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Functional motion detection based on artificial intelligence

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Abstract

Sports injuries can be a major problem for athletes. Therefore, sports injury protection has become a key focus of attention in sports and medical circles. With widening participation in sport, related injuries can have an impact at national level. However, many areas around the world lack adequate medical resources. It takes more time and money for local people to get to the nearest rehabilitation department or physical therapy studio. Artificial intelligence (AI) has undergone vigorous development, leading to increased computing speed and accuracy. Nowadays, two-dimensional image signals can be used for body-posture recognition. This research is based on the Openpose limb-detection AI model, which has corrective exercise training elements and uses functional motion-detection technology as the diagnostic basis (combined with physical therapists’ clinical knowledge of rehabilitation interventions). We propose a 2-D imaging physical health detection system. The system is divided into four main parts: a mobile app user interface, the computing server, professional interface and database. A user video is recorded by the app. The computing server then calculates keypoints of the human body through Openpose and converts these data into clinical test indicators. Finally, health indicators are displayed, and the app registers action scores. The professional interface presents users with health feedback and recommends suitable rehabilitation videos. The computing server is divided into two parts: limb detection and the detection system. The limb-detection aspect is divided into four parts: sample collection, video processing, keypoint processing and results comparison. The detection system is subdivided into system architecture and a health evaluation (forming the basis for video recommendations). The key contributions of this paper are that the proposed system can calculate body posture and automatically detect the physical condition and health of the body. In addition to reducing dependence on professional human resources, the system can also save the trouble of manual angle measurement in traditional physical therapy.

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Acknowledgements

This work was supported by the Young Scientific Research Fund of Dongguan Polytechnic (No. 2020D04).

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Authors and Affiliations

  1. Department of Computer Engineering, Dongguan Polytechnic, Dongguan, 523808, China

    Lingfeng Huang

  2. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Genggeng Liu

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  1. Lingfeng Huang
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  2. Genggeng Liu
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Correspondence to Lingfeng Huang.

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Huang, L., Liu, G. Functional motion detection based on artificial intelligence. J Supercomput 78, 4290–4329 (2022). https://doi.org/10.1007/s11227-021-04037-3

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  • DOI: https://doi.org/10.1007/s11227-021-04037-3

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