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一种基于集成神经网络的用于准确预测跑步机行走过程中能量消耗的非接触式监测系统。

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

作者信息

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

机构信息

Translational Research Center, Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China.

Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, Jinjiang 362201, China.

出版信息

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

DOI:10.1016/j.isci.2024.109093
PMID:38375238
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10875158/
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.

摘要

监测跑步机行走能量消耗（EE）在健康评估和管理中起着重要作用，尤其是在老年人和患有慢性疾病的人群中。然而，目前仍缺乏通用且高度准确的行走EE预测方法。在本文中，我们提出了一种集成神经网络（ENN）模型，该模型基于易于获取的特征，高精度地预测年轻人、老年人和中风幸存者的跑步机行走EE。与先前的研究相比，所提出的模型分别将中风幸存者和年轻人的估计误差降低了13.95%和66.20%。此外，基于Kinect、毫米波雷达和ENN算法开发了一种非接触式监测系统，并对跑步机行走EE进行实时监测。这种ENN模型和监测系统可以与智能设备和跑步机相结合，使其适用于评估、监测和跟踪运动期间以及康复环境中的健康变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/d495be6e2013/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/2d803182b390/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/ab4fa6033e17/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/50d0bf45687e/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/ce129716d393/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/fd8824688cf2/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/9b12cc70ae5b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/fb6982b3f509/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/d495be6e2013/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/2d803182b390/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/ab4fa6033e17/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/50d0bf45687e/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/ce129716d393/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/fd8824688cf2/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/9b12cc70ae5b/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/fb6982b3f509/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2391/10875158/d495be6e2013/gr7.jpg

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