{"title":"利用无标记运动捕捉智能手机应用程序实时估算垂直跳跃高度:概念验证案例研究","authors":"Carlos Balsalobre-Fernández","doi":"10.1177/17543371241227817","DOIUrl":null,"url":null,"abstract":"The aim of the present proof-of-concept case study was to explore the potential of a novel technology using artificial intelligence techniques to measure countermovement jump height (CMJ-h) in real time. Four hundred jumps were recorded from a single male participant over a period of 24 consecutive weeks, while CMJ-h was simultaneously registered with a force plate and a newly developed version of the My Jump Lab iOS app that used computer vision to measure CMJ-h in real time with the iPhone camera. A very high correlation ( r = 0.971, 95% CI = 0.963–0.975) and large agreement (ICC = 0.969, 95% CI = 0.963–0.975) were observed between measurements. Statistically significant, large differences were observed between instruments (mean absolute difference = 0.06 ± 0.01 m, d = 4.4, p < 0.001). However, when using the regression equation between devices to correct the app’s raw data ( R<jats:sup>2</jats:sup> = 0.94, y = 1.0004x – 0.0641), non-significant, trivial differences were observed (mean absolute difference = 0.01 ± 0.008 m, d = 0.1, p = 1.000). Collectively, the findings of this study highlight the potential of this novel artificial intelligence app for the measurement of CMJ-h in real time. However, considering the nature of this investigation, more research is needed to confirm the results observed in a wider population.","PeriodicalId":20674,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","volume":"22 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real time estimation of vertical jump height with a markerless motion capture smartphone app: A proof-of-concept case study\",\"authors\":\"Carlos Balsalobre-Fernández\",\"doi\":\"10.1177/17543371241227817\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of the present proof-of-concept case study was to explore the potential of a novel technology using artificial intelligence techniques to measure countermovement jump height (CMJ-h) in real time. Four hundred jumps were recorded from a single male participant over a period of 24 consecutive weeks, while CMJ-h was simultaneously registered with a force plate and a newly developed version of the My Jump Lab iOS app that used computer vision to measure CMJ-h in real time with the iPhone camera. A very high correlation ( r = 0.971, 95% CI = 0.963–0.975) and large agreement (ICC = 0.969, 95% CI = 0.963–0.975) were observed between measurements. Statistically significant, large differences were observed between instruments (mean absolute difference = 0.06 ± 0.01 m, d = 4.4, p < 0.001). However, when using the regression equation between devices to correct the app’s raw data ( R<jats:sup>2</jats:sup> = 0.94, y = 1.0004x – 0.0641), non-significant, trivial differences were observed (mean absolute difference = 0.01 ± 0.008 m, d = 0.1, p = 1.000). Collectively, the findings of this study highlight the potential of this novel artificial intelligence app for the measurement of CMJ-h in real time. However, considering the nature of this investigation, more research is needed to confirm the results observed in a wider population.\",\"PeriodicalId\":20674,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/17543371241227817\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/17543371241227817","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Real time estimation of vertical jump height with a markerless motion capture smartphone app: A proof-of-concept case study
The aim of the present proof-of-concept case study was to explore the potential of a novel technology using artificial intelligence techniques to measure countermovement jump height (CMJ-h) in real time. Four hundred jumps were recorded from a single male participant over a period of 24 consecutive weeks, while CMJ-h was simultaneously registered with a force plate and a newly developed version of the My Jump Lab iOS app that used computer vision to measure CMJ-h in real time with the iPhone camera. A very high correlation ( r = 0.971, 95% CI = 0.963–0.975) and large agreement (ICC = 0.969, 95% CI = 0.963–0.975) were observed between measurements. Statistically significant, large differences were observed between instruments (mean absolute difference = 0.06 ± 0.01 m, d = 4.4, p < 0.001). However, when using the regression equation between devices to correct the app’s raw data ( R2 = 0.94, y = 1.0004x – 0.0641), non-significant, trivial differences were observed (mean absolute difference = 0.01 ± 0.008 m, d = 0.1, p = 1.000). Collectively, the findings of this study highlight the potential of this novel artificial intelligence app for the measurement of CMJ-h in real time. However, considering the nature of this investigation, more research is needed to confirm the results observed in a wider population.
期刊介绍:
The Journal of Sports Engineering and Technology covers the development of novel sports apparel, footwear, and equipment; and the materials, instrumentation, and processes that make advances in sports possible.