Deep Analysis for Smartphone-based Human Activity Recognition


Pang, Ying Han and Ooi, Shih Yin and Chew, Yong Shan (2020) Deep Analysis for Smartphone-based Human Activity Recognition. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), 24-26 June 2020, Yogyakarta, Indonesia.

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Wearable-based approach and vision-based approach are two of the most common approaches in human activity recognition. However, the concern of privacy issues may limit the application of the vision-based approach. Besides, some individuals are reluctant to wear sensor devices. Hence, smartphone-based human physical activity recognition is a popular alternative. In this paper, we propose a deep analysis to interpret and predict accelerometer data captured using a smartphone for activity recognition. The proposed deep model is able to extract deep features from both spatial and temporal domains of the inertial data. The recognition accuracy of the proposed model is assessed using UCI and WISDM accelerometer data. Empirical results exhibit a promising performance, attaining accuracy score of 90% in UCI and 87% in WISDM database.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Human activity recognition
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 13 Oct 2021 02:59
Last Modified: 13 Oct 2021 03:55


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