Supervised Classification of Motor-Rehabilitation Body Movements with RGB Cameras and Pose Tracking Data

The technological evolution allowed the use of a single camera for precise and effective body tracking, reducing the cost and increasing the accessibility of applications in places where depth cameras and wearable sensors are not available. This paper describes and implements a supervised machine le...

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Bibliographic Details
Published in:Journal on Interactive Systems 2022-09, Vol.13 (1), p.221-231
Main Authors: Rodrigues, Luis Guilherme Silva, Dias, Diego Roberto Colombo, De Paiva Guimarães, Marcelo, Brandão, Alexandre Fonseca, Rocha, Leonardo C., Iope, Rogério Luiz, Brega, José Remo Ferreira
Format: Article
Language:English
Subjects:
classification
computer vision
machine learning
motor-rehabilitation
pose tracking
supervised learning
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Summary:The technological evolution allowed the use of a single camera for precise and effective body tracking, reducing the cost and increasing the accessibility of applications in places where depth cameras and wearable sensors are not available. This paper describes and implements a supervised machine learning process consisting of a mobile application used as a motion capture device which also transforms the data into an input for a machine learning model that classifies upper and lower limbs movements (24 types of human movements). The user performs movements in front of the camera, and the trained model classifies them. We designed the system to work in a motor-rehabilitation context to assist the professional while the patient does physical exercises. The implementation can summarize the movements made during the rehabilitation sessions by counting the repetitions and classifying them when done completely or reached a specific range of motion.
ISSN:2763-7719
2763-7719
DOI:10.5753/jis.2022.2409