{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T04:40:21Z","timestamp":1759812021217,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:00:00Z","timestamp":1759622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004329","name":"Slovenian Research Agency","doi-asserted-by":"publisher","award":["P2-0246"],"award-info":[{"award-number":["P2-0246"]}],"id":[{"id":"10.13039\/501100004329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"crossref","award":["2024ZKPYZN01"],"award-info":[{"award-number":["2024ZKPYZN01"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimal-data scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes.<\/jats:p>","DOI":"10.3390\/info16100865","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T15:05:06Z","timestamp":1759763106000},"page":"865","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Identification of Individuals in Sports: The Case of Darts"],"prefix":"10.3390","volume":"16","author":[{"given":"Val","family":"Vec","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka Cesta 25, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6234-8561","authenticated-orcid":false,"given":"Anton","family":"Kos","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka Cesta 25, 1000 Ljubljana, Slovenia"}]},{"given":"Rongfang","family":"Bie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, No.19, Xinjiekouwai St, Haidian District, Beijing 100875, China"}]},{"given":"Libin","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology\u2014Beijing, Ding No.11 Xueyuan Road, Haidian District, Beijing 100083, China"}]},{"given":"Haodi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4549-3502","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2968-8879","authenticated-orcid":false,"given":"Sa\u0161o","family":"Toma\u017ei\u010d","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka Cesta 25, 1000 Ljubljana, Slovenia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-327X","authenticated-orcid":false,"given":"Anton","family":"Umek","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, University of Ljubljana, Tr\u017ea\u0161ka Cesta 25, 1000 Ljubljana, Slovenia"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"ref_1","unstructured":"Sim, J. 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