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Home-Based Monitor for Gait and Activity Analysis
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Smart Sensor-Driven Gait Rehabilitation Walker Using Machine Learning for Predictive Home-Based Therapy.

Gokul Manavalan1,2, Yuval Arnon3, A N Nithyaa1

  • 1Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai 602105, India.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a smart rehabilitation walker that improves gait symmetry and neuromuscular activity in patients with gait abnormalities. The low-cost device offers real-time feedback for enhanced mobility and rehabilitation.

Keywords:
Gaussian Process Regressionabnormal gaitforce symmetryhaptic feedbackhome rehabilitationsmart rehabilitation walker

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Gait Analysis

Background:

  • Abnormal gait is a significant clinical challenge, especially in aging populations.
  • Existing rehabilitation methods often lack real-time monitoring and adaptive feedback.
  • There is a need for low-cost, accessible solutions for gait rehabilitation and monitoring.

Purpose of the Study:

  • To develop and evaluate a modular, low-cost Smart Rehabilitation Walker (SRW) with integrated multimodal sensing and haptic feedback.
  • To assess the SRW's effectiveness in improving gait biomechanics and neuromuscular activity.
  • To validate the SRW's analytical pipeline for gait rehabilitation trajectory modeling.

Main Methods:

  • The SRW integrates force-sensing resistors, inertial measurement units, and surface electromyography (sEMG).
  • A closed-loop feedback architecture provides real-time corrective intervention.
  • A 15-day pilot study with rheumatoid arthritis patients and validation with an amyotrophic lateral sclerosis dataset were conducted.

Main Results:

  • Significant reduction in inter-limb load asymmetry (79.26% decrease in Force Symmetry Index).
  • Substantial increase in neuromuscular activation (ΔEMG = 4.28, p < 0.001).
  • Gaussian Process Regression model achieved high predictive accuracy (R² ≈ 0.9) for rehabilitation trajectories.

Conclusions:

  • Sensor-enabled assistive walkers offer a scalable platform for quantitative gait rehabilitation.
  • The SRW system enables adaptive feedback and long-term mobility monitoring.
  • This technology holds promise for improving gait in individuals with neuromuscular and musculoskeletal disorders.