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Rigid Body Equilibrium Problems - II

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Updated: Jul 20, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

Automatic multi-IMU-based deep learning evaluation of intensity during static standing balance training exercises.

Safa Jabri¹, Jeremiah Hauth¹, Christopher DiCesare¹

¹University of Michigan-Ann Arbor, Ann Arbor, USA.

Journal of Neuroengineering and Rehabilitation

|November 28, 2025

Summary

Wearable sensors and machine learning models can accurately estimate balance exercise intensity. This technology supports effective home-based rehabilitation by monitoring training dosage.

Keywords:

Balance Balance rehabilitation IMU Machine learning Physical therapy Standing balance.Wearable sensors

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Last Updated: Jul 20, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Published on: April 12, 2016

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Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability

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Published on: September 18, 2020

Area of Science:

Biomedical Engineering
Rehabilitation Science
Machine Learning

Background:

Effective balance rehabilitation necessitates individualized exercise intensity.
Current in-clinic assessment by physical therapists (PTs) limits remote monitoring.
Developing home-based monitoring tools is crucial for progressive rehabilitation.

Purpose of the Study:

To train and evaluate machine learning models for estimating balance exercise intensity.
To utilize data from full-body wearable sensors for intensity assessment.
To support the development of home-based exercise dosage monitoring.

Main Methods:

Collected kinematic data from 47 participants using 13 full-body inertial measurement units (IMUs).
Recruited 42 PTs to rate balance intensity from videos as ground truth.
Trained Convolutional Neural Networks (CNNs) to predict intensity from IMU data.

Main Results:

CNN models using all 13 IMUs achieved a Root Mean Square Error (RMSE) of 0.66, below typical PT inter-rater variability (RMSE 0.74).
Model performance stabilized with four sensors, optimally placed on thighs and back.
Sensitivity analysis confirmed sensor placement impacts predictive accuracy.

Conclusions:

Balance intensity can be accurately assessed using wearable sensors and CNN models.
This approach can enhance supervision and effectiveness of home-based balance rehabilitation.
Enables objective monitoring for personalized exercise prescription.