Maximizing Markerless Motion Capture Data Processing Speed: Enabling Efficient Reporting: 222...American College of Sports Medicine (ACSM) Annual Meeting, May 27-30, 2025, Atlanta, Georgia.
The article focuses on the development and application of markerless motion capture technologies in research, specifically examining how data collection and processing methods affect analysis time. The study involved four participants performing various movements, with data collected and analyzed un...
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 57; pp. 51 - 53 |
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| Autores principales: | , , , |
| Formato: | abstract proceedings research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2025Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187999228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187999228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: 2025Supplement vid: 57 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 187999228 187999228 187999228 10.1249/01.mss.0001154932.10951.ab 187999228 ppf: 51 ppct: 2 formats: tig: atl: Maximizing Markerless Motion Capture Data Processing Speed: Enabling Efficient Reporting: 222...American College of Sports Medicine (ACSM) Annual Meeting, May 27-30, 2025, Atlanta, Georgia. aug: au: Edwards, Nathan A. Caccese, Jaclyn B. Hagen, Joshua A. Onate, James A. affil: The Ohio State University, Columbus, OH. sug: subj: Motion Capture Data Analysis Processing Speed Data Collection Congresses and Conferences Georgia Georgia ab: The article focuses on the development and application of markerless motion capture technologies in research, specifically examining how data collection and processing methods affect analysis time. The study involved four participants performing various movements, with data collected and analyzed under different conditions, including trial length, sample rate, and GPU configurations. Results indicated that longer trial lengths and higher sample rates significantly increased analysis time, while improved GPU quality reduced it. The findings suggest that optimizing these variables can enhance real-time data quality control and feedback for participants, with the fastest configuration allowing for a 10-second walking trial to be analyzed in 65 seconds. The research was partially funded by the NSF Industry/University Cooperative Research Program. pubtype: Academic Journal doctype: abstract proceedings research tables/charts Journal Article ougenre: Unknown language: English refInfo: holdings: @attributes: islocal: N |
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