University of Wollongong
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A single smartwatch-based segmentation approach in human activity recognition

journal contribution
posted on 2024-11-17, 16:59 authored by Yande Li, Lulan Yu, Jun Liao, Guoxin Su, Hashmi Ammarah, Li Liu, Shu Wang
The development of smart wearable devices has driven the rapid progress of activity recognition. However, existing activity recognition methods are still struggling to recognize single arm swings due to coarse-grained sensor data segmentation. Refined arm-swing-wise data segmentation is vital in some specific cases, such as the rehabilitation of disabled patients. In this paper, we propose a smartwatch-based arm-swing-wise data segmentation approach for human activity recognition, which converts original sensor signals into square-wave signals to detect the cut-off points of each arm swing. Particularly, our method can adaptively adjust the window size and step size of a sliding window without considering the change of swing speed. Empirical evaluation on two datasets, a self-collected dataset and a publicly-available benchmark dataset, shows superior performance of our approach over other methods under different settings, such as classifiers, features, and wearing positions.

Funding

National Natural Science Foundation of China (61977012)

History

Journal title

Pervasive and Mobile Computing

Volume

83

Language

English

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