gyroscopes attached on a person's body as the primary signal sources. These systems use either discrete sensors as part of a product designed specifically for this task or sensors that are embedded in mobile devices such as smartphones. The latter approach has the advantage of offering well tested and widely available communication services, e.g. for calling emergency when necessary. Nevertheless, automatic fall detection continues to present significant challenges, with the recognition of the type of fall being the most critical. The aim of this work is to introduce a human fall and activity dataset to be used in testing new detection methods, as well as performing objective comparisons between different reported algorithms for fall detection and activity recognition, based on inertial-sensor data from smartphones. The dataset contains signals recorded from the accelerometer and gyroscope sensors of a latest technology smartphone for four different types of falls and nine different activities of daily living. Utilizing this dataset, the results of an elaborate evaluation of machine learning-based fall detection and fall classification are presented and discussed in detail.
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Diete, Alexander, and Heiner Stuckenschmidt. "Fusing Object Information and Inertial Data for Activity Recognition." Sensors 19, no. 19 (September 23, 2019): 4119. http://dx.doi.org/10.3390/s19194119.
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In the field of pervasive computing, wearable devices have been widely used for recognizing human activities. One important area in this research is the recognition of activities of daily living where especially inertial sensors and interaction sensors (like RFID tags with scanners) are popular choices as data sources. Using interaction sensors, however, has one drawback: they may not differentiate between proper interaction and simple touching of an object. A positive signal from an interaction sensor is not necessarily caused by a performed activity e.g., when an object is only touched but no interaction occurred afterwards. There are, however, many scenarios like medicine intake that rely heavily on correctly recognized activities. In our work, we aim to address this limitation and present a multimodal egocentric-based activity recognition approach. Our solution relies on object detection that recognizes activity-critical objects in a frame. As it is infeasible to always expect a high quality camera view, we enrich the vision features with inertial sensor data that monitors the users’ arm movement. This way we try to overcome the drawbacks of each respective sensor. We present our results of combining inertial and video features to recognize human activities on different types of scenarios where we achieve an F 1 -measure of up to 79.6%.
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Xefteris, S., N. Doulamis, V. Andronikou, T. Varvarigou, and G. Cambourakis. "Behavioral Biometrics in Assisted Living: A Methodology for Emotion Recognition." Engineering, Technology & Applied Science Research 6, no. 4 (August 26, 2016): 1035–44. http://dx.doi.org/10.48084/etasr.634.
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Behavioral biometrics aim at providing algorithms for the automatic recognition of individual behavioral traits, stemming from a person’s actions, attitude, expressions and conduct. In the field of ambient assisted living, behavioral biometrics find an important niche. Individuals suffering from the early stages of neurodegenerative diseases (MCI, Alzheimer’s, dementia) need supervision in their daily activities. In this context, an unobtrusive system to monitor subjects and alert formal and informal carers providing information on both physical and emotional status is of great importance and positively affects multiple stakeholders. The primary aim of this paper is to describe a methodology for recognizing the emotional status of a subject using facial expressions and to identify its uses, in conjunction with pre-existing risk-assessment methodologies, for its integration into the context of a smart monitoring system for subjects suffering from neurodegenerative diseases. Paul Ekman’s research provided the background on the universality of facial expressions as indicators of underlying emotions. The methodology then makes use of computational geometry, image processing and graph theory algorithms for the detection of regions of interest and then a neural network is used for the final classification. Findings are coupled with previous published work for risk assessment and alert generation in the context of an ambient assisted living environment based on Service oriented architecture principles, aimed at remote web-based estimation of the cognitive and physical status of MCI and dementia patients.
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Negrete Ramírez, José Manuel, Philippe Roose, Marc Dalmau, Yudith Cardinale, and Edgar Silva. "A DSL-Based Approach for Detecting Activities of Daily Living by Means of the AGGIR Variables." Sensors 21, no. 16 (August 23, 2021): 5674. http://dx.doi.org/10.3390/s21165674.
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In this paper, we propose a framework for studying the AGGIR (Autonomie Gérontologique et Groupe Iso Ressources—Autonomy Gerontology Iso-Resources Groups) grid model, with the aim of assessing the level of independence of elderly people in accordance with their capabilities of performing daily activities as well as interacting with their environments. In order to model the Activities of Daily Living (ADL), we extend a previously proposed Domain Specific Language (DSL), by defining new operators to deal with constraints related to time and location of activities and event recognition. The proposed framework aims at providing an analysis tool regarding the performance of elderly/disabled people within a home environment by means of data recovered from sensors using a smart-home simulator environment. We perform an evaluation of our framework in several scenarios, considering five of the AGGIR variables (i.e., feeding, dressing, toileting, elimination, and transfers) as well as health-care devices for tracking the occurrence of elderly activities. The results demonstrate the accuracy of the proposed framework for managing the tracked records correctly and, thus, generate the appropriate event information related to the ADL.
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Lopez-Nava, Irvin Hussein, Matias Garcia-Constantino, and Jesus Favela. "Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device." Proceedings 31, no. 1 (November 21, 2019): 60. http://dx.doi.org/10.3390/proceedings2019031060.
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Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts.
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Seyedkazemi Ardebili, E., S. Eken, and K. Küçük. "ACTIVITY RECOGNITION FOR AMBIENT SENSING DATA AND RULE BASED ANOMALY DETECTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 379–82. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-379-2020.
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Abstract. After a brief look at the smart home, we conclude that to have a smart home, and it is necessary to have an intelligent management center. In this article, We have tried to make it possible for the smart home management center to be able to detect the presence of an abnormal state in the behavior of someone who lives in the house. In the proposed method, the daily algorithm examines the rate of changes of a person and provides a number which is henceforth called NNC (Number of normal changes) based on the person’s behavioral changes. We achieve the NNC number using a machine learning algorithm and performing a series of several simple statistical and mathematical calculations. NNC is a number that shows abnormal changes in residents’ behaviors in a smart home, i.e., this number is a small number for a regular person with constant planning and for a person who may not have any fixed principles and regular in personal life is a big number.To increase our accuracy in calculating NNC, we review all common machine learning algorithms and after tests we choose the decision tree because of its higher accuracy and speed and finally, NNC number is obtained by combining the Decision Tree algorithm with statistical and mathematical methods. In this method, we present a set of states and information obtained from the sensors along with the activities performed by the occupant of the house over a period of several days to the proposed algorithm. and the method ahead generates the main NNC number for those days for anyone living in a smart home. To generate this main NNC, we calculate each person’s daily NNC. That means we have daily NNCs for each person (based on his/her behaviors on that day) and the main NNC is the average of these daily NNC. We chose ARAS dataset (Human Activity Datasets in Multiple Homes with Multiple Residents) to implement our method and after tests and replications on the ARAS dataset, and to find anomalies in each person’s behavior in a day, we compare the main (average) NNC with that person’s daily NNC on that day. Finally, we can say, if the main NNC changes more than 30%, there is a possibility of an abnormality. and if the NNC changes more than 60% percent, we can say that an abnormal state or an uncommon event happened that day, and a declaration of an abnormal state will be issued to the resident of the house.
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Kańtoch, Eliasz. "Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk." Sensors 18, no. 10 (September 24, 2018): 3219. http://dx.doi.org/10.3390/s18103219.
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With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.
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Zhang, Yiyuan, Ine D’Haeseleer, José Coelho, Vero Vanden Abeele, and Bart Vanrumste. "Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations." Sensors 21, no. 6 (March 20, 2021): 2176. http://dx.doi.org/10.3390/s21062176.
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This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.
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Karakostas, Anastasios, Alexandra König, Carlos Fernando Crispim-Junior, François Bremond, Alexandre Derreumaux, Ioulietta Lazarou, Ioannis Kompatsiaris, Magda Tsolaki, and Philippe Robert. "A French–Greek Cross-Site Comparison Study of the Use of Automatic Video Analyses for the Assessment of Autonomy in Dementia Patients." Biosensors 10, no. 9 (August 21, 2020): 103. http://dx.doi.org/10.3390/bios10090103.
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Background: At present, the assessment of autonomy in daily living activities, one of the key symptoms in Alzheimer’s disease (AD), involves clinical rating scales. Methods: In total, 109 participants were included. In particular, 11 participants during a pre-test in Nice, France, and 98 participants (27 AD, 38 mild cognitive impairment—MCI—and 33 healthy controls—HC) in Thessaloniki, Greece, carried out a standardized scenario consisting of several instrumental activities of daily living (IADLs), such as making a phone call or preparing a pillbox while being recorded. Data were processed by a platform of video signal analysis in order to extract kinematic parameters, detecting activities undertaken by the participant. Results: The video analysis data can be used to assess IADL task quality and provide clinicians with objective measurements of the patients’ performance. Furthermore, it reveals that the HC statistically significantly outperformed the MCI, which had better performance compared to the AD participants. Conclusions: Accurate activity recognition data for the analyses of the performance on IADL activities were obtained.
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Yan, Jianjun, Xueqiang Wang, Jiangtao Shi, and Shuai Hu. "Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks." Sensors 23, no. 4 (February 14, 2023): 2153. http://dx.doi.org/10.3390/s23042153.
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The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.
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Davis, Jensen, Shannon Howard, Gregory King, Phanidar Boddu, Kiran Jyothi, and Joan McDowd. "ALEXA, ASSESS MY MEMORY: THE FEASIBILITY OF EXTENDED HEALTH MONITORING IN AN OLDER-ADULT-LIVING COMMUNITY." Innovation in Aging 3, Supplement_1 (November 2019): S337. http://dx.doi.org/10.1093/geroni/igz038.1224.
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Abstract The goal of most older adults is to live independently in their own homes, for as long as possible. There are many advantages to aging in place for the individual, but also challenges as changes in cognitive and physical health can occur over time. Especially for older adults living alone, tracking these changes is critical for early intervention and prevention. The relatively easy availability of consumer technology may provide one mechanism for monitoring older adults in their homes. We designed a pilot study to test the feasibility and acceptability of using wearable sensors (Fitbit sensors), in conjunction with automated interactive voice recognition technology (Amazon Echo), to monitor older adults’ physical and cognitive health during daily activities. Participants (7 females, 2 males; 65-80 years of age) were recruited from a housing complex for older adults with low income. They were interviewed about health monitoring technology before and after a 2-week measurement period during which they were expected to wear the Fitbit daily and interact with the Amazon Echo for 8 consecutive days. Feasibility challenges included limited skill in Echo interactions, remembering to do the assessments, and charging/uploading Fitbit data. Qualitative analysis of interviews revealed generally positive attitudes about technology, but low comfort operating the devices. These preliminary findings suggest that with additional training for older adults, sensors and voice recognition technologies could have significant roles in maintaining older adult quality of life by contributing to early detection of decline and timely intervention.
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Arshad, Muhammad Haseeb, Muhammad Bilal, and Abdullah Gani. "Human Activity Recognition: Review, Taxonomy and Open Challenges." Sensors 22, no. 17 (August 27, 2022): 6463. http://dx.doi.org/10.3390/s22176463.
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Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.
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Papadogiorgaki, Maria, Nikos Grammalidis, Athina Grammatikopoulou, Konstantinos Apostolidis, Ekaterini S. Bei, Kostas Grigoriadis, Stylianos Zafeiris, George Livanos, Vasileios Mezaris, and Michalis E. Zervakis. "An Integrated Support System for People with Intellectual Disability." Electronics 12, no. 18 (September 8, 2023): 3803. http://dx.doi.org/10.3390/electronics12183803.
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People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. In this paper, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special user needs of this population. Furthermore, the proposed platform integrates capabilities for the automatic recognition of health-related emergencies, such as fever, oxygen saturation decline, and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing appropriate notifications to caregivers and automated assistance to people with ID through voice instructions and interaction with a virtual assistant. A short-scale pilot study has been carried out, where a group of end-users participated in the testing of the integrated platform, verifying its effectiveness concerning the recommended services. The experimental results indicate the potential value of the proposed system in providing routine health measurements, identifying and managing emergency cases, and supporting a creative and qualitative daily life for people with disabilities.
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Qiu, Yuting, James Meng, and Baihua Li*. "Automated Falls Detection Using Visual Anomaly Detection and Pose-based Approaches: Experimental Review and Evaluation." Journal of Biomedical Research & Environmental Sciences 5, no. 1 (January 2024): 055–63. http://dx.doi.org/10.37871/jbres1872.
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Falls are a pervasive problem facing elderly populations, associated with significant morbidity and mortality. Prompt recognition of falls, especially in elderly people with cognitive or physical impairments who cannot raise the alarm themselves, is a challenge. To this end, wearable sensors can be used to detect fall behaviour, including smartwatches and wristbands. These devices are limited by their intrusiveness, require user compliance and have issues around endurance and comfort, reducing their effectiveness in elderly populations. They can also only target patients already recognised as falls risks, and cannot apply to non-identified patients. Leveraging state of the art AI deep learning, we introduce two types of automated fall detection techniques using visual information from cameras: 1) self-supervised autoencoder, distinguishing falls from normal behaviour as an anomaly detection problem, 2) supervised human posture-based fall activity recognition. Five models are trained and evaluated based on two publicly available video datasets, composed of activities of daily living and simulated falls in an office-like environment. To test the models for real-world fall detection, we developed two new datasets, including videos of real falls in elderly people, and more complex backgrounds and scenarios. The experimental results show autoencoder detectors are able to predict falls directly from images where the background is pre-learned. While the pose-based approach uses foreground body pose only for AI learning, better targeting complex scenarios and backgrounds. Video-based methods could be a potential for low-cost and non-invasive falls detection, increasing safety in care environments, while also helping elderly people retain independence in their own homes.
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Condado, Paulo A., and Fernando G. Lobo. "Security and privacy concerns in assisted living environments." Journal of Smart Cities and Society 2, no. 2 (August 23, 2023): 99–121. http://dx.doi.org/10.3233/scs-230015.
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Assisted living environments use interconnected devices to assist people with limitations in performing daily activities. In these environments, human activity recognition is critical for detecting abnormal situations, such as falls or health problems, and providing appropriate assistance to inhabitants. Despite their advantages, assisted living environments raise security and privacy concerns due to the collection and storage of sensitive data about their inhabitants. This paper addresses security and privacy concerns related to intelligent environments designed to assist individuals with limitations. It discusses the weaknesses of IoT devices and domotic technologies and presents research conducted by authors to mitigate these issues. This study shows that, despite hardware constraints, it is possible to design a relatively secure assisted living environment that prevents hacker attacks and data leaks. Additionally, it is essential to comprehend which information should be shared with external entities, such as health care services, and when to share it to ensure the inhabitants’ well-being. Our main goal has been to gather knowledge to improve the privacy and data protection of technology-rich assisted living environments implemented to assist people with limitations and their family members in performing their daily tasks.
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Saeed, Aaqib, Tanir Ozcelebi, and Johan Lukkien. "Synthesizing and Reconstructing Missing Sensory Modalities in Behavioral Context Recognition." Sensors 18, no. 9 (September 6, 2018): 2967. http://dx.doi.org/10.3390/s18092967.
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Detection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user’s context in the daily-life situation a system needs to learn from multimodal data that are often imbalanced, and noisy with missing values. The model is likely to encounter missing sensors in real-life conditions as well (such as a user not wearing a smartwatch) and it fails to infer the context if any of the modalities used for training are missing. In this paper, we propose a method based on an adversarial autoencoder for handling missing sensory features and synthesizing realistic samples. We empirically demonstrate the capability of our method in comparison with classical approaches for filling in missing values on a large-scale activity recognition dataset collected in-the-wild. We develop a fully-connected classification network by extending an encoder and systematically evaluate its multi-label classification performance when several modalities are missing. Furthermore, we show class-conditional artificial data generation and its visual and quantitative analysis on context classification task; representing a strong generative power of adversarial autoencoders.
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Ortíz-Barrios, Miguel Angel, Ian Cleland, Chris Nugent, Pablo Pancardo, Eric Järpe, and Jonathan Synnott. "Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling." Remote Sensing 12, no. 5 (February 28, 2020): 771. http://dx.doi.org/10.3390/rs12050771.
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Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R pred 2 = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables.
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Akbari, Ali, Jonathan Martinez, and Roozbeh Jafari. "Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches." ACM Transactions on Embedded Computing Systems 20, no. 2 (March 2021): 1–20. http://dx.doi.org/10.1145/3431503.
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Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.
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Karvonen, Niklas, and Denis Kleyko. "A Domain Knowledge-Based Solution for Human Activity Recognition: The UJA Dataset Analysis." Proceedings 2, no. 19 (October 19, 2018): 1261. http://dx.doi.org/10.3390/proceedings2191261.
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Detecting activities of daily living (ADL) allows for rich inference about user behavior, which can be of use in the care of for example, elderly people, chronic diseases, and psychological conditions. This paper proposes a domain knowledge-based solution for detecting 24 different ADLs in the UJA dataset. The solution is inspired by a Finite State Machine and performs activity recognition unobtrusively using only binary sensors. Each day in the dataset is segmented into: morning, day, evening in order to facilitate the inference from the sensors. The model performs the ADL recognition in two steps. The first step is to detect the sequence of activities in a given event stream of binary sensors, and the second step is to assign a starting and ending times for each of detected activities. Our proposed model achieved an accuracy of 81.3% using only a very small amount of operations, making it an interesting approach for resource-constrained devices that are common in smart environments. It should be noted, however, that the model can end up in faulty states which could cause a series of mis-classifications before the model is returned to the true state.
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Leon, Beatriz, Angelo Basteris, Francesco Infarinato, Patrizio Sale, Sharon Nijenhuis, Gerdienke Prange, and Farshid Amirabdollahian. "Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy." BioMed Research International 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/318016.
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Stroke survivors often suffer impairments on their wrist and hand. Robot-mediated rehabilitation techniques have been proposed as a way to enhance conventional therapy, based on intensive repeated movements. Amongst the set of activities of daily living, grasping is one of the most recurrent. Our aim is to incorporate the detection of grasps in the machine-mediated rehabilitation framework so that they can be incorporated into interactive therapeutic games. In this study, we developed and tested a method based on support vector machines for recognizing various grasp postures wearing a passive exoskeleton for hand and wrist rehabilitation after stroke. The experiment was conducted with ten healthy subjects and eight stroke patients performing the grasping gestures. The method was tested in terms of accuracy and robustness with respect to intersubjects’ variability and differences between different grasps. Our results show reliable recognition while also indicating that the recognition accuracy can be used to assess the patients’ ability to consistently repeat the gestures. Additionally, a grasp quality measure was proposed to measure the capabilities of the stroke patients to perform grasp postures in a similar way than healthy people. These two measures can be potentially used as complementary measures to other upper limb motion tests.
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Han, Kun, Qiongqian Yang, and Zefan Huang. "A Two-Stage Fall Recognition Algorithm Based on Human Posture Features." Sensors 20, no. 23 (December 5, 2020): 6966. http://dx.doi.org/10.3390/s20236966.
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Falls are seriously threatening the health of elderly. In order to reduce the potential danger caused by falls, this paper proposes a two-stage fall recognition algorithm based on human posture features. For preprocessing, we construct the new key features: deflection angles and spine ratio to describe the changes of human posture based on the human skeleton extracted by OpenPose. In the first stage, based on the variables: tendency symbol and steady symbol integrated by the scattered key features, we divide the human body state into three states: stable state, fluctuating state, and disordered state. By analyzing whether the body is in a stable state, the ADL (activities of daily living) actions with high stability can be preliminarily excluded. In the second stage: to further identify the confusing ADL actions and the fall actions, we innovatively design a time-continuous recognition algorithm. When human body is constantly in an unstable state, the human posture features: compare value γ, energy value ε, state score τ are proposed to form a feature vector, and support vector machine (SVM), K nearest neighbors (KNN), decision tree (DT), random forest (RF) are utilized for classification. Experiment results demonstrate that SVM with linear kernel function can distinguish falling actions best and our approach achieved a detection accuracy of 97.34%, precision of 98.50%, and the recall, F1 score are 97.33%, 97.91% respectively. Compared with previous state-of-art algorithms, our algorithm can achieve the highest recognition accuracy. It proves that our fall detection method is effective.
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Casilari, Eduardo, Moisés Álvarez-Marco, and Francisco García-Lagos. "A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems." Symmetry 12, no. 4 (April 20, 2020): 649. http://dx.doi.org/10.3390/sym12040649.
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Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.
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Prabhakar, Ashish John, Srikanth Prabhu, Aayush Agrawal, Siddhisa Banerjee, Abraham M. Joshua, Yogeesh Dattakumar Kamat, Gopal Nath, and Saptarshi Sengupta. "Use of Machine Learning for Early Detection of Knee Osteoarthritis and Quantifying Effectiveness of Treatment Using Force Platform." Journal of Sensor and Actuator Networks 11, no. 3 (August 23, 2022): 48. http://dx.doi.org/10.3390/jsan11030048.
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Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression.
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Mihoub, Alaeddine. "A Deep Learning-Based Framework for Human Activity Recognition in Smart Homes." Mobile Information Systems 2021 (September 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/6961343.
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Human behavior modeling in smart environments is a growing research area treating several challenges related to ubiquitous computing, pattern recognition, and ambient assisted living. Thanks to recent progress in sensing devices, it is now possible to design computational models able of accurate detection of residents’ activities and daily routines. For this goal, we introduce in this paper a deep learning-based framework for activity recognition in smart homes. This framework proposes a detailed methodology for data preprocessing, feature mining, and deep learning techniques application. The novel framework was designed to ensure a deep exploration of the feature space since three main approaches are tested, namely, the all-features approach, the selection approach, and the reduction approach. Besides, the framework proposes the evaluation and the comparison of several well-chosen deep learning techniques such as autoencoder, recurrent neural networks (RNN), and some of their derivatives models. Concretely, the framework was applied on the “Orange4Home” dataset which represents a recent dataset specially designed for smart homes research. Our main findings show that the best approach for efficient classification is the selection approach. Furthermore, our overall results outperformed baseline models based on random forest classifiers and the principal component analysis technique, especially the results of our RNN-based model for the all-features approach and the results of our autoencoder-based model for the feature reduction approach.
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Shahid, Zahraa Khais, Saguna Saguna, and Christer Åhlund. "Detecting Anomalies in Daily Activity Routines of Older Persons in Single Resident Smart Homes: Proof-of-Concept Study." JMIR Aging 5, no. 2 (April 11, 2022): e28260. http://dx.doi.org/10.2196/28260.
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Background One of the main challenges of health monitoring systems is the support of older persons in living independently in their homes and with relatives. Smart homes equipped with internet of things devices can allow older persons to live longer in their homes. Previous surveys used to identify sensor-based data sets in human activity recognition systems have been limited by the use of public data set characteristics, data collected in a controlled environment, and a limited number of older participants. Objective The objective of our study is to build a model that can learn the daily routines of older persons, detect deviations in daily living behavior, and notify these anomalies in near real-time to relatives. Methods We extracted features from large-scale sensor data by calculating the time duration and frequency of visits. Anomalies were detected using a parametric statistical approach, unusually short or long durations being detected by estimating the mean (μ) and standard deviation (σ) over hourly time windows (80 to 355 days) for different apartments. The confidence level is at least 75% of the tested values within two (σ) from the mean. An anomaly was triggered where the actual duration was outside the limits of 2 standard deviations (μ−2σ, μ+2σ), activity nonoccurrence, or absence of activity. Results The patterns detected from sensor data matched the routines self-reported by users. Our system observed approximately 1000 meals and bathroom activities and notifications sent to 9 apartments between July and August 2020. A service evaluation of received notifications showed a positive user experience, an average score of 4 being received on a 1 to 5 Likert-like scale. One was poor, two fair, three good, four very good, and five excellent. Our approach considered more than 75% of the observed meal activities were normal. This figure, in reality, was 93%, normal observed meal activities of all participants falling within 2 standard deviations of the mean. Conclusions In this research, we developed, implemented, and evaluated a real-time monitoring system of older participants in an uncontrolled environment, with off-the-shelf sensors and internet of things devices being used in the homes of older persons. We also developed an SMS-based notification service and conducted user evaluations. This service acts as an extension of the health/social care services operated by the municipality of Skellefteå provided to older persons and relatives.
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Madokoro, Hirokazu, Stephanie Nix, Hanwool Woo, and Kazuhito Sato. "A Mini-Survey and Feasibility Study of Deep-Learning-Based Human Activity Recognition from Slight Feature Signals Obtained Using Privacy-Aware Environmental Sensors." Applied Sciences 11, no. 24 (December 12, 2021): 11807. http://dx.doi.org/10.3390/app112411807.
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Numerous methods and applications have been proposed in human activity recognition (HAR). This paper presents a mini-survey of recent HAR studies and our originally developed benchmark datasets of two types using environmental sensors. For the first dataset, we specifically examine human pose estimation and slight motion recognition related to activities of daily living (ADL). Our proposed method employs OpenPose. It describes feature vectors without effects of objects or scene features, but with a convolutional neural network (CNN) with the VGG-16 backbone, which recognizes behavior patterns after classifying the obtained images into learning and verification subsets. The first dataset comprises time-series panoramic images obtained using a fisheye lens monocular camera with a wide field of view. We attempted to recognize five behavior patterns: eating, reading, operating a smartphone, operating a laptop computer, and sitting. Even when using panoramic images including distortions, results demonstrate the capability of recognizing properties and characteristics of slight motions and pose-based behavioral patterns. The second dataset was obtained using five environmental sensors: a thermopile sensor, a CO2 sensor, and air pressure, humidity, and temperature sensors. Our proposed sensor system obviates the need for constraint; it also preserves each subject’s privacy. Using a long short-term memory (LSTM) network combined with CNN, which is a deep-learning model dealing with time-series features, we recognized eight behavior patterns: eating, operating a laptop computer, operating a smartphone, playing a game, reading, exiting, taking a nap, and sitting. The recognition accuracy for the second dataset was lower than for the first dataset consisting of images, but we demonstrated recognition of behavior patterns from time-series of weak sensor signals. The recognition results for the first dataset, after accuracy evaluation, can be reused for automatically annotated labels applied to the second dataset. Our proposed method actualizes semi-automatic annotation, false recognized category detection, and sensor calibration. Feasibility study results show the new possibility of HAR used for ADL based on unique sensors of two types.
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Qadir, Muhammad Usman, Izhar Ul Haq, Muhammad Awais Khan, Kamran Shah, Houssam Chouikhi, and Mohamed A. Ismail. "Design, Analysis, and Development of Low-Cost State-of-the-Art Magnetorheological-Based Microprocessor Prosthetic Knee." Sensors 24, no. 1 (January 1, 2024): 255. http://dx.doi.org/10.3390/s24010255.
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For amputees, amputation is a devastating experience. Transfemoral amputees require an artificial lower limb prosthesis as a replacement for regaining their gait functions after amputation. Microprocessor-based transfemoral prosthesis has gained significant importance in the last two decades for the rehabilitation of lower limb amputees by assisting them in performing activities of daily living. Commercially available microprocessor-based knee joints have the needed features but are costly, making them beyond the reach of most amputees. The excessive cost of these devices can be attributed to custom sensing and actuating mechanisms, which require significant development cost, making them beyond the reach of most amputees. This research contributes to developing a cost-effective microprocessor-based transfemoral prosthesis by integrating off-the-shelf sensing and actuating mechanisms. Accordingly, a three-level control architecture consisting of top, middle, and low-level controllers was developed for the proposed prosthesis. The top-level controller is responsible for identifying the amputee intent and mode of activity. The mid-level controller determines distinct phases in the activity mode, and the low-level controller was designed to modulate the damping across distinct phases. The developed prosthesis was evaluated on unilateral transfemoral amputees. Since off-the-shelf sensors and actuators are used in i-Inspire, various trials were conducted to evaluate the repeatability of the sensory data. Accordingly, the mean coefficients of correlation for knee angle, force, and inclination were computed at slow and medium walking speeds. The obtained values were, respectively, 0.982 and 0.946 for knee angle, 0.942 and 0.928 for knee force, and 0.825 and 0.758 for knee inclination. These results confirmed that the data are highly correlated with minimum covariance. Accordingly, the sensors provide reliable and repeatable data to the controller for mode detection and intent recognition. Furthermore, the knee angles at self-selected walking speeds were recorded, and it was observed that the i-Inspire Knee maintains a maximum flexion angle between 50° and 60°, which is in accordance with state-of-the-art microprocessor-based transfemoral prosthesis.
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Wu, Jiaxuan, Yunfei Feng, and Peng Sun. "Sensor Fusion for Recognition of Activities of Daily Living." Sensors 18, no. 11 (November 19, 2018): 4029. http://dx.doi.org/10.3390/s18114029.
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Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.
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Ihianle, Isibor Kennedy, Usman Naeem, and Abdel-Rahman Tawil. "Recognition of Activities of Daily Living from Topic Model." Procedia Computer Science 98 (2016): 24–31. http://dx.doi.org/10.1016/j.procs.2016.09.007.
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