What question did this study set out to answer?

March 28, 2026Open Access

A Non-Invasive Simplified Model for Estimating Lower Limb Muscle Forces During Slow Gait in Older Adults and Post-Stroke Individuals

KLKai LiuUniversity of Tasmania HGHongxiang GuoZhejiang University of Science and Technology JLJiaying LiuZhejiang University of Science and Technology

Key Points

The research aims to create a non-invasive model for estimating lower limb muscle forces during slow gait in older and post-stroke individuals.
Designed a muscle force estimation model using kinematic parameters.
Constructed a Digital Twin Musculoskeletal Model calibrated to individual anthropometry.
Evaluated model performance with twelve elderly subjects at various walking speeds.
Strong Pearson correlations were found between the model predictions and reference data for nine lower limb muscles at slow walking speeds.
Correlations declined for some muscles as walking speed increased due to unaccounted factors.
The model showed consistent performance in stroke patients for specific affected side muscles, indicating utility in rehabilitation.

Abstract

This study proposes a non-invasive, simplified muscle force estimation model (NSMFEM) designed for elderly individuals and stroke patients under slow walking conditions. The model estimates lower limb muscle forces dynamically using only kinematic parameters—with real-time muscle fiber length as the key variable—thus avoiding the limitations of traditional surface electromyography (sEMG) -based approaches such as environmental interference, signal noise, and difficulty in obtaining deep muscle sEMG. A personalized Digital Twin Musculoskeletal Model (DTMSM) was constructed by scaling a reference kinematic model and calibrating muscle origin/insertion markers based on individual anthropometry. Muscle architecture indices were derived from a multiple regression model with publicly available anatomical data. Twelve elderly subjects (eight healthy ESND and four post-stroke ESP) were evaluated at varying walking speeds. Results at slow speeds (X-slow and slow) show strong Pearson correlations between NSMFEM predictions and reference data for the majority of nine representative lower limb muscles (e. g. , TFL, Iliacus, Pectineus, TibAnt, Soleus) ; passive forces of TFL, Iliacus, and VasInt also correlate strongly. As speed rises, correlations for some muscles (e. g. , VasInt, TibPost) decline, reflecting the growing influence of segmental acceleration and muscle activation—factors omitted in the model. For stroke patient gait (ESP), Spearman analysis indicates maintained strong correlations for affected side muscles GlutMax1, TFL, Pectineus, and Soleus, supporting the model’s utility in stroke rehabilitation assessment. Overall, NSMFEM offers a practical, sEMG free method for non-invasive dynamic muscle force estimation in slow walking elderly and post-stroke populations, aiding functional assessment and personalized rehabilitation planning. Future efforts will aim to incorporate muscle activation corrections to extend the model to faster walking speeds.

AI에게 질문

Bookmark

View Full Paper