Výsledky vyhledávání - Activity Recognition in Pervasive Computing Environments

CSI-Based Location Independent Human Activity Recognition Using Deep Learning

Autoři: Fahd Abuhoureyah Yan Chiew Wong Ahmad Sadhiqin Bin Mohd Isira a další

Zdroj: Human-Centric Intelligent Systems, Vol 3, Iss 4, Pp 537-557 (2023)

Témata: Artificial intelligence, Location-independent sensing, Ambient Intelligence, Computer Networks and Communications, Flexibility (engineering), Non-contact Physiological Monitoring Technology, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, Information technology, 02 engineering and technology, Noise (video), FOS: Medical engineering, Activity Recognition, Pattern recognition (psychology), 7. Clean energy, 12. Responsible consumption, Context-Aware Applications, Nexmon CSI based, Engineering, Deep Learning, Segmentation, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Image (mathematics), FOS: Mathematics, Internet of Things and Edge Computing, Statistics, Deep learning, QA75.5-76.95, 15. Life on land, T58.5-58.64, Computer science, Process (computing), Operating system, Electronic computers. Computer science, Activity recognition, Computer Science, Physical Sciences, 8. Economic growth, Human activity recognition, Computer Vision and Pattern Recognition, Mathematics

Přístupová URL adresa: https://doaj.org/article/18168cc0f50943f19b2f28342cde3857

Enhanced Recognition of Human Activity using Hybrid Deep Learning Techniques

Autoři: S. Abinaya T. Rajasenbagam K. Indira a další

Zdroj: International Journal of Electrical and Electronics Research. 12:36-40

Témata: Artificial intelligence, Activity Recognition in Pervasive Computing Environments, Deep learning, 02 engineering and technology, Human Activity Analysis, 15. Life on land, Activity Recognition, Pattern recognition (psychology), Computer science, 12. Responsible consumption, Anomaly Detection in High-Dimensional Data, Context-Aware Applications, Deep Learning, Artificial Intelligence, Action Recognition, 13. Climate action, Human Action Recognition and Pose Estimation, Computer Science, Physical Sciences, 11. Sustainability, 8. Economic growth, 0202 electrical engineering, electronic engineering, information engineering, Computer Vision and Pattern Recognition, 14. Life underwater

Design of Optimal Deep Learning Based Human Activity Recognition on Sensor Enabled Internet of Things Environment

Autoři: Fahd N. Al‐Wesabi Amani Abdulrahman Albraikan Anwer Mustafa Hilal a další

Zdroj: IEEE Access, Vol 9, Pp 143988-143996 (2021)

Témata: human activity recognition, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Internet of Things, Activity Recognition in Pervasive Computing Environments, Metaheuristics, 02 engineering and technology, Activity Recognition, Deep Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Embedded system, Preprocessor, Hyperparameter, metaheuristics, Geography, Wearables, Internet of Things and Edge Computing, feature extraction, Feature extraction, Wearable computer, Deep learning, Human Activity Analysis, Computer science, TK1-9971, wearables, Action Recognition, Human Action Recognition and Pose Estimation, Activity recognition, Computer Science, Physical Sciences, Human activity recognition, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition, Benchmark (surveying), Geodesy

Popis souboru: application/pdf

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09539181.pdf
https://doaj.org/article/ddb6d5a4b38d46b0a9832c7a4cab5e4a
https://doaj.org/article/ddb6d5a4b38d46b0a9832c7a4cab5e4a
https://doi.org/10.1109/ACCESS.2021.3112973
https://ieeexplore.ieee.org/abstract/document/9539181
https://dblp.uni-trier.de/db/journals/access/access9.html#Al-WesabiAHAADR21
https://hdl.handle.net/10576/57734

Intelligent system for human activity recognition in IoT environment

Autoři: Hussein Khaled Osama Abu-Elnasr Samir Elmougy a další

Zdroj: Complex Intell Systems
Complex & Intelligent Systems, Vol 9, Iss 4, Pp 3535-3546 (2021)

Témata: Artificial neural network, Composite material, IoT, Artificial intelligence, Ambient Intelligence, IoT Security, Computer Networks and Communications, Internet of Things, Health Professions, Activity Recognition in Pervasive Computing Environments, Information technology, 02 engineering and technology, Activity Recognition, Pattern recognition (psychology), Context-Aware Applications, Layer (electronics), Convolution (computer science), Health Information Management, F1 score, Health Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Edge Computing, Embedded system, Data mining, Decision support system, Intelligent system, Computational intelligence, Precision and recall, Machine Learning in Healthcare and Medicine, Internet of Things and Edge Computing, Measure (data warehouse), Linguistics, QA75.5-76.95, T58.5-58.64, Computer science, Materials science, FOS: Philosophy, ethics and religion, Philosophy, Electronic computers. Computer science, Computer Science, Physical Sciences, FOS: Languages and literature, Recall, Original Article, Human activity recognition, Computer Vision and Pattern Recognition, Capsule neural network

Přístupová URL adresa: https://link.springer.com/content/pdf/10.1007/s40747-021-00508-5.pdf
https://pubmed.ncbi.nlm.nih.gov/34777979
https://doaj.org/article/eeda89c543d746fd81ec40782f7e1cfe
https://link.springer.com/article/10.1007/s40747-021-00508-5
https://paperity.org/p/270345051/intelligent-system-for-human-activity-recognition-in-iot-environment
https://link.springer.com/content/pdf/10.1007/s40747-021-00508-5.pdf

A perspective on human activity recognition from inertial motion data

Autoři: Walid Gomaa Mohamed A. Khamis

Zdroj: Neural Computing and Applications. 35:20463-20568

Témata: Artificial intelligence, Ambient Intelligence, Scale (ratio), Non-contact Physiological Monitoring Technology, Biomedical Engineering, Perspective (graphical), Activity Recognition in Pervasive Computing Environments, 02 engineering and technology, FOS: Medical engineering, Activity Recognition, 01 natural sciences, Quantum mechanics, 7. Clean energy, Data science, Anomaly Detection in High-Dimensional Data, Context-Aware Applications, Big data, Engineering, Artificial Intelligence, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Software deployment, Embedded system, Data mining, Wearable Sensors, Software engineering, Human–computer interaction, Geography, 9. Industry and infrastructure, Motion (physics), Physics, Wearable technology, Wearable computer, Computer science, 0104 chemical sciences, Inertial measurement unit, Activity recognition, Implementation, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, Benchmark (surveying), Smartwatch, Motion capture, Geodesy

Recognition of human activity using GRU deep learning algorithm

Autoři: Saeed Mohsen

Zdroj: Multimedia Tools and Applications. 82:47733-47749

Témata: Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Activity Recognition in Pervasive Computing Environments, Receiver operating characteristic, 02 engineering and technology, Activity Recognition, 7. Clean energy, Psychoanalysis, Deep Learning, Engineering, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Confusion, Wireless Sensor Networks: Survey and Applications, Internet of Things and Edge Computing, Electronic engineering, Deep learning, 15. Life on land, Computer science, Sensitivity (control systems), 3. Good health, Algorithm, FOS: Psychology, Confusion matrix, Activity recognition, Computer Science, Physical Sciences, Fall Detection, Computer Vision and Pattern Recognition

Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

Autoři: Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran a další

Zdroj: AIMS Mathematics, Vol 8, Iss 5, Pp 12520-12539 (2023)

Témata: human activity recognition, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Activity Recognition in Pervasive Computing Environments, Activity Recognition, 7. Clean energy, Machine learning, QA1-939, dimensionality reduction, Geography, Internet of Things and Edge Computing, deep learning, healthcare, parameter tuning, Deep learning, Human Activity Analysis, Computer science, 3. Good health, Action Recognition, Human Action Recognition and Pose Estimation, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, Benchmark (surveying), Mathematics, Geodesy

Přístupová URL adresa: https://doaj.org/article/d93c1c8f9965427d95dc44726deeef21

An Efficient Human Activity Recognition Using Hybrid Features and Transformer Model

Autoři: Oumaima Saidani Majed Alsafyani Roobaea Alroobaea a další

Zdroj: IEEE Access, Vol 11, Pp 101373-101386 (2023)

Témata: PAMP2, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Activity Recognition in Pervasive Computing Environments, Activity Recognition, Pattern recognition (psychology), 7. Clean energy, Context-Aware Applications, Engineering, Machine learning, hybrid features, 10. No inequality, Transformer, Internet of Things and Edge Computing, Discriminative model, Voltage, Human Activity Analysis, 15. Life on land, Computer science, TK1-9971, 3. Good health, HAR, Action Recognition, Human Action Recognition and Pose Estimation, Activity recognition, Electrical engineering, Computer Science, Physical Sciences, transformer, Feature extraction, Human activity recognition, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition

Přístupová URL adresa: https://doaj.org/article/ae4e9287c3bf461eb99113f3145e6a10

An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

Autoři: Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul

Zdroj: Intelligent Automation & Soft Computing. 35:1245-1259

Témata: Artificial intelligence, Ambient Intelligence, Physiology, Smoking Cessation and Health Effects of Tobacco, Activity Recognition in Pervasive Computing Environments, Convolutional neural network, 02 engineering and technology, Activity Recognition, 7. Clean energy, Deep Learning, Health Sciences, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Embedded system, 2. Zero hunger, Public Health, Environmental and Occupational Health, Deep learning, Wearable computer, Computer science, Role of Mediterranean Diet in Health Outcomes, 3. Good health, FOS: Biological sciences, Activity recognition, Computer Science, Physical Sciences, Medicine, Computer Vision and Pattern Recognition, Smartwatch

Computer Vision with Optimal Deep Stacked Autoencoder-based Fall Activity Recognition for Disabled Persons in the IoT Environment

Autoři: Eatedal Alabdulkreem Radwa Marzouk Mesfer Alduhayyem a další

Zdroj: Journal of Disability Research. 2

Témata: Artificial neural network, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Feature (linguistics), Non-contact Physiological Monitoring Technology, Internet of Things, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, FOS: Medical engineering, Activity Recognition, Engineering, Computer security, Field (mathematics), Machine learning, FOS: Mathematics, Geography, Internet of Things and Edge Computing, Pure mathematics, Deep learning, Linguistics, Human Activity Analysis, Autoencoder, Computer science, Process (computing), FOS: Philosophy, ethics and religion, 3. Good health, Philosophy, Operating system, Action Recognition, Activity recognition, Computer Science, Physical Sciences, FOS: Languages and literature, Fall Detection, Feature extraction, Computer Vision and Pattern Recognition, Benchmark (surveying), Mathematics, Geodesy

Outdoor activity classification using smartphone based inertial sensor measurements

Autoři: Rushikesh Bodhe Saaveethya Sivakumar Gopal Sakarkar a další

Zdroj: Multimedia Tools and Applications. 83:76963-76989

Témata: Artificial intelligence, Activity Recognition in Pervasive Computing Environments, 02 engineering and technology, Activity Recognition, 01 natural sciences, Motion Detection, 7. Clean energy, Real-time computing, Wireless Indoor Localization Techniques and Systems, Visual Object Tracking and Person Re-identification, Engineering, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Wearable Sensors, Human–computer interaction, Computer science, 0104 chemical sciences, Accelerometer, Operating system, Inertial measurement unit, Multimedia, Smartphone application, Computer Science, Physical Sciences, Indoor Localization, Indoor Tracking, Computer Vision and Pattern Recognition

Industry 4.0-Oriented Deep Learning Models for Human Activity Recognition

Autoři: Saeed Mohsen Ahmed Elkaseer Steffen Scholz

Zdroj: IEEE Access, Vol 9, Pp 150508-150521 (2021)
IEEE Access, 9, 150508-150521

Témata: ddc:004, human activity recognition, Artificial neural network, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Non-contact Physiological Monitoring Technology, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, Convolutional neural network, 02 engineering and technology, FOS: Medical engineering, Activity Recognition, Pattern recognition (psychology), Context-Aware Applications, Engineering, Deep Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, convolutional neural network (CNN), Internet of Things and Edge Computing, long short-term memory (LSTM), Proposal ID: 2021-026-030289, DATA processing & computer science, Deep learning, Linguistics, Industry 4.0, Computer science, TK1-9971, FOS: Philosophy, ethics and religion, Philosophy, 3DP, Activity recognition, Computer Science, Physical Sciences, FOS: Languages and literature, Recall, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition

Popis souboru: application/pdf

Přístupová URL adresa: https://doaj.org/article/bb6dc818c9354e2c892c56a200131b9b
https://dblp.uni-trier.de/db/journals/access/access9.html#MohsenES21
https://ieeexplore.ieee.org/document/9605677/
https://publikationen.bibliothek.kit.edu/1000140540
https://doi.org/10.5445/IR/1000140540
https://publikationen.bibliothek.kit.edu/1000140540/134900268

Wireless body area sensor networks based human activity recognition using deep learning

Autoři: Ehab El-Adawi Ehab Essa Mohamed Handosa a další

Zdroj: Sci Rep
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)

Témata: Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Science, Non-contact Physiological Monitoring Technology, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, Convolutional neural network, 02 engineering and technology, FOS: Medical engineering, Activity Recognition, Pattern recognition (psychology), 7. Clean energy, Article, Real-time computing, Context-Aware Applications, Wearable Electronic Devices, Engineering, Deep Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Humans, Human Activities, Embedded system, Wearable Sensors, Computer network, Internet of Things and Edge Computing, 9. Industry and infrastructure, Wearable computer, Deep learning, Body area network, Computer science, Process (computing), 3. Good health, Operating system, Activity recognition, Computer Science, Physical Sciences, Wireless, Telecommunications, Medicine, Neural Networks, Computer, Computer Vision and Pattern Recognition, Algorithms, Wireless sensor network

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38302545
https://doaj.org/article/eb04861a8d6349e18283c441fa495629

New incremental SVM algorithms for human activity recognition in smart homes

Autoři: Yala Nawal Mourad Oussalah Belkacem Fergani a další

Přispěvatelé: Yala Nawal Mourad Oussalah Belkacem Fergani a další

Zdroj: Journal of Ambient Intelligence and Humanized Computing. 14:13433-13450

Témata: Incremental, Wireless Home Automation Systems, Artificial intelligence, Support vector machine, Ambient Intelligence, Computer Networks and Communications, SVM, Activity Recognition in Pervasive Computing Environments, 02 engineering and technology, Incremental SVM, Activity Recognition, Pattern recognition (psychology), Engineering, Cluster analysis, Smart home, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Smart Homes, Electrical and Electronic Engineering, Smart home Activity recognition Incremental learning Incremental SVM, Data mining, Incremental learning, Computational intelligence, Internet of Things and Edge Computing, Sensors, [INFO.INFO-IU] Computer Science [cs]/Ubiquitous Computing, Computer science, Activity recognition, Computer Science, Physical Sciences, Feature extraction, Computer Vision and Pattern Recognition, Classifier (UML)

Popis souboru: application/pdf

Přístupová URL adresa: http://urn.fi/urn:nbn:fi-fe20231013140040
https://hal.science/hal-04251578v1/document
https://hal.science/hal-04251578v1
https://doi.org/10.1007/s12652-022-03798-w

RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition

Autoři: Sakorn Mekruksavanich Anuchit Jitpattanakul

Zdroj: Mathematical Biosciences and Engineering, Vol 19, Iss 6, Pp 5671-5698 (2022)

Témata: human activity recognition, Artificial neural network, Artificial intelligence, Ambient Intelligence, complex human activity, smartwatch sensors, Computer Networks and Communications, Recurrent neural network, Social Sciences, Activity Recognition in Pervasive Computing Environments, Transportation, Heuristic, 02 engineering and technology, Activity Recognition, Pattern recognition (psychology), bidirectional gated recurrent unit, Deep Learning, Machine learning, QA1-939, 0202 electrical engineering, electronic engineering, information engineering, Humans, Human Activities, Exercise, Internet of Things and Edge Computing, deep learning, Deep learning, Computer science, Accelerometer, Operating system, Activity recognition, Computer Science, Physical Sciences, Neural Networks, Computer, Computer Vision and Pattern Recognition, TP248.13-248.65, Mathematics, Algorithms, Biotechnology, Understanding Human Mobility Patterns

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/35603373
https://doaj.org/article/b1ad238d7a944dc6b27dc6e8afaead9d

Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

Autoři: Syed Aziz Shah Ahsen Tahir Julien Le Kernec a další

Zdroj: Neural Computing and Applications. 34:7933-7953

Témata: Artificial neural network, Artificial intelligence, Support vector machine, Ambient Intelligence, Non-contact Physiological Monitoring Technology, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, 02 engineering and technology, FOS: Medical engineering, Activity Recognition, Pattern recognition (psychology), Context-Aware Applications, Engineering, Context (archaeology), Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Psychiatry, Doppler Radar, Radar, Naive Bayes classifier, Geography, Activities of daily living, Autoencoder, Computer science, Gait Recognition for Human Identification, Archaeology, Physical Sciences, Computer Science, Telecommunications, Fall Detection, Medicine, Computer Vision and Pattern Recognition

Popis souboru: application/pdf

Přístupová URL adresa: https://link.springer.com/content/pdf/10.1007/s00521-022-06886-2.pdf
https://eprints.gla.ac.uk/262697/1/262697.pdf

Activities Recognition, Anomaly Detection and Next Activity Prediction Based on Neural Networks in Smart Homes

Autoři: Khaled A. Alaghbari Mohamad Hanif Md Saad Aini Hussain a další

Zdroj: IEEE Access, Vol 10, Pp 28219-28232 (2022)

Témata: Artificial neural network, Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Recurrent neural network, Social Sciences, Activity Recognition in Pervasive Computing Environments, Anomaly detection, 02 engineering and technology, Activity Recognition, Pattern recognition (psychology), Deep Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Technology Adoption and Aging Population, activity recognition, Smart Homes, 10. No inequality, Demography, autoencoder, Internet of Things and Edge Computing, Smart homes, Physics, deep neural network, Deep learning, Autoencoder, Condensed matter physics, Computer science, anomaly detection, sequence prediction, TK1-9971, FOS: Sociology, Long short term memory, Anomaly (physics), 3. Good health, Activity recognition, Computer Science, Physical Sciences, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition

Přístupová URL adresa: https://doaj.org/article/8fcc7ebd87ef443ca55cca577ffdb8eb

ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors

Autoři: Sakorn Mekruksavanich Anuchit Jitpattanakul Kanokwan Sitthithakerngkiet a další

Zdroj: IEEE Access, Vol 10, Pp 51142-51154 (2022)

Témata: Artificial intelligence, Ambient Intelligence, Computer Networks and Communications, Non-contact Physiological Monitoring Technology, Biomedical Engineering, Activity Recognition in Pervasive Computing Environments, Convolutional neural network, 02 engineering and technology, FOS: Medical engineering, Activity Recognition, 7. Clean energy, Real-time computing, Context-Aware Applications, deep residual network, Engineering, Deep Learning, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Embedded system, Wearable Sensors, Human–computer interaction, Internet of Things and Edge Computing, 9. Industry and infrastructure, Wearable technology, deep learning, Wrist-worn wearable sensor, Wearable computer, Deep learning, complex activity recognition, Computer science, TK1-9971, Algorithm, Activity recognition, Residual, Computer Science, Physical Sciences, Electrical engineering. Electronics. Nuclear engineering, Computer Vision and Pattern Recognition, attention mechanism, Smartwatch

Přístupová URL adresa: https://doaj.org/article/ffdd7542ce9d4f1d90bb4fba241bdcc4

Real Time Human Activity Recognition from Accelerometer Data using Convolutional Neural Networks

Autoři: Md. Ashikur Rahman Yousuf Mia Mizanur Rahman Masum a další

Zdroj: 2022 7th International Conference on Communication and Electronics Systems (ICCES). :1394-1397

Témata: Artificial intelligence, Time series, Ambient Intelligence, Feature (linguistics), Activity Recognition in Pervasive Computing Environments, Convolutional neural network, Mobile device, Activity Recognition, Pattern recognition (psychology), 7. Clean energy, Context-Aware Applications, Deep Learning, Machine learning, Data mining, Accelerometer Data, Deep learning, Linguistics, Computer science, FOS: Philosophy, ethics and religion, Accelerometer, Philosophy, Operating system, Categorization, Activity recognition, Computer Science, Physical Sciences, FOS: Languages and literature, Feature extraction, Computer Vision and Pattern Recognition

Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor

Autoři: Mst. Alema Khatun Mohammad Abu Yousuf Sabbir Ahmed a další

Přispěvatelé: Mst. Alema Khatun Mohammad Abu Yousuf Sabbir Ahmed a další

Zdroj: IEEE J Transl Eng Health Med
IEEE Journal of Translational Engineering in Health and Medicine, Vol 10, Pp 1-16 (2022)
IEEE Journal of Translational Engineering in Health and Medicine

Témata: Artificial intelligence, Economics, Non-contact Physiological Monitoring Technology, Gyroscopes, 02 engineering and technology, Context-Aware Applications, Engineering, Android (operating system), 0202 electrical engineering, electronic engineering, information engineering, Attention, Human Activities, Wearable technology, Wearable computer, Human Activity Analysis, Gyroscope, smartphones, Inertial measurement unit, Aerospace engineering, mHealth, Human Action Recognition and Pose Estimation, Physical Sciences, Computer Vision and Pattern Recognition, LSTM, Algorithms, accelerometers, Computer applications to medicine. Medical informatics, R858-859.7, Biomedical Engineering, gyroscopes, Activity Recognition in Pervasive Computing Environments, Convolutional neural network, FOS: Medical engineering, Activity Recognition, Article, Wearable Electronic Devices, Machine learning, Medical technology, Humans, R855-855.5, Embedded system, Economic growth, Wearable Sensors, Sensors, Health care, Deep learning, Computer science, attention, Accelerometer, Smartphones, Operating system, Action Recognition, Activity recognition, Computer Science, Neural Networks, Computer, Accelerometers

Popis souboru: application/pdf

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/35795873
https://doaj.org/article/4c5307cb245b44148e8482b0a61ac86e
https://hdl.handle.net/11250/3027620