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http://dx.doi.org/10.7472/jksii.2020.21.4.127

Development of a Machine-Learning based Human Activity Recognition System including Eastern-Asian Specific Activities  

Jeong, Seungmin (Department of Medical IT Engineering, Soonchunhyang University)
Choi, Cheolwoo (Department of Medical IT Engineering, Soonchunhyang University)
Oh, Dongik (Department of Medical IT Engineering, Soonchunhyang University)
Publication Information
Journal of Internet Computing and Services / v.21, no.4, 2020 , pp. 127-135 More about this Journal
Abstract
The purpose of this study is to develop a human activity recognition (HAR) system, which distinguishes 13 activities, including five activities commonly dealt with in conventional HAR researches and eight activities from the Eastern-Asian culture. The eight special activities include floor-sitting/standing, chair-sitting/standing, floor-lying/up, and bed-lying/up. We used a 3-axis accelerometer sensor on the wrist for data collection and designed a machine learning model for the activity classification. Data clustering through preprocessing and feature extraction/reduction is performed. We then tested six machine learning algorithms for recognition accuracy comparison. As a result, we have achieved an average accuracy of 99.7% for the 13 activities. This result is far better than the average accuracy of current HAR researches based on a smartwatch (89.4%). The superiority of the HAR system developed in this study is proven because we have achieved 98.7% accuracy with publically available 'pamap2' dataset of 12 activities, whose conventionally met the best accuracy is 96.6%.
Keywords
Human Activity Recognition; Smartwatch; Accelerometer; Machine Learning; Activity Classification; Feature Extraction; Feature Reduction;
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