{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:47:52Z","timestamp":1782452872020,"version":"3.54.5"},"reference-count":27,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology of Taiwan","doi-asserted-by":"crossref","award":["MOST 109-2221-E-011-130-MY2"],"award-info":[{"award-number":["MOST 109-2221-E-011-130-MY2"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Falls among older adults remain a major public health concern, yet scalable and interpretable sensing approaches for functional fall-risk stratification remain limited. This study presents a lightweight contactless framework for five-level Short Physical Performance Battery (SPPB)-aligned functional fall-risk stratification using monocular RGB video. A total of 688 community-dwelling older adults completed SPPB-aligned assessments, including balance, five-times sit-to-stand, and 3 m gait tasks. Because prospective fall-event outcomes were unavailable, supervised labels were constructed from a pre-specified SPPB-aligned functional risk index rather than observed future falls. BlazePose-based two-dimensional keypoints were extracted, normalized using pelvis-centered and height-scaled transformations, and represented as temporal skeletal trajectories. Biomechanical descriptors were fused with embeddings from the proposed Temporal Convolutional Artificial Intelligence Fall-Risk Network (TCAI-FallNet). Participant-level data partitioning was used to reduce data leakage. TCAI-FallNet achieved a macro-averaged area under the curve of 0.91 and an overall accuracy of 81.3%. The trained model had a footprint under 3 MB, and TCN inference latency was below 20 ms per sequence under workstation-based evaluation. These findings suggest that TCAI-FallNet may support contactless SPPB-aligned functional mobility risk stratification, while prospective fall-event validation remains necessary.<\/jats:p>","DOI":"10.3390\/s26123894","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:04:04Z","timestamp":1781874244000},"page":"3894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Temporal Convolutional Network for Contactless SPPB-Aligned Functional Fall-Risk Stratification in Older Adults Using Monocular RGB Video"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9708-4358","authenticated-orcid":false,"given":"Kai-Chih","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-7445","authenticated-orcid":false,"given":"Rong-Jong","family":"Wai","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hung-Yu","family":"Chang Chien","sequence":"additional","affiliation":[{"name":"Department of Health Promotion and Health Education, National Taiwan Normal University, Taipei 106, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021). Falls, World Health Organization. Technical Report."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1017\/S0714980800008576","article-title":"Development of a scale to identify the fall-prone patient","volume":"8","author":"Morse","year":"1989","journal-title":"Can. J. Aging"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E., and Sheikh, Y. (2017, January 21\u201326). Realtime multi-person 2D pose estimation using part affinity fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_5","unstructured":"Bazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., and Grundmann, M. (2020). BlazePose: On-device real-time body pose tracking. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"M85","DOI":"10.1093\/geronj\/49.2.M85","article-title":"A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission","volume":"49","author":"Guralnik","year":"1994","journal-title":"J. Gerontol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1053\/apmr.2001.24893","article-title":"Gait variability and fall risk in community-living older adults: A 1-year prospective study","volume":"82","author":"Hausdorff","year":"2001","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12370","DOI":"10.1109\/JSEN.2023.3267300","article-title":"Robust abnormal human-posture recognition using OpenPose and multiview cross-information","volume":"23","author":"Xu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"37124","DOI":"10.1109\/JSEN.2025.3602945","article-title":"FIBiLS: Fall detection of healthy elderly using IMU sensor and BiLSTM model","volume":"25","author":"Gaud","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JSEN.2021.3131037","article-title":"Wearable pre-impact fall detection system based on 3D accelerometer and subject\u2019s height","volume":"22","author":"Escriba","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2100609","DOI":"10.1109\/JTEHM.2020.2998326","article-title":"Measuring gait variables using computer vision to assess mobility and fall risk in older adults with dementia","volume":"8","author":"Ng","year":"2020","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106262","DOI":"10.1016\/j.clinbiomech.2024.106262","article-title":"Accurate fall risk classification in elderly using one gait cycle data and machine learning","volume":"115","author":"Nishiyama","year":"2024","journal-title":"Clin. Biomech."},{"key":"ref_13","unstructured":"Bai, S., Kolter, J.Z., and Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv."},{"key":"ref_14","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, N., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, S., Kong, L., Tong, Z., Yamaguchi, Y., and Nakamura, M. (2025). Towards sensor-based mobility assessment for older adults: A multimodal framework integrating PoseNet gait dynamics and InBody composition. Sensors, 25.","DOI":"10.3390\/s25185878"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Osness, E., Isley, S., Bertrand, J.K., Dennett, L., Bates, J., Van Decker, N., Stanhope, A., Omkar, A., Dolgoy, N., and Ezeugwu, V.E. (2025). Markerless motion capture parameters associated with fall risk or frailty: A scoping review. Sensors, 25.","DOI":"10.3390\/s25185741"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Han, X., Guffanti, D., and Brunete, A. (2025). A comprehensive review of vision-based sensor systems for human gait analysis. Sensors, 25.","DOI":"10.3390\/s25020498"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"18287","DOI":"10.1109\/JSEN.2021.3082982","article-title":"A novel environment-adaptive Timed Up and Go test system for fall risk assessment with wearable inertial sensors","volume":"21","author":"Diao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1111\/j.1532-5415.2008.01777.x","article-title":"Five times sit-to-stand test is a predictor of recurrent falls in healthy community-living subjects aged 65 and older","volume":"56","author":"Buatois","year":"2008","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Cao, J., Zhao, Q., Chen, M., Luo, J., Wang, H., Yu, L., Tsui, K.L., and Zhao, Y. (2024). Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: An investigation and interpretation of discriminatory parameters. BMC Geriatr., 24.","DOI":"10.1186\/s12877-024-04723-w"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"146404","DOI":"10.1109\/ACCESS.2021.3123297","article-title":"A feasible fall evaluation system via artificial intelligence gesture detection of gait and balance for sub-healthy community-dwelling older adults in Taiwan","volume":"9","author":"Lin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17064","DOI":"10.1109\/JSEN.2021.3081188","article-title":"Validation of a 3D markerless system for gait analysis based on OpenPose and two RGB webcams","volume":"21","author":"Taborri","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_23","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rehouma, H., and Boukadoum, M. (2025). Fall detection by deep learning-based bimodal movement and pose sensing with late fusion. Sensors, 25.","DOI":"10.3390\/s25196035"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e73290","DOI":"10.2196\/73290","article-title":"AI-assisted dynamic postural control screening to improve functional mobility in older adult populations: Quasi-experimental study","volume":"8","author":"Lin","year":"2025","journal-title":"JMIR Aging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4181","DOI":"10.1109\/JSEN.2023.3344605","article-title":"Artificial intelligence and IoT in elderly fall prevention: A review","volume":"24","author":"Mohan","year":"2024","journal-title":"IEEE Sens. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:16:18Z","timestamp":1782450978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,18]]},"references-count":27,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123894"],"URL":"https:\/\/doi.org\/10.3390\/s26123894","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,18]]}}}