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A Drone-mounted Depth Camera-based Motion Capture System for Sports Performance Analysis
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0002-8359-5745
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.ORCID iD: 0000-0003-1668-9896
Dalarna University.ORCID iD: 0000-0001-8385-3209
2023 (English)In: Artificial Intelligence in HCI: Proceedings 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023 / [ed] Degen, H., Ntoa, S., Springer Nature , 2023, p. 489-503Conference paper, Published paper (Refereed)
Abstract [en]

Video is the most used tool for sport performance analysis as it provides a common reference point for the coach and the athlete. The problem with video is that it is a subjective tool. To overcome this, motion capture systems can used to get an objective 3D model of a per- son’s posture and motion, but only in laboratory settings. Unfortunately, many activities, such as most outdoor sports, cannot be captured in a lab without compromising the activity. In this paper, we propose to use an aerial drone system equipped with depth cameras, AI-based marker- less motion capture software to perform automatic skeleton tracking and real-time sports performance analysis of athletes. We experiment with off-the-shelf drone systems, miniaturized depth cameras, and commer- cially available skeleton tracking software to build a system for analyzing sports-related performance of athletes in their real settings. To make this a fully working system, we have conducted a few initial experiments and identified many issues that still needs to be addressed.

Place, publisher, year, edition, pages
Springer Nature , 2023. p. 489-503
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14051
Keywords [en]
Quadcopter, Drone, Motion capture, Skeleton tracking, Depth camera, Sports performance analysis
National Category
Computer Vision and Robotics (Autonomous Systems) Embedded Systems Sport and Fitness Sciences Human Computer Interaction
Research subject
Human-computer Interaction; Computer Science; Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-334230DOI: 10.1007/978-3-031-35894-4_36OAI: oai:DiVA.org:kth-334230DiVA, id: diva2:1789002
Conference
Artificial Intelligence in HCI 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023
Projects
My Digital Drone Twin
Note

Part of proceedings ISBN 978-3-031-35893-7  978-3-031-35894-4

QC 20230818

Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-08-18Bibliographically approved

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Jacobsson, MartinWillén, Jonas

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CiteExportLink to record
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