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A New Sensorized Approach Based on a DeepLabCut Model and IR Thermography for Characterizing the Thermal Profile in

Davide Crisafulli¹, Marta Spataro¹, Cristiano De Marchis¹

¹Department of Engineering, University of Messina, Contrada di Dio, 98166 Messina, Italy.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This study uses infrared thermography and AI (DeepLabCut) to monitor knee temperature during exercises. This non-invasive method aids in diagnosing knee injuries and tracking recovery.

Keywords:

DeepLabCut Wii Balance Board diagnostic infrared thermography sit-to-stand

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Area of Science:

Biomedical Engineering
Sports Medicine
Medical Imaging

Background:

Knee injuries and diseases affect athletes and older adults, necessitating effective monitoring tools.
Surface temperature analysis offers insights into joint health, aiding early diagnosis of conditions like osteoarthritis and tendon injuries.
Current methods for temperature assessment can be time-consuming and less accurate.

Purpose of the Study:

To develop an innovative, non-invasive method for monitoring knee joint temperature variations.
To integrate infrared thermography with AI-driven analysis for precise, marker-free measurements.
To correlate thermal profiles with biomechanical data during functional exercises.

Main Methods:

Utilized infrared thermography to capture knee surface temperature during sit-to-stand exercises.
Employed Resnet 152 (DeepLabCut based) for automated region of interest (ROI) selection and analysis.
Collected weight distribution data using a Wii Balance Board concurrently with thermal imaging.

Main Results:

Successfully detected and monitored temperature variations in specific knee regions.
Automated ROI selection via DeepLabCut improved accuracy and efficiency over traditional methods.
Established a correlation between thermal profiles and weight distribution during exercises.

Conclusions:

The combined approach of thermography and DeepLabCut offers a precise, non-invasive tool for knee health assessment.
This technology can aid in diagnosing knee pathologies, evaluating rehabilitation progress, and assessing treatment efficacy.
Highlights the potential for accessible, preventive diagnostic tools for joint health monitoring.