Seungmin Jeong, Cheolwoo Choi, Dongik Oh, Journal of Internet Computing and Services, Vol. 21, No. 4, pp. 127-135, Aug. 2020
10.7472/jksii.2020.21.4.127, Full Text:
Keywords: Human Activity Recognition, Smartwatch, Accelerometer, Machine Learning, Activity Classification, feature extraction, feature reduction

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%.

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