{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T21:07:13Z","timestamp":1790888833775,"version":"4.1.0"},"reference-count":41,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Aging causes declines in cognitive and motor functions, often manifested in altered gait. Dual-task gait is a sensitive marker for early functional changes. This study investigated whether neural network (NN) models can differentiate gait patterns across age groups under dual-task conditions, and which joint features contribute most to age classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Thirty-six healthy were recruited for three groups: young (18\u201337 years), middle-aged (38\u201357), and older adults (\u226558), with 12 in each. Gait data were collected under dual-task conditions (reverse-counting while walking) using a Vicon motion capture system and force plates. The outcomes from gait analysis included spatiotemporal parameters, full-body joint angles, and lower limb joint force, moment and power. Eleven kinds of NN models with different combinations of gait outcomes were constructed using NNs to classify age groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The NN model combining all kinematics, kinetics, and spatiotemporal parameters achieved the highest classification accuracy (95.70\u2009\u00b1\u20093.15%), with F1 scores (the harmonic mean of precision and recall) above 0.948 across all age groups. Among single-feature models, the full-body kinematic model performed best (94.17\u2009\u00b1\u20093.47%), while the spatiotemporal model showed the lowest accuracy. The middle-aged group achieved the highest accuracy in the full-body kinematic model. Ankle-related features were a main factor in contributing to the NN models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>NN models with all-parameters can accurately classify age-related dual-task gait differences. By capturing gait changes, NN offers a quantitative tool for early detection of motor and cognitive decline, with potential applications in fall risk prediction, dementia screening, and targeted rehabilitation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329261474682","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T14:06:15Z","timestamp":1786457175000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A preliminary analysis of relationship between dual-task gait and age-related cognitive ability using neural network\u00a0models"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1548-3570","authenticated-orcid":false,"given":"Yuying","family":"Zhang","sequence":"first","affiliation":[{"name":"University Department of Orthopaedic and Trauma Surgery, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingying","family":"Xu","sequence":"additional","affiliation":[{"name":"University Department of Orthopaedic and Trauma Surgery, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afsoun","family":"Nicholas","sequence":"additional","affiliation":[{"name":"Susan Carnegie Centre, Stracathro Hospital, Dundee, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graham","family":"Arnold","sequence":"additional","affiliation":[{"name":"University Department of Orthopaedic and Trauma Surgery, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3987-9270","authenticated-orcid":false,"given":"Weijie","family":"Wang","sequence":"additional","affiliation":[{"name":"University Department of Orthopaedic and Trauma Surgery, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2022.956744"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.exger.2023.112102"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.humov.2016.03.010"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/gly148"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2022.09.087"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.952245"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.exger.2022.112022"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-180016"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1159\/000504340"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci14020148"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2024.3517635"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44339-024-00010-w"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3169284"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0244396"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2018.2809478"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-04209-1"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44163-024-00115-6"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3228073"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13186-3"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1186\/s41074-019-0054-2"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.105986"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109081"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13186-3"},{"key":"e_1_3_3_25_2","first-page":"165","article-title":"A three-dimensional real-time gait-based age detection system using machine learning","volume":"75","author":"Azhar M","year":"2023","unstructured":"Azhar M, et al. 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