Exercise is an indispensable part of people’s lives and is closely related to their health. Human Activity Recognition (HAR), which involves detects and analyzes human body activity, has become the focus of current research. Photoplethysmography (PPG) has advantages such as convenience for detection and low cost, and is widely used in wearable devices becoming an ideal choice for HAR. In this study, we used wavelet scattering transform (WST) to extract features from PPG and then performed activity recognition on it. We achieved excellent classification accuracy of 92.54% and 97.76% respectively in the experiments of three-class and four-class exercise detection. The results showed this method based on wavelet scattering transform and PPG can accurately detect exercise types and provide effective support for HAR.
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