Human Pose Estimation for Rehabilitation by Computer Vision

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Human pose estimation (HPE) is a valuable tool for rehabilitation, providing critical insights into the body's posture and movements. Both patients and therapists can significantly benefit from this technology, which enhances various aspects of the rehabilitation process by offering precise and real-time feedback on body mechanics. This research explores four well-known models in HPE: BlazePose, OpenPose, MoveNet, and OpenPifPaf. Each model is examined in detail, focusing on their architecture and working principles. BlazePose is renowned for its efficiency and accuracy, making it suitable for real-time performance applications. OpenPose is a comprehensive framework that detects multiple body parts, offering a detailed human posture analysis. MoveNet is designed for high-speed applications, providing quick and accurate pose estimation, while OpenPifPaf excels in producing precise keypoint detection, which is crucial for detailed posture analysis. The comparison between these models is demonstrated through practical cases of rehabilitation exercises. Since rehabilitation often requires exercises to be performed slowly and deliberately to ensure safety and effectiveness, this study emphasizes model accuracy over speed. We can assess the models in actual rehabilitation scenarios' reliability and suitability for different rehabilitation exercises. This research aims to provide a thorough understanding of how each HPE model operates and their respective strengths and limitations in rehabilitation. Through detailed analysis and real-world comparisons, we highlight the potential of HPE technology to improve rehabilitation outcomes by offering accurate, real-time feedback to both patients and therapists. This feature can lead to more effective rehabilitation programs tailored to the specific needs of individual patients.