{"title":"Design of a Portable Biofeedback System for Monitoring Femoral Load During Partial Weight-Bearing Walking","authors":"Tao Ma;Tianyang Fan;Xun Xu;Tao Sun","doi":"10.1109/TNSRE.2025.3540062","DOIUrl":null,"url":null,"abstract":"Patients with femoral fractures are typically advised to undergo partial weight-bearing (PWB) gait training during the postoperative rehabilitation period to facilitate bone healing and restore lower limb function. Various current portable biofeedback devices monitor ground reaction force (GRF) to assess the femoral loading of patients with fractures during PWB walking. However, due to the influence of muscle forces and the complexity of load transmission in the lower limbs, GRF may not accurately reflect the internal forces in the femur during walking. In this study, we developed an innovative biofeedback device that incorporates inertial measurement units and pressure-sensitive insoles. Utilizing data collected from 12 participants, a physics-informed temporal convolutional network (PITCN) method was proposed to estimate the internal femoral loading. The performance of the PITCN method was compared with two other machine learning approaches and a baseline method, demonstrating superior predictive capabilities. The study also revealed that, irrespective of the weight-bearing level during walking, the peak femoral loading consistently exceeded the peak GRF. Moreover, the timing of the peak values for these two forces within each gait cycle may not always coincide. These findings further emphasize the necessity of monitoring and providing feedback on the actual femoral loading, rather than solely relying on GRF, during PWB gait training for patients with fractures. The developed system is a non-invasive, reliable, and portable device that provides audio feedback. It shows potential as a viable solution for gait rehabilitation training in daily life, contributing to the enhancement of patients’ rehabilitation outcomes.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"770-779"},"PeriodicalIF":4.8000,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879047","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10879047/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Patients with femoral fractures are typically advised to undergo partial weight-bearing (PWB) gait training during the postoperative rehabilitation period to facilitate bone healing and restore lower limb function. Various current portable biofeedback devices monitor ground reaction force (GRF) to assess the femoral loading of patients with fractures during PWB walking. However, due to the influence of muscle forces and the complexity of load transmission in the lower limbs, GRF may not accurately reflect the internal forces in the femur during walking. In this study, we developed an innovative biofeedback device that incorporates inertial measurement units and pressure-sensitive insoles. Utilizing data collected from 12 participants, a physics-informed temporal convolutional network (PITCN) method was proposed to estimate the internal femoral loading. The performance of the PITCN method was compared with two other machine learning approaches and a baseline method, demonstrating superior predictive capabilities. The study also revealed that, irrespective of the weight-bearing level during walking, the peak femoral loading consistently exceeded the peak GRF. Moreover, the timing of the peak values for these two forces within each gait cycle may not always coincide. These findings further emphasize the necessity of monitoring and providing feedback on the actual femoral loading, rather than solely relying on GRF, during PWB gait training for patients with fractures. The developed system is a non-invasive, reliable, and portable device that provides audio feedback. It shows potential as a viable solution for gait rehabilitation training in daily life, contributing to the enhancement of patients’ rehabilitation outcomes.
期刊介绍:
Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.