A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

Shangjun Huang; Houde Dai; Xiaoming Yu; Xie Wu; Kuan Wang; Jiaxin Hu; Hanchen Yao; Rui Huang; Wenxin Niu

doi:10.1016/j.isci.2024.109093

A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network

iScience. 2024 Feb 2;27(3):109093. doi: 10.1016/j.isci.2024.109093. eCollection 2024 Mar 15.

Authors

Shangjun Huang¹, Houde Dai², Xiaoming Yu³, Xie Wu⁴, Kuan Wang¹, Jiaxin Hu², Hanchen Yao², Rui Huang⁴, Wenxin Niu¹

Affiliations

¹ Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China.
² Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China.
³ Rehabilitation Medical Center, Shanghai Seventh's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200137, China.
⁴ Key Laboratory of Exercise and Health Sciences, Ministry of Education, Shanghai University of Sport, Shanghai 200438, China.

Abstract

The monitoring of treadmill walking energy expenditure (EE) plays an important role in health evaluations and management, particularly in older individuals and those with chronic diseases. However, universal and highly accurate prediction methods for walking EE are still lacking. In this paper, we propose an ensemble neural network (ENN) model that predicts the treadmill walking EE of younger and older adults and stroke survivors with high precision based on easy-to-obtain features. Compared with previous studies, the proposed model reduced the estimation error by 13.95% and 66.20% for stroke survivors and younger adults, respectively. Furthermore, a contactless monitoring system was developed based on Kinect, mm-wave radar, and ENN algorithms, and the treadmill walking EE was monitored in real time. This ENN model and monitoring system can be combined with smart devices and treadmill, making them suitable for evaluating, monitoring, and tracking changes in health during exercise and in rehabilitation environments.

Keywords: Applied sciences; Health sciences; Natural sciences.