Feasibility of 3D Body Tracking from Monocular 2D Video Feeds in Musculoskeletal Telerehabilitation

Abstract

Musculoskeletal conditions affect millions of people globally; however, conventional treatments pose challenges concerning price, accessibility, and convenience. Many telerehabilitation solutions offer an engaging alternative but rely on complex hardware for body tracking. This work explores the feasibility of a model for 3D Human Pose Estimation (HPE) from monocular 2D videos (MediaPipe Pose) in a physiotherapy context, by comparing its performance to ground truth measurements. MediaPipe Pose was investigated in eight exercises typically performed in musculoskeletal physiotherapy sessions, where the Range of Motion (ROM) of the human joints was the evaluated parameter. This model showed the best performance for shoulder abduction, shoulder press, elbow flexion, and squat exercises. Results have shown a MAPE ranging between 14.9% and 25.0%, Pearson’s coefficient ranging between 0.963 and 0.996, and cosine similarity ranging between 0.987 and 0.999. Some exercises (e.g., seated knee extension and shoulder flexion) posed challenges due to unusual poses, occlusions, and depth ambiguities, possibly related to a lack of training data. This study demonstrates the potential of HPE from monocular 2D videos, as a markerless, affordable, and accessible solution for musculoskeletal telerehabilitation approaches. Future work should focus on exploring variations of the 3D HPE models trained on physiotherapy-related datasets, such as the Fit3D dataset, and post-preprocessing techniques to enhance the model’s performance.

Application of Information Theory to Computer Vision and Image Processing

“…The field of machine vision encompasses a diverse range of technologies and methodologies, including artificial intelligence algorithms like deep learning algorithms and neural networks for recognizing [12] and classifying objects in images or videos [13], enhancing image quality and reducing noise in images [14], and 3D vision and depth sensing [15]. These algorithms are robust and adaptable, and they are used in embedded systems [16], robust control mechanisms [17], inertial navigation systems, robotics, interconnectivity, big data applications, and cloud computing applications [18].…”