Literatura académica sobre el tema "Detection and recognition of activities of daily living"
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Artículos de revistas sobre el tema "Detection and recognition of activities of daily living"
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Refonaa, J., Bandaru Suhas, B. V. S. Bhaskar, S. L. JanyShabu, S. Dhamodaran, Sardar Maran, Maria Anu y M. Lakshmi. "Fall Detection and Daily Living Activity Recognition Logic Regression". Journal of Computational and Theoretical Nanoscience 17, n.º 8 (1 de agosto de 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 y Rubén García-Vidal. "Anomaly Detection in Activities of Daily Living with Linear Drift". Cognitive Computation 12, n.º 6 (1 de julio de 2020): 1233–51. http://dx.doi.org/10.1007/s12559-020-09740-6.
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Howedi, Aadel, Ahmad Lotfi y Amir Pourabdollah. "Exploring Entropy Measurements to Identify Multi-Occupancy in Activities of Daily Living". Entropy 21, n.º 4 (19 de abril de 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 y 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 y Teruo Higashino. "Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags". Sensors 24, n.º 11 (21 de mayo de 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 y Eftim Zdravevski. "Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices". Electronics 8, n.º 12 (7 de diciembre de 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 y Ahmad Jalal. "Intelligent ADL Recognition via IoT-Based Multimodal Deep Learning Framework". Sensors 23, n.º 18 (16 de septiembre de 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 y Soon Ju Kang. "Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition". Sensors 24, n.º 19 (4 de octubre de 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 y 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, n.º 2 (4 de julio de 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 y Thomas M. Deserno. "Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking". Sensors 23, n.º 4 (12 de febrero de 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.
Tesis sobre el tema "Detection and recognition of activities of daily living"
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Peng, Yingli. "Improvement of Data Mining Methods on Falling Detection and Daily Activities Recognition". Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25521.
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With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.
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Viard, Kévin. "Modelling and Recognition of Human Activities of Daily Living in a Smart Home". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLN022/document.
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La plupart des travaux réalisés dans le domaine de l'assistance à l'autonomie à domicile (AAL) reposent sur l'utilisation de capteurs visuels et audio tels que des caméras. Or, ces capteurs sont souvent rejetés par le patient à cause de leur caractère invasif. Des approches alternatives requièrent l'utilisation de capteurs embarqués sur la personne (GPS, bracelets électroniques ou puces RFID dans les vêtements...), et leur pertinence est donc ramenée à l'hypothèse que les personnes les portent effectivement, sans jamais les rejeter ni les oublier. Pour ces raisons, dans cette thèse, nous trouvons plus pertinentes les approches uniquement basées sur l'utilisation de capteurs binaires intégrés dans l'habitat, tels que les détecteurs de mouvements, les tapis sensitifs ou les barrières optiques. Dans un tel contexte technologique, il devient intéressant d'utiliser les paradigmes, les modèles et les outils des systèmes à événement Discrets (SED), initialement plutôt développés pour la modélisation, l'analyse et la commande des systèmes industriels complexes. Dans ces travaux de thèse, l'objectif est de construire une approche pour la modélisation et le suivi des habitudes de vie, basée sur les modèles et les paradigmes des SED et répondant à une problématique qui s'énonce de la manière suivante : L'objectif est de développer un cadre global pour découvrir et reconnaître les activités de la vie quotidienne d'un habitant vivant seul dans une maison intelligente. Cette maison intelligente doit être équipée uniquement de capteurs binaires, l'étiquetage par des experts des activités observées ne doit pas être nécessaire et les activités peuvent être représentées par des modèles probabilistes. La première méthode présentée dans cette thèse permet, à partir d'une base de données d'apprentissage et d'une description experte des activités à modéliser listées par des médecins, de construire pour chaque activité un modèle sous la forme d'un automate à état-fini probabiliste (PFA). La seconde méthode développée lors de cette thèse permet d'estimer en temps réel, à partir des seules données observées par les capteurs ambiants, quelle activité la personne observée réalise effectivement. Les méthodes décrites dans cette thèse sont illustrées en utilisant les données générées localement via l'utilisation d'un appartement prêté par ENS Paris-Saclay équipé pour répondre aux besoins expérimentaux de cette thèseMost of the work done in the field of ambient assisted living (AAL) is based on the use of visual and audio sensors such as cameras. However, these sensors are often rejected by the patient because of their invasiveness. Alternative approaches require the use of sensors embedded on the person (GPS, electronic wristbands or RFID chips in clothing ...), and their relevance is therefore reduced to the assumption that people actually wear them, without rejecting nor forgetting them. For these reasons, in this thesis, we find more relevant the approaches based on the use of binary sensors integrated into the habitat only, such as motion detectors, sensory mats or optical barriers. In such a technological context, it becomes interesting to use paradigms, models and tools of Discrete Event Systems (DES), initially developed for modeling, analysis and control of complex industrial systems. In this thesis work, the goal is to build an activity of daily living modeling and monitoring approach, based on the models and the paradigm of the DES and answering a problem that is expressed as follows:The objective is to develop a global framework to discover and recognise activities of daily living of an inhabitant living alone in a smart home. This smart home have to be equipped with binary sensors only, expert labeling of activities should not be needed and activities can be represented by probabilistic models. The first method presented in this thesis allows to build a probabilistic finite-state automata (PFA) from a learning database and an expert description of the activities to be modeled given by the medical staff. The second method developed during this thesis estimates, according to the observations, the activity performed by the monitored inhabitant. The methods described in this thesis are applied on data generated using an apartment lent by ENS Paris-Saclay and equipped according the experimental needs of this thesis
La maggior parte dei lavori nel settore dell’Ambient Assisted Living (AAL) si basasull’uso di sensori visivi e audio come le telecamere. Tuttavia, questi sensori sonospesso rifiutati dal paziente a causa della loro natura invasiva. Gli approcci alternativi richiedono l’uso di sensori integrati nella persona (GPS, bracciali elettronici o chipRFID...), e la loro rilevanza è quindi ridotta all’ipotesi che le persone li indossino effettivamente, senza mai rifiutarli o dimenticarli.Per questi motivi, in questa tesi, troviamo approcci più rilevanti basati esclusivamente sull’uso di sensori binari integrati nell’habitat, come rilevatori di movimento,tappeti sensoriali o barriere fotoelettriche.In tale contesto tecnologico, diventa interessante utilizzare i paradigmi, i modelli egli strumenti dei sistemi ad eventi discreti (SED), inizialmente sviluppati per la modellazione, l’analisi e il controllo di sistemi industriali complessi.In questo lavoro di tesi, l’obiettivo è quello di presentare un metodo per la modellazione e il monitoraggio delle abitudini di vita, basato sui modelli e paradigmi di SEDe rispondendo ad un problema che si esprime come segue : L’obiettivo è quello di sviluppare un quadro globale per rivelare e riconoscere le attività della vita quotidiana di una persona che abita da sola in una smart home chedovrebbe essere dotata solo di sensori binari. Inoltre si suppone che non sia necessarial’etichettatura delle attività osservate da parte di un esperto e tali attività sono rappresentate da modelli probabilistici.Il primo metodo presentato in questa tesi permette di costruire un modello probabilistico di automa a stati finiti (PFA) ottenuto da un database di apprendimento e unadescrizione delle attività da parte di medici. Il secondo metodo sviluppato in questa tesi stima, alla luce delle osservazioni, qualeattività svolge la persona osservata. I metodi descritti sono illustrati utilizzando dati generati localmente attraverso l’usodi un appartamento messo a disposizione da ENS Paris-Saclay e attrezzato per soddisfarele esigenze sperimentali di questa tesi
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Ball, Stephen. "Investigating telemonitoring technologies for the detection of activities and the application of BLE in smart homes for elderly independent living". Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/123510/1/Stephen%20Ball%20Thesis.pdf.
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Due to the rise in the elderly population and the prevalence of chronic diseases, healthcare organizations around the world are faced with an economic burden which will continue to grow. For this reason there is an urgent demand to reduce the intake of elders in hospitals and nursing homes by allowing them to live independently for greater lengths of time. In response to this demand, researchers are strongly focusing on 'telemonitoring', which is the use of information technology (IT) to monitor the health status of an individual from a remote location (e.g. their home).
The first aim of this thesis was to investigate how telemonitoring technologies can detect elderly activities for health assessment purposes. To assess a patient's health status holistically, a wide variety of factors needed be considered by practitioners. Currently many telemonitoring technologies in research have addressed these factors/assessments by monitoring elderly activities. However based on the literature reviews, researchers have not been able to develop a comprehensive understanding of how these technologies support each assessment.
Therefore the first contribution in chapter 3 of this thesis addresses this gap. A literature review was conducted where 215 telemonitoring technologies were identified from 82 papers, published between 2000 and 2016. Six assessments which involve monitoring of activities were identified as (1) mobility, (2) nutrition, (3) safety, (4) cognitive, (5) social, and (6) routine. All included technologies were categorized into six tables according to the assessment that they supported. From assessing the contents of these tables, it was found that a significant portion of ITs relate to mobility, nutritional, safety and routine assessments. Many of the studies were found to assess technologies inside of laboratory setting and still require improvement before they are suited for real world application. It also found that many of the technologies were not equipped with wireless communication.
In recent years recently have been focusing been integrating wireless sensing technology into telemonitoring applications. Many of these wireless technologies are small, unobtrusive, and usually need to be powered by small batteries (e.g. coin cell) which have limited capacity. For this reason, researchers have had difficulties prolonging battery life to a duration that is practical. However the recent release of Bluetooth Low Energy (BLE) has the potential of resolving this issue due to its power saving qualities.
The 2nd aim of this thesis is to assess the performance of Bluetooth Low Energy (BLE) in telemonitoring frameworks using advertising mode. Advertising mode is often used for device discovery purposes, however it can also be used to send context data without the need for device connection establishment. This method has received little investigation from researchers and yet it has the potential of offering advantages such as reduction in power consumption and manufacturing costs. Therefore in this thesis, the performance of BLE advertising mode was used within two telemonitoring applications.
Firstly, a new device called 'BLUESOUND' is proposed. The device uses ultrasound sensing technology which can efficiently differentiate multiple residents in a home environment based on their height. The device consists of three sensing/communication modules: A Passive Infrared (PIR) occupancy module, an ultrasound array module and a BLE communication module. The PIR occupancy module is used to detect walking direction, while the ultrasound array measures the resident's height. The combination of these two technologies can also be used to detect a resident's velocity. BLE advertising mode is used to communicate acquired data to a smart phone gateway/database. A new embedded algorithm was able to increase the energy efficiency of the identification technology. Comprehensive modelling and experimentation was undertaken to assess the performance the BLUESOUND device. The BLUESOUND device was able to distinguish between multiple resident identities by measuring height accurately.
Currently researchers have developed various wearable ECG monitors as there is a demand to detect the onset of cardiac disease earlier in the elderly population. However most of these devices have only lasted a couple of days on a coin cell battery which is not practical. Therefore the performance of BLE advertising mode was explored using a virtual BLE based ECG model in MATLAB. To further minimize power consumption, an ECG extraction technique (based on the 'So and Chan' algorithm [1]) was used in the model to extract some of the most significant points on the signal. Based on three simulation trails, ECG data was transferred to a scanning device with high accuracy (average of 99.62%). It was estimated that the virtual system is approximately 13 times more energy efficient compared to sending ECG stream data continuously when a connection is established.
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Tayyub, Jawad. "Hierarchical modelling and recognition of activities of daily living". Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/22186/.
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Activity recognition is becoming an increasingly important task in artificial intelligence. Successful activity recognition systems must be able to model and recognise activities ranging from simple short activities spanning a few seconds to complex longer activities spanning minutes or hours. We define activities as a set of qualitatively interesting interactions between people, objects and the environment. Accurate activity recognition is a desirable task in many scenarios such as surveillance, smart environments, robotic vision etc. In the domain of robotic vision specifically, there is now an increasing interest in autonomous robots that are able to operate without human intervention for long periods of time. The goal of this research is to build activity recognition approaches for such systems that are able to model and recognise simple short activities as well as complex longer activities arising from long-term autonomous operation of intelligent systems. The research makes the following key contributions: 1. We present a qualitative and quantitative representation to model simple activities as observed by autonomous systems. 2. We present a hierarchical framework to efficiently model complex activities that comprise of many sub-activities at varying levels of granularity. Simple activities are modelled using a discriminative model where a combined feature space, consisting of qualitative and quantitative spatio-temporal features, is generated in order to encode various aspects of the activity. Qualitative features are computed using qualitative spatio-temporal relations between human subjects and objects in order to abstractly represent the simple activity. Unlike current state-of-the-art approaches, our approach uses significantly fewer assumptions and does not require any knowledge about object types, their affordances, or the constituent activities of an activity. The optimal and most discriminating features are then extracted, using an entropy-based feature selection process, to best represent the training data. A novel approach for building models of complex long-term activities is presented as well. The proposed approach builds a hierarchical activity model from mark-up of activities acquired from multiple annotators in a video corpus. Multiple human annotators identify activities at different levels of conceptual granularity. Our method automatically infers a ‘part-of’ hierarchical activity model from this data using semantic similarity of textual annotations and temporal consistency. We then consolidate hierarchical structures learned from different training videos into a generalised hierarchical model represented as an extended grammar describing the over all activity. We then describe an inference mechanism to interpret new instances of activities. Simple short activity classes are first recognised using our previously learned generalised model. Given a test video, simple activities are detected as a stream of temporally complex low-level actions. We then use the learned extended grammar to infer the higher-level activities as a hierarchy over the low-level action input stream. We make use of three publicly available datasets to validate our two approaches of modelling simple to complex activities. These datasets have been annotated by multiple annotators through crowd-sourcing and in-house annotations. They consist of daily activity videos such as ‘cleaning microwave’, ‘having lunch in a restaurant’, ‘working in an office’ etc. The activities in these datasets have all been marked up at multiple levels of abstraction by multiple annotators, however no information on the ‘part-of’ relationship between activities is provided. The complexity of the videos and their annotations allows us to demonstrate the effectiveness of the proposed methods.
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Balasubramanian, Koushik. "Perception Framework for Activities of Daily Living Manipulation Tasks". Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/450.
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There is an increasing concern in tackling the problems faced by the elderly community and physically in-locked people to lead an independent life experience problems with self- care. The need for developing service robots that can help people with mobility impairments is hence very essential. Developing a control framework for shared human-robot autonomy will allow locked-in individuals to perform the Activities of Daily Living (ADL) in a exible way. The relevant ADL scenarios were identi ed as handling objects, self-feeding, and opening doors for indoor nav- igation assistance. Multiple experiments were conducted, which demonstrates that the robot executes these daily living tasks reliably without requiring adjustment to the environment. The indoor manipulation tasks hold the challenge of dealing with a wide range of unknown objects. This thesis presents a framework developed for grasping without requiring a priori knowledge of the objects being manipulated. A successful manipulation task requires the combination of aspects such as envi- ronment modeling, object detection with pose estimation, grasp planning, motion planning followed by an e?cient grasp execution, which is validated by a 6+2 Degree of Freedom robotic manipulator.
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Uitto, T. (Teemu). "Detection and recognition of daily activities by utilizing novel technologies". Master's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201711093088.
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This thesis studies novel outdoor and indoor positioning technologies enhanced with wearable or body worn motion sensors to recognize daily activities. In gerontology and geriatric medicine, it is fundamentally important to be able to detect changes in physical, psychosocial, and cognitive outcomes over time. In this thesis, intelligent systems for assessing aging changes were discussed. By utilizing novel technologies and methods, efficiency of home care services can be improved. The studied concept enables early detection of changes in functional ability and daily performance. As a research methods both interviews and literature studies were used. The technology study was conducted as a literature study, whereas the concept creation and selection of suitable technologies were based on interviews with experts belonging to 5GTN alliance. As a result, technologies for outdoor and indoor tracking were selected, and sensors for real time tracking of the daily activities and routines were proposed. Pilot project of indoor tracking of elderly people is starting in autumn 2017. Results of that are not covered in this thesis but those are covered in forthcoming thesesOpinnäytetyössä tutkittiin ulko- ja sisäpaikannusteknologioita, sekä yhdessä niiden kanssa käytettäviä kannettavia ja/tai puettavia liikesensoreita, joilla voidaan havaita ja tunnistaa päivittäisiä aktiviteettejä. Ikääntyvien ihmisten hoidossa on erinomaisen tärkeää pystyä havaitsemaan muutoksia fyysisessä, psykososiaalisessa sekä kognitiivisessa toimintakyvyssä. Tässä työssä käsiteltiin älykkäitä järjestelmiä ikääntymisestä aiheutuneiden muutosten arvioimiseksi. Uusia teknologioita ja menetelmiä hyödyntämällä voidaan parantaa kotihoitopalvelujen tehokkuutta. Työssä tutkittu konsepti mahdollistaa toimintakyvyn muutosten, sekä päivittäisen suorituskyvyn muutosten varhaisen tunnistamisen. Tutkimusmenetelminä käytettiin sekä haastatteluja että kirjallisuustutkimuksia. Teknologiatutkimus suoritettiin kirjallisuustutkimuksena eri lähteistä, kun taas konseptointi ja teknologioiden valinta suoritettiin haastattelemalla 5GTN allianssiin kuuluvia moniammatillisia asiantuntijoita. Tutkimuksen tuloksena valittiin käytettävät teknologiat ulko- ja sisäpaikannusmenetelmiin, sekä valittiin sensorityypit päivittäisten toimintojen ja rutiinien reaaliaikaiseen seurantaan. Syksyllä 2017 on käynnistymässä pilottiprojekti, jossa sisätila-antureiden avulla seurataan ikäihmisten toimintaa omassa kodissaan. Tuloksia tuosta projektista ei käsitellä tässä opinnäytetyössä, vaan ne käsitellään tulevissa opinnäytteissä
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Bouaziz, Ghazi. "Développement et mise en œuvre d'un système de détection de l'isolement social basé sur la reconnaissance des activités en matière de repas et de mobilité chez les personnes âgées à domicile". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES137.
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La reconnaissance d'activités de vie quotidienne (AVQs) fait l'objet, depuis des années, de recherches pour proposer des solutions performantes. Elle s'appuie sur l'analyse spatio-temporelle de situations, de comportements, etc. dont les données d'entrée sont les informations fournies par des capteurs ambiants ou par des capteurs portés par la personne. Cette thèse se focalise sur l'instrumentation du lieu de vie par des capteurs ambiants et sur la détection d'un état d'isolement social chez les personnes âgées. Deux approches sont utilisées pour évaluer l'isolement social. La première se base sur des questionnaires. La deuxième approche s'appuie sur l'utilisation de technologies pour la récupération objective de données représentatives d'un état ou d'un comportement. En particulier, l'activité " se nourrir " est liée à une séquence d'actions telles que faire les courses, cuisiner, manger et faire la vaisselle. L'activité " se déplacer " est liée à la mobilité au sein du domicile et au fait de sortir du logement. La littérature montre que ces deux activités semblent pertinentes pour évaluer un risque potentiel d'isolement social chez les aînés. Les travaux de thèse portent sur quatre contributions principales : Un état bibliographique des recherches sur la détection des AVQs afin d'en identifier les apports et les limites et tracer des voies de recherches pertinentes. Des critères spécifiques ont été choisis pour inclure les articles dans lesquels des systèmes de détection d'activités sont présentés. Une démarche de conception système appliquée à la reconnaissance d'AVQs. Cette démarche s'intègre dans un processus d'Ingénierie Système. Elle décrit l'analyse des exigences, leur modélisation au travers de diagrammes SysML et la mise en place d'une architecture matérielle et logicielle basée sur un réseau IoT. L'analyse des AVQs, dans notre étude, utilise les données de détecteurs de mouvement et de capteurs de contacts. Un affichage sur une application web permet de visualiser les résultats obtenus à destination de l'aide-soignant ou de la famille. L'utilisation originale de quatre méthodes de classification des AVQs à savoir "préparer le repas", "prendre le repas", "faire la vaisselle", "dormir/se relaxer", "hygiène", "la personne à l'extérieur du logement", "un visiteur à l'intérieur de la maison" et "autres activités". Les trois premières méthodes utilisées sont K-means, le modèle de mélange gaussien et BIRCH auxquelles on applique une pondération aux données. Les activités liées au repas n'ont ainsi pas le même poids que le reste des données, ce qui a permis d'améliorer la détection des AVQs. Le quatrième algorithme est basé sur une méthode logique à la suite de la détermination d'une matrice de corrélation prenant en entrée l'ensemble des capteurs disponibles. En utilisant les données de la matrice de corrélation, l'algorithme personnalise la détection des activités liées au repas en différenciant une personne qui prépare seule son repas d'une personne qui bénéficie d'un service de portage de repas. Nous validons nos méthodes en se référant aux formulaires remplis par les participants au début et à la fin de l'expérimentation dans lesquels ils indiquent le déroulement de leur journée-type. Ces algorithmes ont été appliqués sur une base de données annotée ouverte pour confirmer la précision de nos approches. La proposition d'un score du niveau d'isolement social chez la personne suivie. Ce score est établi sur la base de l'identification des activités pour extraire les habitudes quotidiennes au travers d'indicateurs du comportement (Le temps passé à l'extérieur de la maison et à l'intérieur de la cuisine, etc.). Six aînées ont été suivies pendant plus de 3 mois. L'algorithme régression logistique a été utilisé pour l'extraction du niveau d'isolement social qui a été comparé à celui identifié grâce au questionnaire " Lubben Social Network Scale " rempli avec chaque participant au début et à la fin de l'étudeThe recognition of daily life activities has been the subject of research for years to provide effective solutions. It is based on the spatio-temporal analysis of situations and behaviors whose input data is information provided by ambient sensors or by sensors worn by the person. This thesis focuses on the instrumentation of the living space by ambient sensors and on the detection of a state of social isolation in elderly people. Two approaches are used to assess social isolation. The first one is based on questionnaires. The second approach is based on the use of technologies for the objective acquisition of data representative of a state, behavior, etc. In particular, the activity "eating" is linked to a sequence of actions such as shopping, cooking, eating and washing dishes. The activity "moving" is linked to mobility within the home and leaving the home. The literature shows that these two activities seem to be relevant for assessing a potential risk of social isolation among older people. The thesis work focuses on four main contributions: A bibliographic review of ADLs detection research to identify its contributions and limitations, and to outline relevant research directions. Specific criteria were chosen to include articles presenting activity detection systems. A system design approach applied to the detection of ADLs. This approach is part of a system engineering process. It describes the analysis of requirements, their modeling through SysML diagrams and the implementation of a hardware and software architecture based on an IoT network. The analysis of ADLs, in our study, uses data from motion detectors and contact sensors. A display on a web application allows you to visualize the results obtained for the caregiver or the family. The original use of four methods to classify ADLs, namely "preparing the meal", "eating the meal", "washing the dishes", "sleeping/relaxing", "hygiene", "the person outside the home", "a visitor inside the home" and "other activities". The first three methods used are K-means, the Gaussian mixture model and BIRCH, which applies weights to the data. Meal-related activities therefore do not have the same weight as the rest of the data, which made it possible to improve the detection of ADLs. The fourth algorithm is based on a logical method following the determination of a correlation matrix using all the available sensors as input. Using the data from the correlation matrix, the algorithm personalizes the detection of meal-related activities by distinguishing a person preparing their meal from a person using a meal delivery service. We validate our methods by referring to the forms filled in by the participants at the beginning and end of the experiment, in which they describe the course of their typical day. These algorithms were applied to an open annotated database to confirm the accuracy of our approaches. The proposal of a score for the level of social isolation of the person being monitored. This score is based on the identification of activities to extract daily habits through behavioral indicators (time spent outside the house and in the kitchen, etc.). Six elderly people were followed for more than 3 months. The logistic regression algorithm was used to extract the level of social isolation, which was compared with the level of social isolation identified using the "Lubben Social Network Scale" questionnaire, which was completed by each participant at the beginning and end of the study
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Li, Yunjie. "Applying Data Mining Techniques on Continuous Sensed Data : For daily living activity recognition". Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23424.
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Nowadays, with the rapid development of the Internet of Things, the applicationfield of wearable sensors has been continuously expanded and extended, especiallyin the areas of remote electronic medical treatment, smart homes ect. Human dailyactivities recognition based on the sensing data is one of the challenges. With avariety of data mining techniques, the activities can be automatically recognized. Butdue to the diversity and the complexity of the sensor data, not every kind of datamining technique can performed very easily, until after a systematic analysis andimprovement. In this thesis, several data mining techniques were involved in theanalysis of a continuous sensing dataset in order to achieve the objective of humandaily activities recognition. This work studied several data mining techniques andfocuses on three of them; Decision Tree, Naive Bayes and neural network, analyzedand compared these techniques according to the classification results. The paper alsoproposed some improvements to the data mining techniques according to thespecific dataset. The comparison of the three classification results showed that eachclassifier has its own limitations and advantages. The proposed idea of combing theDecision Tree model with the neural network model significantly increased theclassification accuracy in this experiment.
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Pazhoumand-Dar, Hossein. "Unsupervised monitoring of an elderly person's activities of daily living using Kinect sensors and a power meter". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2017. https://ro.ecu.edu.au/theses/1971.
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The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored.
Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly.
To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel.
A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value.
Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns.
As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours.
The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs.
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Zaineb, Liouane. "Services e-santé basés sur la reconnaissance et la prédiction des activités quotidiennes dans les espaces intelligents". Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S109.
Texto completoResumen
Les progrès de la technologie des capteurs et leur disponibilité ont permis de mesurer diverses propriétés et activités des habitants dans une maison intelligente. Cependant, l’obtention de connaissances significatives à partir d’une grande quantité d’informations collectées à partir d’un réseau de capteurs n’est pas une tâche simple. En raison de la complexité du comportement des habitants, l’extraction d’informations significatives et la prédiction précise des valeurs représentant les activités futures d’un occupant sont des défis de recherche [6]. L’objectif principal de notre travail de thèse est d’assurer une analyse efficace des données recueillies à partir des capteurs d’occupation dans une maison intelligente. A ce propos, ce travail se base sur la reconnaissance et l’évaluation des activités quotidiennes d’une personne âgée dans le but d’observer, de prédire et de suivre l’évolution de son état de dépendance, de santé et de détecter par la même occasion, la présence d’une perte ou d’une perturbation de l’autonomie en temps réelAdvances in sensor technology and their availability have measured various properties and activities of residents in a smart home. However, obtaining significant knowledge from a large amount of information collected from a sensor network is not a simple task. Due to the complexity of the behavior of the inhabitants, the extraction of meaningful information and the accurate prediction of values representing the future activities of an occupant are research challenges. The main objective of our thesis work is to ensure an efficient analysis of data collected from occupancy sensors in a smart home. In this regard, this work is based on the recognition and evaluation of the daily activities of an elderly person in order to observe, predict and monitor the evolution of his state of dependence, health and to detect by the same occasion, the presence of a loss or a disruption of autonomy in real time
Capítulos de libros sobre el tema "Detection and recognition of activities of daily living"
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Cartas, Alejandro, Juan Marín, Petia Radeva y Mariella Dimiccoli. "Recognizing Activities of Daily Living from Egocentric Images". En Pattern Recognition and Image Analysis, 87–95. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58838-4_10.
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Alam, Md Jahangir, Yazid Attabi, Patrick Kenny, Pierre Dumouchel y Douglas O’Shaughnessy. "Automatic Emotion Recognition from Cochlear Implant-Like Spectrally Reduced Speech". En Ambient Assisted Living and Daily Activities, 332–40. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_48.
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Arias Tapia, Susana A., Sylvie Ratté, Héctor F. Gómez A., Alexandra González Eras, José Barbosa, Juan Carlos Torres, Ruth Reátegui Rojas et al. "First Contribution to Complex Emotion Recognition in Patients with Alzheimer’s Disease". En Ambient Assisted Living and Daily Activities, 341–47. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_49.
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Märker, Marcus, Sebastian Wolf, Oliver Scharf, Daniel Plorin y Tobias Teich. "KNX-Based Sensor Monitoring for User Activity Detection in AAL-environments". En Ambient Assisted Living and Daily Activities, 18–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_4.
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Synnott, Jonathan, Chris Nugent y Paul Jeffers. "A Thermal Data Simulation Tool for the Testing of Novel Approaches to Activity Recognition". En Ambient Assisted Living and Daily Activities, 10–13. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_2.
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Siddiqi, Muhammad Hameed, Rahman Ali, Byeong Ho Kang y Sungyoung Lee. "A New Feature Extraction Technique for Human Facial Expression Recognition Systems Using Depth Camera". En Ambient Assisted Living and Daily Activities, 131–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_21.
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Lozano-Monasor, Elena, María T. López, Antonio Fernández-Caballero y Francisco Vigo-Bustos. "Facial Expression Recognition from Webcam Based on Active Shape Models and Support Vector Machines". En Ambient Assisted Living and Daily Activities, 147–54. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_23.
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Gu, Feng, Francisco Flórez-Revuelta, Dorothy Monekosso y Paolo Remagnino. "A Multiple Kernel Learning Based Fusion Framework for Real-Time Multi-View Action Recognition". En Ambient Assisted Living and Daily Activities, 26–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_5.
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Avgerinakis, Konstantinos, Alexia Briassouli y Ioannis Kompatsiaris. "Activity Detection and Recognition of Daily Living Events". En Health Monitoring and Personalized Feedback using Multimedia Data, 139–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17963-6_8.
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Liouane, Zaineb, Tayeb Lemlouma, Philippe Roose, Fréderic Weis y Hassani Messaoud. "An Improved Elman Neural Network for Daily Living Activities Recognition". En Advances in Intelligent Systems and Computing, 697–707. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53480-0_69.
Texto completoActas de conferencias sobre el tema "Detection and recognition of activities of daily living"
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Negrete Ramírez, José Manuel y Yudith Cardinale. "Activities of Daily Living Detection on Healthcare: A Categorization". En iWOAR '22: 7th international Workshop on Sensor-based Activity Recognition and Artificial Intelligence. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3558884.3558887.
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Negin, Farhood, Abhishek Goel, Abdelrahman G. Abubakr, Francois Bremond y Gianpiero Francesca. "Online Detection of Long-Term Daily Living Activities by Weakly Supervised Recognition of Sub-Activities". En 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2018. http://dx.doi.org/10.1109/avss.2018.8639471.
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Pirsiavash, H. y D. Ramanan. "Detecting activities of daily living in first-person camera views". En 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6248010.
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Amano, Rina y Jianhua Ma. "Recognition and Change Point Detection of Dogs' Activities of Daily Living Using Wearable Devices". En 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2021. http://dx.doi.org/10.1109/dasc-picom-cbdcom-cyberscitech52372.2021.00116.
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Johnson, Brian Bradley. "Noninvasive Patient Monitoring with Ambient Sensors to Monitor Physical and Cognitive Health for Individuals Living with Alzheimer’s Disease". En 2024 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/dmd2024-1030.
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Abstract An estimated 6.2 million Americans aged 65 or older live with Alzheimer’s Disease and Alzheimer’s Disease Related Dementias in the United States, and 55 million globally. Fall detection, prediction, and prevention patient monitoring technology for this population has not been widely adopted as the standard of care because of privacy concerns with artificial intelligent video surveillance and problematic user experience design with wearables. The current standard of care for falls is eyewitness or self-report and therefore highly susceptible to human error. Therefore, solutions need to be scalable, affordable, and clinically effective with broad technology user acceptance. Despite many prevention and intervention methods that have been tried in past decades, falls remain the number one concern in aging care. The Centers for Medicare and Medicaid Services pay an enormous cost estimated at $50B per year on falls and fall related injuries. Human activity recognition with noninvasive ambient sensors is a versatile approach to patient monitoring as human movement can be translated into activities of daily living with sophisticated algorithms. This method does not use cameras and requires no contact with the body and thus it could be accepted at a higher rate than previous technologies and could therefore be adopted as the standard of care across the continuum.
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Avgerinakis, K., A. Briassouli y I. Kompatsiaris. "Recognition of Activities of Daily Living". En 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.181.
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Bergeron, Frederic, Sylvain Giroux, Kevin Bouchard y Sebastien Gaboury. "RFID based activities of daily living recognition". En 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017. http://dx.doi.org/10.1109/uic-atc.2017.8397548.
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Poularakis, Stergios, Konstantinos Avgerinakis, Alexia Briassouli y Ioannis Kompatsiaris. "Computationally efficient recognition of activities of daily living". En 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7350797.
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Bergeron, Frédéric, Kevin Bouchard, Sylvain Giroux, Sébastien Gaboury y Bruno Bouchard. "Real-Time Constraints for Activities of Daily Living Recognition". En PETRA '16: 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2910674.2935840.
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Liouane, Zaineb, Tayeb Lemlouma, Philippe Roose, Fréderic Weis y Hassani Messaoud. "A Markovian-based Approach for Daily Living Activities Recognition". En 5th International Conference on Sensor Networks. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005809502140219.
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