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Updated: Jul 5, 2025

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

A Machine Learning App for Monitoring Physical Therapy at Home.

Bruno Pereira^1,2, Bruno Cunha^1,3, Paula Viana^1,2

¹Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This study introduces a smartphone app for physical therapy, enabling patients to perform shoulder rehabilitation exercises autonomously. The app uses pose estimation to assess movement quality, improving accessibility and reducing costs associated with traditional therapy.

Keywords:

exercise evaluation mobile health pose estimation rehabilitation remote monitoring

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Last Updated: Jul 5, 2025

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Digital Health

Background:

Traditional shoulder rehabilitation is limited by technician availability, cost, and patient travel.
Autonomous recovery sessions can improve accessibility and adherence to physical therapy.
Wearable and camera-based systems offer potential for remote rehabilitation support.

Approach:

Developed a smartphone application utilizing pose estimation (MediaPipe) to analyze patient movements.
Extracted 33 key body points to evaluate exercise quality against reference videos.
Employed cosine similarity and dynamic time warping for movement comparison.

Key Points:

Validated the smartphone application's methods against an optoelectronic motion capture system (QTM).
Identified statistically significant differences in method performance across various exercises.
Emphasized the need for exercise-specific method selection in digital rehabilitation.

Conclusions:

The smartphone-based system facilitates autonomous shoulder rehabilitation, enhancing patient independence.
Pose estimation offers a viable tool for assessing movement quality in remote physical therapy.
Future research should explore further optimization and validation of digital rehabilitation tools.