Relevant bibliographies by topics / Activity Recognition (AR)

Academic literature on the topic 'Activity Recognition (AR)'

Author: Grafiati
Published: 10 December 2022
Last updated: 11 March 2023

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Journal articles on the topic "Activity Recognition (AR)"

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Roy, Debaditya, Sarunas Girdzijauskas, and Serghei Socolovschi. "Confidence-Calibrated Human Activity Recognition." Sensors 21, no. 19 (September 30, 2021): 6566. http://dx.doi.org/10.3390/s21196566.

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Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.
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Harkrider, Ashley W., and Steven Brad Smith. "Acceptable Noise Level, Phoneme Recognition in Noise, and Measures of Auditory Efferent Activity." Journal of the American Academy of Audiology 16, no. 08 (September 2005): 530–45. http://dx.doi.org/10.3766/jaaa.16.8.2.

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Acceptable noise level (ANL) is unrelated to sentence recognition in noise but may be related to phoneme recognition in noise (PRN). Individual differences in efferent activity in medial olivocochlear bundle (MOCB) and acoustic reflex (AR) pathways may account for intersubject variability in ANL and PRN. Monotic and dichotic ANL, monotic PRN, contralateral suppression of transient evoked otoacoustic emissions, and ipsilateral and contralateral acoustic reflex thresholds were measured in 31 adults with normal hearing. Results indicate that monotic ANL and PRN are unrelated. Monotic and dichotic ANL are related, suggesting that nonperipheral factors mediate ANL. Intersubject variability in ANL cannot be accounted for by individual differences in MOCB or AR activation. Intersubject variability in PRN cannot be accounted for by individual differences in MOCB or contralateral AR activation. It may be influenced by the ipsilateral AR pathway. Efferent activity in the contralateral AR arc is correlated with efferent activity in the MOCB.
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Baik, Kyong-Up, Sang-Hun Jung, and Byung-Zun Ahn. "Recognition of pharmacophore of ar-turmerone for its anticancer activity." Archives of Pharmacal Research 16, no. 3 (September 1993): 254–56. http://dx.doi.org/10.1007/bf02974492.

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Freedman, Richard, Hee-Tae Jung, and Shlomo Zilberstein. "Plan and Activity Recognition from a Topic Modeling Perspective." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 360–64. http://dx.doi.org/10.1609/icaps.v24i1.13683.

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We examine new ways to perform plan recognition (PR) using natural language processing (NLP) techniques. PR often focuses on the structural relationships between consecutive observations and ordered activities that comprise plans. However, NLP commonly treats text as a bag-of-words, omitting such structural relationships and using topic models to break down the distribution of concepts discussed in documents. In this paper, we examine an analogous treatment of plans as distributions of activities. We explore the application of Latent Dirichlet Allocation topic models to human skeletal data of plan execution traces obtained from a RGB-D sensor. This investigation focuses on representing the data as text and interpreting learned activities as a form of activity recognition (AR). Additionally, we explain how the system may perform PR. The initial empirical results suggest that such NLP methods can be useful in complex PR and AR tasks.
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Lupión, Marcos, Javier Medina-Quero, Juan F. Sanjuan, and Pilar M. Ortigosa. "DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería." Sensors 21, no. 2 (January 8, 2021): 405. http://dx.doi.org/10.3390/s21020405.

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Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
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Lupión, Marcos, Javier Medina-Quero, Juan F. Sanjuan, and Pilar M. Ortigosa. "DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería." Sensors 21, no. 2 (January 8, 2021): 405. http://dx.doi.org/10.3390/s21020405.

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Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
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Lauer, Luisa, Kristin Altmeyer, Sarah Malone, Michael Barz, Roland Brünken, Daniel Sonntag, and Markus Peschel. "Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children." Sensors 21, no. 19 (October 5, 2021): 6623. http://dx.doi.org/10.3390/s21196623.

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Augmenting reality via head-mounted displays (HMD-AR) is an emerging technology in education. The interactivity provided by HMD-AR devices is particularly promising for learning, but presents a challenge to human activity recognition, especially with children. Recent technological advances regarding speech and gesture recognition concerning Microsoft’s HoloLens 2 may address this prevailing issue. In a within-subjects study with 47 elementary school children (2nd to 6th grade), we examined the usability of the HoloLens 2 using a standardized tutorial on multimodal interaction in AR. The overall system usability was rated “good”. However, several behavioral metrics indicated that specific interaction modes differed in their efficiency. The results are of major importance for the development of learning applications in HMD-AR as they partially deviate from previous findings. In particular, the well-functioning recognition of children’s voice commands that we observed represents a novelty. Furthermore, we found different interaction preferences in HMD-AR among the children. We also found the use of HMD-AR to have a positive effect on children’s activity-related achievement emotions. Overall, our findings can serve as a basis for determining general requirements, possibilities, and limitations of the implementation of educational HMD-AR environments in elementary school classrooms.
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Sanchez-Comas, Andres, Kåre Synnes, and Josef Hallberg. "Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies." Sensors 20, no. 15 (July 29, 2020): 4227. http://dx.doi.org/10.3390/s20154227.

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Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL—smartphones, wearables, video, and electronic components—and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.
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Kang, James Jin, Tom Luan, and Henry Larkin. "Data Processing of Physiological Sensor Data and Alarm Determination Utilising Activity Recognition." International Journal of Information, Communication Technology and Applications 2, no. 1 (September 24, 2016): 108–31. http://dx.doi.org/10.17972/ijicta20162132.

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Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests and examples of using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app.
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Al-Taei, Ali. "A Smartphone -Based Model for Human Activity Recognition." Ibn AL- Haitham Journal For Pure and Applied Science 30, no. 3 (December 29, 2017): 243. http://dx.doi.org/10.30526/30.3.1628.

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Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classifier overperforms other classifiers that were examined in this research paper.
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Dissertations / Theses on the topic "Activity Recognition (AR)"

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PATARA, FULVIO. "Multi-level meta-modeling architectures applied to eHealth." Doctoral thesis, 2016. http://hdl.handle.net/2158/1041924.

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Over the last decade, a growing digital universe of unstructured or semi- structured human-sourced information, structured process-mediated data, and well-structured machine-generated data, encourages the adoption of innovative forms of data modeling and information processing to enable enhanced insight, decision making, and process automation applied to a variety of different contexts. Healthcare comprises a notable domain of interest, where the availability of a large amount of information can be exploited to take relevant and tangible benefits in terms of efficiency of the care process, improved out- comes and reduced health system costs. However, due to the complex nature of clinical data, a number of challenges needs to be faced, mainly related on how data characterized by volume, variety, variability, velocity, and veracity can be effectively and efficiently modeled, and how these data can be exploited for increasing the domain knowledge and supporting decision-making processes. The aim of this dissertation is to describe the crucial role played by soft- ware architectures in order to overcome challenges posed by the healthcare context. Specifically, this dissertation addresses the development and applicability of multi-level meta-modeling architectures to various scenarios of eHealth, where flexibility and changeability represent primary requirements. Meta-modeling principles are concretely exploited in the implementation of an adaptable patient-centric Electronic Health Record (EHR) system to face a number of challenging requirements, including: adaptability to different specialities and organizational contexts; run-time configurability by domain experts; interoperability of heterogeneous data produced by various sources and accessed by various actors; applicability of guideline recommendations for evaluating clinical practice compliance; applicability of Activity Recognition techniques for monitoring and classifying human activities in pervasive intelligent environments.
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Book chapters on the topic "Activity Recognition (AR)"

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Kim, Hyunju, and Dongman. "AR-T: Temporal Relation Embedded Transformer for the Real World Activity Recognition." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 617–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94822-1_40.

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Gjoreski, Hristijan, Ivana Kiprijanovska, Simon Stankoski, Stefan Kalabakov, John Broulidakis, Charles Nduka, and Martin Gjoreski. "Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning." In Smart Innovation, Systems and Technologies, 153–67. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8944-7_10.

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Razzaq, Muhammad Asif, and Sungyoung Lee. "MMOU-AR: Multimodal Obtrusive and Unobtrusive Activity Recognition Through Supervised Ontology-Based Reasoning." In Advances in Intelligent Systems and Computing, 963–74. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19063-7_75.

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Conference papers on the topic "Activity Recognition (AR)"

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Zhenyu He. "Activity recognition from accelerometer signals based on Wavelet-AR model." In 2010 International Conference on Progress in Informatics and Computing (PIC). IEEE, 2010. http://dx.doi.org/10.1109/pic.2010.5687572.

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Zhen-Yu He and Lian-Wen Jin. "Activity recognition from acceleration data using AR model representation and SVM." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620779.

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Ajgou, Riadh, Salim Sbaa, Said Ghendir, Ali Chamsa, and A. Taleb-Ahmed. "Robust remote speaker recognition system based on AR-MFCC features and efficient speech activity detection algorithm." In 2014 11th International Symposium on Wireless Communications Systems (ISWCS). IEEE, 2014. http://dx.doi.org/10.1109/iswcs.2014.6933448.

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Abreu, Eduardo, Gerson Cavalheiro, Ana Pernas, Adenauer Yamin, João Lopes, and Cláudio Geyer. "Uma Abordagem não Intrusiva para Reconhecimento de Atividades em Casas Inteligentes Explorando Processamento Semântico*." In X Simpósio Brasileiro de Computação Ubíqua e Pervasiva. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcup.2018.3294.

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O objetivo deste trabalho, denominado EXEHDA-AR (Execution Environment for Highly Distributed Applications-Activity Recognition), é prover recursos arquiteturais que permitam o reconhecimento de atividades no middleware EXEHDA, explorando uma abordagem baseada em processamento semântico. Para tanto foram concebidos componentes arquiteturais, os quais foram integrados ao Subsistema de Reconhecimento de Contexto e Adaptação do EXEHDA. Um estudo de caso sobre casas inteligentes foi desenvolvido para avaliar as funcionalidades propostas para o EXEHDA-AR, sendo obtida uma acurácia média de 94,36% no reconhecimento de atividades. Estes resultados apontam que métodos baseados em processamento semântico constituem uma alternativa viável, com baixo nível de intrusão.
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