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.
Keywords: BlazePose, Computer vision, Human pose estimation, MoveNet, OpenPose, OpenPifPaf.