inis 22(4): e4

Research Article

Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm

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  • @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
To-Hieu Dao1, Hai-Yen Hoang2, Van-Nhat Hoang1, Duc-Tan Tran1, Duc-Nghia Tran3,*
  • 1: Phenikaa University
  • 2: Thai Nguyen University
  • 3: Vietnam Academy of Science and Technology
*Contact email: nghiatd@ioit.ac.vn

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.