Development and Validation of an Artificial Intelligence‑Based Motion Analysis System for Upper Extremity Rehabilitation Exercises in Patients with Spinal Cord Injury: A Randomized Controlled Trial

Healthcare, 2024 · DOI: 10.3390/healthcare12010007 · Published: December 19, 2023

Spinal Cord Injury Bioinformatics Rehabilitation

Simple Explanation

This study created an AI system that gives real-time feedback on movement to help people with spinal cord injuries (SCI) do their rehabilitation exercises correctly and stay motivated. The system uses a camera to watch the person exercise and provide feedback on their form during Thera-band exercises, focusing on motions like chest press, shoulder press, and arm curl. The goal was to see if this AI-powered feedback could help people with SCI improve their upper body strength and make rehab exercises more engaging.

Study Duration

8 weeks

Participants

9 participants with spinal cord injury (4 in experimental group, 5 in control group)

Evidence Level

Level 1: Randomized Controlled Trial

Key Findings

1
The experimental group (using the AI system) showed increased strength in all measured variables, whereas the control group showed constant or reduced results.
2
There was no statistically significant difference between the experimental and control groups, potentially due to the small sample size.
3
The usability test indicated that participants liked being able to see themselves through real‑time motion analysis and that the instructor guided them on how to perform the exercise.

Research Summary

This study developed an AI‑based real‑time motion feedback system for patients with spinal cord injury (SCI) during rehabilitation, aiming to enhance their interest and motivation. The effectiveness of the system in improving upper‑limb muscle strength during the Thera band ex‑ercises was evaluated. Consequently, the computer program‑based system developed in this study could be effective in muscle strengthening. Furthermore, these findings may serve as a valuable foundation for future AI‑driven rehabilitation exercise systems.

Practical Implications

Enhanced Motivation

AI-based feedback system can increase patient engagement and motivation during rehabilitation exercises.

Potential for Home Use

The system can be further developed for independent home use, providing convenient access to rehabilitation exercises.

Foundation for Future AI Systems

The findings provide a valuable foundation for developing more advanced AI-driven rehabilitation exercise systems.

Study Limitations

1
Small sample size
2
Participants were chronic free‑living outpatients, making it difficult to control external factors
3
Some participants missed exercise sessions

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