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Wearable Sensor Data Based Human Activity Recognition using Deep Learning: A new approach

Abstract : With a tremendous increase in mobile and wearable devices, the study of sensor-based activity recognition has drawn a lot of attention in the past years. In recent years, the applications of Human Activity Recognition are getting more and more attention, especially in eldercare and healthcare as an assistive technology when combined with the Internet of Things. In this paper, we propose three deep learning approaches to improve the accuracy of activity detection on the WISDM dataset. Particularly, we apply a convolutional neural network to extract the interesting features, then we use softmax function, support vector machine, and random forest for classification tasks. The results show that the hybrid algorithm, convolutional neural network combined with the support vector machine, outperforms all the previous methods in classifying every activity. In addition, not only the support vector machine but also the random forest shows better accuracy in classification tasks than the neural network classification and the former approaches do.
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https://hal.archives-ouvertes.fr/hal-02547033
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Submitted on : Thursday, June 3, 2021 - 12:07:06 PM
Last modification on : Wednesday, November 9, 2022 - 1:20:04 PM

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Phuong Hanh Tran, Thong Nguyen, Kim Phuc Tran, Cédric Heuchenne. Wearable Sensor Data Based Human Activity Recognition using Deep Learning: A new approach. FLINS/ISKE 2020, Sep 2020, Cologne (on line), Germany. pp.581-588, ⟨10.1142/9789811223334_0070⟩. ⟨hal-02547033v2⟩

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