Research Article
Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm
@ARTICLE{10.4108/eetinis.v9i4.2571, author={To-Hieu Dao and Hai-Yen Hoang and Van-Nhat Hoang and Duc-Tan Tran and Duc-Nghia Tran}, title={Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={9}, number={4}, publisher={EAI}, journal_a={INIS}, year={2022}, month={12}, keywords={Classification, recognition, activity, real-time, wearable, microcontroller, moderate performance}, doi={10.4108/eetinis.v9i4.2571} }- To-Hieu Dao
Hai-Yen Hoang
Van-Nhat Hoang
Duc-Tan Tran
Duc-Nghia Tran
Year: 2022
Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm
INIS
EAI
DOI: 10.4108/eetinis.v9i4.2571
Abstract
There has been increasing interest in the application of artificial intelligence technologies to improve the quality of support services in healthcare. Some constraints, such as space, infrastructure, and environmental conditions, present challenges with assistive devices for humans. This paper proposed a wearable-based real-time human activity recognition system to monitor daily activities. The classification was done directly on the device, and the results could be checked over the internet. The accelerometer data collection application was developed on the device with a sampling frequency of 20Hz, and the random forest algorithm was embedded in the hardware. To improve the accuracy of the recognition system, a feature vector of 31 dimensions was calculated and used as an input per time window. Besides, the dynamic window method applied by the proposed model allowed us to change the data sampling time (1-3 seconds) and increase the performance of activity classification. The experiment results showed that the proposed system could classify 13 activities with a high accuracy of 99.4%. The rate of correctly classified activities was 96.1%. This work is promising for healthcare because of the convenience and simplicity of wearables.
Copyright © 2022 To-Hieu Dao et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
