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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

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Towards Intelligent Assessment in Personalized Physiotherapy with Computer Vision.

Victor García¹, Olga C Santos¹

¹PhyUM Reserch Center, Department of Artificial Intelligence, Computer Science School, UNED, 28040 Madrid, Spain.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces YOLO Pose for automated physiotherapy assessments, enabling objective mobility analysis from video data. This computer vision approach enhances rehabilitation tracking and patient interaction potential.

Keywords:

YOLO Pose computer vision optical sensors physical therapy assessment pose estimation semantic framework

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Published on: March 14, 2017

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Published on: December 11, 2013

Area of Science:

Computer Vision
Biomedical Engineering
Rehabilitation Science

Background:

Traditional physiotherapy assessments are often time-consuming and subjective.
Accurate patient mobility evaluation is crucial for effective rehabilitation.
There is a need for objective and automated methods in physiotherapy.

Purpose of the Study:

To explore the use of open-source computer vision algorithms, specifically YOLO Pose, for automated, vision-based analysis in physiotherapy.
To enable objective evaluation of patient movements and rehabilitation progress using skeletal data from optical sensors.
To develop a semantic framework for structured interpretation of clinical parameters from visual information.

Main Methods:

Utilized YOLO Pose, an open-source computer vision algorithm, for pose estimation from video input.
Extracted skeletal data from optical sensor (camera) feeds.
Analyzed visual information to propose a semantic framework for clinical parameter interpretation.

Main Results:

YOLO Pose demonstrated reliable pose estimation capabilities.
The system provides a foundation for objective assessment of patient mobility and rehabilitation progress.
Preliminary findings support the potential for automated, vision-based physiotherapy analysis.

Conclusions:

YOLO Pose offers a promising tool for objective and automated physiotherapy assessments.
The developed semantic framework aids in structured interpretation of clinical data.
Future work may integrate Natural Language Processing (NLP) for enhanced patient-AI interaction.