{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T19:16:12Z","timestamp":1779390972594,"version":"3.53.1"},"reference-count":39,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Action quality assessment (AQA) tasks in computer vision evaluate action quality in videos, and they can be applied to sports for performance evaluation. A typical example of AQA is predicting the final score from a video that captures an entire figure skating program. However, no previous studies have predicted individual jump scores, which are of great interest to competitors because of the high weight of competition. Despite the presence of unnecessary information in figure skating videos, human specialists can focus and reduce information when they evaluate jumps. In this study, we clarified the eye movements of figure skating judges and skaters while evaluating jumps and proposed a prediction model for jump performance that utilized specialists\u2019 gaze location to reduce information. Kinematic features obtained from the tracking system were input into the model in addition to videos to improve accuracy. The results showed that skaters focused more on the face, whereas judges focused on the lower extremities. These gaze locations were applied to the model, which demonstrated the highest accuracy when utilizing both specialists\u2019 gaze locations. The model outperformed human predictions and the baseline model (RMSE:0.775), suggesting a combination of human specialist knowledge and machine capabilities could yield higher accuracy.<\/jats:p>","DOI":"10.3390\/s23229282","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T11:31:36Z","timestamp":1700479896000},"page":"9282","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Action Quality Assessment Model Using Specialists\u2019 Gaze Location and Kinematics Data\u2014Focusing on Evaluating Figure Skating Jumps"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0322-9524","authenticated-orcid":false,"given":"Seiji","family":"Hirosawa","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan"},{"name":"Faculty of Sport and Health Sciences, Toin University of Yokohama, Yokohama 225-8503, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takaaki","family":"Kato","sequence":"additional","affiliation":[{"name":"Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0882, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takayoshi","family":"Yamashita","sequence":"additional","affiliation":[{"name":"College of Engineering, Chubu University, Kasugai 487-8501, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7361-0027","authenticated-orcid":false,"given":"Yoshimitsu","family":"Aoki","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lei, Q., Du, J.X., Zhang, H.B., Ye, S., and Chen, D.S. 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