Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (12): 2832-2840.DOI: 10.3778/j.issn.1673-9418.2104086

• Artificial Intelligence • Previous Articles     Next Articles

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

WANG Lin1, SUN Qian1, MA Xiaona1, GAO Yongyan2, LIU Yi1, MA Hongwei1, YANG Dongqiang1,+()   

  1. 1. College of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
    2. Department of Sports, Shandong Jianzhu University, Jinan 250101, China
  • Received:2021-04-13 Revised:2021-06-01 Online:2022-12-01 Published:2021-06-08
  • About author:WANG Lin, born in 1975, M.S. candidate, lecturer. Her research interests include machine learning, intelligent city, etc.
    SUN Qian, born in 1983, M.S. candidate, lecturer. Her research interest is intelligent city.
    MA Xiaona, born in 1977, M.S. candidate, associate professor. Her research interests include software testing, machine learning, etc.
    GAO Yongyan, born in 1980, M.S. candidate, associate professor. Her research interests include physical education teaching and training, physical culture, etc.
    LIU Yi, born in 1978, M.S. candidate, lecturer. His research interests include machine vision, artificial intelligence, etc.
    MA Hongwei, born in 1969, Ph.D. candidate, professor, M.S. supervisor. His research interests include computer system architecture, computer network, machine learning, etc.
    YANG Dongqiang, born in 1970, Ph.D. candidate, associate professor, M.S. supervisor. His research interests include natural language processing, artificial intelligence, etc.
  • Supported by:
    Natural Science Foundation of Shandong Province(ZR2018MF012);Natural Science Foundation of Shandong Province(ZR2020QF029);Ph.D. Foundation of Shandong Jianzhu University(XNBS1811)


王琳1, 孙倩1, 马晓娜1, 高永艳2, 刘毅1, 马宏伟1, 杨东强1,+()   

  1. 1.山东建筑大学 计算机科学与技术学院,济南 250101
    2.山东建筑大学 体育部,济南 250101
  • 通讯作者: +E-mail:
  • 作者简介:王琳(1975—),女,山东泗水人,硕士研究生,讲师,主要研究方向为机器学习、智慧城市等。
  • 基金资助:


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 fusion. The dataset with jitter is used as the test data, which shows that feature selection can mitigate model overfitting and improve the model’s generalization ability and robustness. Embedded feature selection adopts classical elastic network algorithm. Experimental results show that the features extracted from accelerometer sensors play a more decisive role than those from gyroscope sensors in PAEE and the neural network model of multi-sensor feature fusion based on correlation coefficient method is the optimal model.

Key words: physical activity energy expenditure, feature selection, neural network



关键词: 人体活动能量消耗, 特征选择, 神经网络

CLC Number: