{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:56:31Z","timestamp":1777704991596,"version":"3.51.4"},"reference-count":25,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,2,2]]},"abstract":"<jats:p>At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes\u2019 motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete\u2019s actual situation to establish the athlete\u2019s limb trajectory calculation model and analyzes the athletes\u2019 movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.<\/jats:p>","DOI":"10.3233\/jifs-189229","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T11:04:08Z","timestamp":1599563048000},"page":"2325-2335","source":"Crossref","is-referenced-by-count":4,"title":["Monitoring and analysis of athletes\u2019 local body movement status based on BP neural network"],"prefix":"10.1177","volume":"40","author":[{"given":"Ling","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Physical Education, Northeast Electric Power University, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faze","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Basic Teaching and Research, Yango University, Fuzhou, 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