Stroke is the leading cause of disability not only throughout Europe. The potential aftereffects of a stroke can impact physical, cognitive, and emotional areas. Among the physical consequences, stroke survivors usually develop spasticity, an abnormal muscle tightness caused by prolonged muscle contraction, which results in reduced functionality, loss of independence, and decreased quality of life. Early detection and treatment, which includes rehabilitation and pharmaceutical therapy, are crucial to regain muscle tone and recover functionality of the limbs.

The main objective of this project is the design and development of an intelligent decision support system based on machine learning to identify the muscles affected by spasticity as well as objectively quantify the level of deterioration in both the upper and lower limbs. The approach is focused on stroke patients and aims to minimize the invasiveness of the assessment by using high-definition images of the limbs of the patient as input of a HPD system.

First Android-based prototype using BlazePose for detecting upper and lower limb joints and landmarks.

Project ref. PID2024-155343OB-I00 funded by MCIU/ AEI / 10.13039/501100011033 /FEDER, UE.