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Publication
Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation
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Advisor(s)
Abstract(s)
Angle assessment is crucial in rehabilitation and significantly
influences physiotherapists’ decision-making. Although visual inspection
is commonly used, it is known to be approximate. This work aims to
be a preliminary study about using the AI image-based to assess upper
limb joint angles. Two main frameworks were evaluated: MediaPipe and
Yolo v7. The study was performed with 28 participants performing four
upper limb movements. The results showed that Yolo v7 achieved greater
estimation accuracy than Mediapipe, with MAEs of around 5◦ and 17◦,
respectively. However, even with better results, Yolo v7 showed some
limitations, including the point of detection in only a 2D plane, the
higher computational power required to enable detection, and the difficulty
of performing movements requiring more than one degree of Freedom
(DOF). Nevertheless, this study highlights the detection capabilities
of AI approaches, showing be a promising approach for measuring
angles in rehabilitation activities, representing a cost-effective and easyto-
implement solution.
Description
Keywords
Join Angle Measurement Artificial Intelligence Motion Capture Robotic Rehabilitation
Pedagogical Context
Citation
Klein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, José (2024). Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 3–18. ISBN 978-3-031-53035-7
Publisher
Springer Nature