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C. Wong Magna Fund in Ningbo University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100020738","id-type":"DOI","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/03et85d35","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: Changes in midsole hardness may affect lower-limb impact loading during forefoot strike (FFS) running in children, yet the biomechanical basis for discriminating elevated VALR-related loading remains unclear. (2) Methods: Fourteen school-aged boys performed FFS running tests in experimental shoes with four midsole hardness levels (37, 42, 47, and 52 Shore C). Lower-limb kinematics and surface electromyography (sEMG) data were collected during the dominant leg stance phase. After preprocessing, VALR was calculated from 336 valid trials, and 28 stance-phase biomechanical features were extracted, yielding a final machine-learning dataset of 324 trials after excluding incomplete feature data. VALR was used to compare loading changes and define trial-level elevated-loading labels based on the median VALR value. Classification models were evaluated under participant-level GroupKFold validation, and XGBoost was retained for exploratory SHAP analysis. (3) Results: VALR showed an upward trend with increasing hardness, but no statistically supported change point was identified. XGBoost achieved an accuracy of 75.93%, precision of 74.14%, recall of 79.63%, F1-value of 0.768, and pooled out-of-fold AUC of 0.738. SHAP analysis indicated that distal and non-sagittal kinematic features contributed most to model classification. (4) Conclusions: Elevated VALR-related loading during children\u2019s FFS running may be characterized by a multi-feature model-based pattern rather than a fixed midsole hardness threshold.<\/jats:p>","DOI":"10.3390\/s26123942","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3942","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Machine Learning Using Sensor-Derived Biomechanical Features to Classify Elevated VALR-Related Loading Across Midsole Hardness Conditions in School-Aged Boys"],"prefix":"10.3390","volume":"26","author":[{"given":"Yiyao","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, 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 T2N 1N4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2206-2750","authenticated-orcid":false,"given":"Fengping","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongxu","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianqi","family":"Pan","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1962-4147","authenticated-orcid":false,"given":"Yucheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, 18011 Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lidong","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Material Science and Technology, Audi Hungaria Faculty of Automotive Engineering, Sz\u00e9chenyi Istv\u00e1n University, 9026 Gy\u0151r, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuiyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaolong","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2187-9440","authenticated-orcid":false,"given":"Yaodong","family":"Gu","sequence":"additional","affiliation":[{"name":"Faculty of Sports Science, Ningbo University, Ningbo 315211, China"},{"name":"Department of Kinesiology, Hungarian University of Sports Science, 1123 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6271898","DOI":"10.1155\/2018\/6271898","article-title":"A Review of Pediatric Lower Extremity Data for Pedestrian Numerical Modeling: Injury Epidemiology, Anatomy, Anthropometry, Structural, and Mechanical Properties","volume":"2018","author":"Meng","year":"2018","journal-title":"Appl. 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