Trends in human activity recognition with focus on machine learning and power requirements
The advancement and availability of technology can be employed to improve our daily lives. One example is Human Activity Recognition (HAR). HAR research has been mainly explored using imagery but is currently evolving to the use of sensors and has the ability to have a positive impact, including ind...
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doaj-03f2968bc571420cb46ec717be6e09a82021-08-20T04:37:11ZengElsevierMachine Learning with Applications2666-82702021-09-015100072Trends in human activity recognition with focus on machine learning and power requirementsBinh Nguyen0Yves Coelho1Teodiano Bastos2Sridhar Krishnan3Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada; Corresponding author.Department of Electrical Engineering, Federal University of Espírito Santo (UFES), Av. Fernando Ferrari, 514, 29075-910, Vitória, BrazilDepartment of Electrical Engineering, Federal University of Espírito Santo (UFES), Av. Fernando Ferrari, 514, 29075-910, Vitória, BrazilDepartment of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaThe advancement and availability of technology can be employed to improve our daily lives. One example is Human Activity Recognition (HAR). HAR research has been mainly explored using imagery but is currently evolving to the use of sensors and has the ability to have a positive impact, including individual health monitoring and removing the barrier of healthcare. To reach a marketable HAR device, state-of-the-art classifications and power consumption methods such as convolutional neural network (CNN), data compression and other emerging techniques are reviewed here. The review of the current literature creates a foundation in HAR and addresses the lack of available HAR datasets, recommendation of classification and power reduction techniques, current drawbacks and their respective solutions, as well as future trends in HAR. The lack of publicly available datasets makes it difficult for new users to explore the field of HAR. This paper dedicates a section to publicly available datasets for users to access. Finally, a framework is suggested for HAR applications, which envelopes the current literature and emerging trends in HAR.http://www.sciencedirect.com/science/article/pii/S2666827021000360HARMachine learningPower consumptionWearable devicesTelemedicine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Binh Nguyen Yves Coelho Teodiano Bastos Sridhar Krishnan |
spellingShingle |
Binh Nguyen Yves Coelho Teodiano Bastos Sridhar Krishnan Trends in human activity recognition with focus on machine learning and power requirements Machine Learning with Applications HAR Machine learning Power consumption Wearable devices Telemedicine |
author_facet |
Binh Nguyen Yves Coelho Teodiano Bastos Sridhar Krishnan |
author_sort |
Binh Nguyen |
title |
Trends in human activity recognition with focus on machine learning and power requirements |
title_short |
Trends in human activity recognition with focus on machine learning and power requirements |
title_full |
Trends in human activity recognition with focus on machine learning and power requirements |
title_fullStr |
Trends in human activity recognition with focus on machine learning and power requirements |
title_full_unstemmed |
Trends in human activity recognition with focus on machine learning and power requirements |
title_sort |
trends in human activity recognition with focus on machine learning and power requirements |
publisher |
Elsevier |
series |
Machine Learning with Applications |
issn |
2666-8270 |
publishDate |
2021-09-01 |
description |
The advancement and availability of technology can be employed to improve our daily lives. One example is Human Activity Recognition (HAR). HAR research has been mainly explored using imagery but is currently evolving to the use of sensors and has the ability to have a positive impact, including individual health monitoring and removing the barrier of healthcare. To reach a marketable HAR device, state-of-the-art classifications and power consumption methods such as convolutional neural network (CNN), data compression and other emerging techniques are reviewed here. The review of the current literature creates a foundation in HAR and addresses the lack of available HAR datasets, recommendation of classification and power reduction techniques, current drawbacks and their respective solutions, as well as future trends in HAR. The lack of publicly available datasets makes it difficult for new users to explore the field of HAR. This paper dedicates a section to publicly available datasets for users to access. Finally, a framework is suggested for HAR applications, which envelopes the current literature and emerging trends in HAR. |
topic |
HAR Machine learning Power consumption Wearable devices Telemedicine |
url |
http://www.sciencedirect.com/science/article/pii/S2666827021000360 |
work_keys_str_mv |
AT binhnguyen trendsinhumanactivityrecognitionwithfocusonmachinelearningandpowerrequirements AT yvescoelho trendsinhumanactivityrecognitionwithfocusonmachinelearningandpowerrequirements AT teodianobastos trendsinhumanactivityrecognitionwithfocusonmachinelearningandpowerrequirements AT sridharkrishnan trendsinhumanactivityrecognitionwithfocusonmachinelearningandpowerrequirements |
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