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Traditional methods for obtaining this metric rely on laboratory-based equipment, limiting practical application. To address this limitation, this study proposes and validates a novel method for predicting the knee resultant joint moment method using a Graph Neural Network and Gated Recurrent Unit (GNN-GRU) model. An independent GRU model was used as a baseline for comparison. Biomechanical data were collected from 30 male tennis players (age: 20.30\u2009\u00b1\u20091.66\u2009years, height: 176.60\u2009\u00b1\u20092.74\u2009cm, weight: 70.80\u2009\u00b1\u20093.89\u2009kg, BMI: 22.71\u2009\u00b1\u20091.38\u2009kg\/m\n                    <jats:sup>2<\/jats:sup>\n                    , training experience: 9.20\u2009\u00b1\u20092.81\u2009years) during the performance of the tennis serve. Sagittal plane joint angles of both lower limbs were used as model inputs to predict the resultant joint moment of the supporting leg. A paired-sample t-test compared predicted and actual values. Layer-wise Relevance Propagation (LRP) was applied to quantify the contribution of individual joint angles. The GNN-GRU model demonstrated significantly better prediction performance than the standalone GRU model (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.05). No significant differences were observed between predicted and actual values (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&gt;\u20090.05). LRP results showed knee contribution close to 1 during the Preparation Phase (PP). In the Flight Phase (FP), ankle and hip contributions increased significantly, both approaching 1. During the Landing Phase (LP), the knee joint maintained a contribution above 0.4. This study supports the identification of potentially high-risk movements in real-world tennis training and competition and provides a reference for the early detection of knee joint injuries.\n                  <\/jats:p>","DOI":"10.1177\/09544119251361341","type":"journal-article","created":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T09:11:49Z","timestamp":1759569109000},"page":"899-908","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation"],"prefix":"10.1177","volume":"239","author":[{"given":"Jianqi","family":"Pan","sequence":"first","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4345-8201","authenticated-orcid":false,"given":"Zixiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengping","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien S","family":"Baker","sequence":"additional","affiliation":[{"name":"Department of Sport and Physical Education, Hong Kong Baptist University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaodong","family":"Gu","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1021","article-title":"Lower-limb activity during the power serve in tennis: effects of performance level","volume":"37","author":"Girard O","year":"2005","unstructured":"Girard O, Micallef JP, Millet GP. 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