Research on Prediction Model of Physical Activity Energy Expenditure with Wearable Sensors

• Published in: Jisuanji kexue yu tansuo Vol. 16; no. 12; pp. 2832 - 2840
• Main Author: WANG Lin, SUN Qian, MA Xiaona, GAO Yongyan, LIU Yi, MA Hongwei, YANG Dongqiang
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01-12-2022
• Subjects: physical activity energy expenditure|feature selection|neural network
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2104086

To solve the contradiction between multiple wearable sensor features and the limited computing power and storage capacity of embedded devices, feature engineering is used to select the best features for predicting physical activity energy expenditure (PAEE) on the basis of data fusion of multiple sensors (accelerometer and gyroscope sensors). In the data preprocessing stage, time-domain and frequency-domain features of the sensor are extracted by using sliding window technology, and sinusoidal curve fitting is used for dataset at three velocity levels, finally hypothesis testing is carried out to check data outliers. A WEKA experimental platform is constructed based on filtering, warpper and embedded feature selection algorithms and machine learning prediction models such as multiple linear regression, regression tree, support vector machine and neural network. Finally, the optimal model is selected by evaluating the correlation coefficient and mean absolute error of each model during the decision level fusio