Human Activity Recognition Using an Ensemble Learning Algorithm with Smartphone Sensor Data

Human activity recognition (HAR) can monitor persons at risk of COVID-19 virus infection to manage their activity status. Currently, many people are isolated at home or quarantined in some specified places due to the spread of COVID-19 virus all over the world. This situation raises the requirement...

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Veröffentlicht in:Electronics (Basel) Jg. 11; H. 3; S. 322
Hauptverfasser: Tan, Tan-Hsu, Wu, Jie-Ying, Liu, Shing-Hong, Gochoo, Munkhjargal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.02.2022
Schlagworte:
Algorithms
Artificial neural networks
COVID-19
Ensemble learning
Human activity recognition
Machine learning
Mental health
Moving object recognition
Neural networks
Sensors
Smartphones
Viral diseases
Viruses
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Human activity recognition (HAR) can monitor persons at risk of COVID-19 virus infection to manage their activity status. Currently, many people are isolated at home or quarantined in some specified places due to the spread of COVID-19 virus all over the world. This situation raises the requirement of using the HAR to observe physical activity levels to assess physical and mental health. This study proposes an ensemble learning algorithm (ELA) to perform activity recognition using the signals recorded by smartphone sensors. The proposed ELA combines a gated recurrent unit (GRU), a convolutional neural network (CNN) stacked on the GRU and a deep neural network (DNN). The input samples of DNN were an extra feature vector consisting of 561 time-domain and frequency-domain parameters. The full connected DNN was used to fuse three models for the activity classification. The experimental results show that the precision, recall, F1-score and accuracy achieved by the ELA are 96.8%, 96.8%, 96.8%, and 96.7%, respectively, which are superior to the existing schemes.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11030322