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Generalized and efficient skill assessment from IMU data with applications in gymnastics and medical training

Khan, A., Mellor, S., King, R., Janko, B., Harwin, W. ORCID:, Sherratt, R. S. ORCID:, Craddock, I. and Plotz, T. (2020) Generalized and efficient skill assessment from IMU data with applications in gymnastics and medical training. ACM Transactions on Computing for Healthcare, 2 (1). pp. 1-21. ISSN 2691-1957

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To link to this item DOI: 10.1145/3422168


Human activity recognition is progressing from automatically determining what a person is doing and when, to additionally analyzing the quality of these activities—typically referred to as skill assessment. In this chapter, we propose a new framework for skill assessment that generalizes across application domains and can be deployed for near-real-time applications. It is based on the notion of repeatability of activities defining skill. The analysis is based on two subsequent classification steps that analyze (1) movements or activities and (2) their qualities, that is, the actual skills of a human performing them. The first classifier is trained in either a supervised or unsupervised manner and provides confidence scores, which are then used for assessing skills. We evaluate the proposed method in two scenarios: gymnastics and surgical skill training of medical students. We demonstrate both the overall effectiveness and efficiency of the generalized assessment method, especially compared to previous work.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences > Biomedical Sciences
Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:99285


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