ArticleiScience2024
A contactless monitoring system for accurately predicting energy expenditure during treadmill walking based on an ensemble neural network.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From past to future: a review of methods for assessing physical activity energy expenditure.Journal of health, population, and nutrition · 2025Article
- Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework.Frontiers in bioengineering and biotechnology · 2025Article
- Estimating within-stride metabolic cost from stride-average data using autoencoders and expander networks.Frontiers in bioengineering and biotechnology · 2025Article
- Comparison of two portable metabolic systems for measuring energy expenditure at rest and during exercise in untrained women.Frontiers in physiology · 2025Article
- Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Effects of 12-week gait retraining on plantar flexion torque, architecture, and behavior of the medial gastrocnemiusFrontiers in bioengineering and biotechnology · 2024Article
- 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.Frontiers in bioengineering and biotechnology · 2024Article
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Authors and funding
9 authors.
Funding
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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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.