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Explaining rifle shooting factors through multi-sensor body tracking
Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
Jönköping University, School of Engineering, JTH, Department of Computer Science and Informatics.
Jönköping University, School of Engineering, JTH, Department of Computing.ORCID iD: 0000-0002-2161-7371
Saab AB, Training & Simulat, Huskvarna, Sweden..
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2023 (English)In: Intelligent Data Analysis, ISSN 1088-467X, E-ISSN 1571-4128, Vol. 27, no 2, p. 535-554Article in journal (Refereed) Published
Abstract [en]

There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but have mostly focused on single aspects of postural control. This study has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. A data collection was performed with 13 human participants carrying out live rifle shooting scenarios while being recorded with multiple body tracking sensors. A pre-processing pipeline produced a novel skeleton sequence representation, which was used to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (80%). Shooting performance could be detected to an extent by separating participants using their strong and weak shooting hand. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this study have laid the groundwork for future research in the sports shooting domain.

Place, publisher, year, edition, pages
IOS Press, 2023. Vol. 27, no 2, p. 535-554
Keywords [en]
Machine learning, explainable AI, transformers, skeleton graphs, rifle shooting
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hj:diva-60346DOI: 10.3233/IDA-216457ISI: 000970251100014Scopus ID: 2-s2.0-85161187936Local ID: GOA;;880113OAI: oai:DiVA.org:hj-60346DiVA, id: diva2:1756846
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Knowledge Foundation, 20180191Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-06-26Bibliographically approved

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Westphal, Florian

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