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Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation

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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.

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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

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