{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T12:36:58Z","timestamp":1765888618914,"version":"3.40.5"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,5,26]],"date-time":"2021-05-26T00:00:00Z","timestamp":1621987200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scientific Programming"],"published-print":{"date-parts":[[2021,5,26]]},"abstract":"<jats:p>In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes\u2019 posture is conducive to accurately judge sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes\u2019 performance and reducing physical damage. The manufacturing level of MEMS sensors continues to improve, and status perception of assembly objects is becoming more and more abundant and accurate. The shape is small and can be worn, and data can be collected continuously without obstacles. The price is relatively low, the privacy protection is strong, and the advantages are clear and prominent. A considerable number of technicians choose to use MEMS sensors as the main tool for human behavior detection data collection. Therefore, this article designs multiple MEMS inertial sensors to form a human body lower limb capture device, and its core components are composed of accelerometer, gyroscope, and magnetometer. In order to make the obtained acceleration value, angular velocity value, and magnetometer value accurately reflect the movement state of the lower limb structure, different data fusion algorithms and magnetometer ellipsoid fitting calibration algorithms are studied to realize the calculation of the posture angle of each joint point and obtain martial arts posture big data. In addition, through big data analysis, this article designs a martial arts training performance and injury risk prediction model, which can provide guidance and suggestions for martial arts teaching tasks.<\/jats:p>","DOI":"10.1155\/2021\/9993916","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:15:49Z","timestamp":1622160949000},"page":"1-8","source":"Crossref","is-referenced-by-count":7,"title":["Martial Arts Training Prediction Model Based on Big Data and MEMS Sensors"],"prefix":"10.1155","volume":"2021","author":[{"given":"Shisen","family":"Li","sequence":"first","affiliation":[{"name":"Department of Physical Education, North China University of Science and Technology, Tangshan 063210, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9873-0874","authenticated-orcid":true,"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Physical Education, Heng Shui University, Heng Shui 053000, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Physical Education, Heng Shui University, Heng Shui 053000, Hebei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.52810\/TPRIS.2021.100012"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10188-x"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.52810\/TPRIS.2021.100009"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6276"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1080\/01691864.2020.1713886"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.06.139"},{"article-title":"On-line event-driven hand gesture recognition based on surface electromyographic signals","author":"S. 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