{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:55:45Z","timestamp":1781592945657,"version":"3.54.5"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Hebei Provincial Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Committee","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.engappai.2026.115136","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T02:26:16Z","timestamp":1779416776000},"page":"115136","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Home-based sarcopenia diagnosis via multimodal gait analysis and personalized large language model intervention"],"prefix":"10.1016","volume":"179","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2368-7397","authenticated-orcid":false,"given":"Yinghao","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanchao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuming","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuliang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115136_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.metabol.2023.155711","article-title":"Pathophysiology of sarcopenia: Genetic factors and their interplay with environmental factors","volume":"149","author":"Aslam","year":"2023","journal-title":"Metabolism"},{"key":"10.1016\/j.engappai.2026.115136_b2","series-title":"How attentive are graph attention networks?","author":"Brody","year":"2021"},{"key":"10.1016\/j.engappai.2026.115136_b3","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.gaitpost.2017.06.019","article-title":"A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms","volume":"57","author":"Caldas","year":"2017","journal-title":"Gait Posture"},{"key":"10.1016\/j.engappai.2026.115136_b4","series-title":"Huatuogpt-o1, towards medical complex reasoning with llms","author":"Chen","year":"2024"},{"key":"10.1016\/j.engappai.2026.115136_b5","series-title":"Shizhengpt: Towards multimodal llms for traditional chinese medicine","author":"Chen","year":"2025"},{"issue":"3","key":"10.1016\/j.engappai.2026.115136_b6","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.jamda.2019.12.012","article-title":"Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment","volume":"21","author":"Chen","year":"2020","journal-title":"J. Am. Med. Dir. Assoc."},{"issue":"10191","key":"10.1016\/j.engappai.2026.115136_b7","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.1016\/S0140-6736(19)31138-9","article-title":"Sarcopenia","volume":"393","author":"Cruz-Jentoft","year":"2019","journal-title":"Lancet"},{"key":"10.1016\/j.engappai.2026.115136_b8","doi-asserted-by":"crossref","unstructured":"Gu, Jihao, Li, Kun, Wang, Fei, Wei, Yanyan, Wu, Zhiliang, Fan, Hehe, Wang, Meng, 2025. Motion matters: Motion-guided modulation network for skeleton-based micro-action recognition. In: Proceedings of the 33rd ACM International Conference on Multimedia. pp. 5461\u20135470.","DOI":"10.1145\/3746027.3754722"},{"issue":"7","key":"10.1016\/j.engappai.2026.115136_b9","doi-asserted-by":"crossref","first-page":"6238","DOI":"10.1109\/TCSVT.2024.3358415","article-title":"Benchmarking micro-action recognition: Dataset, methods, and applications","volume":"34","author":"Guo","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.engappai.2026.115136_b10","doi-asserted-by":"crossref","unstructured":"He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, Sun, Jian, 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.engappai.2026.115136_b11","series-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"10.1016\/j.engappai.2026.115136_b12","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.1335251","article-title":"Effective evaluation of hgcnmlp method for markerless 3D pose estimation of musculoskeletal diseases patients based on smartphone monocular video","volume":"11","author":"Hu","year":"2024","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.engappai.2026.115136_b13","series-title":"Bidirectional LSTM-CRF models for sequence tagging","author":"Huang","year":"2015"},{"key":"10.1016\/j.engappai.2026.115136_b14","series-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size","author":"Iandola","year":"2016"},{"key":"10.1016\/j.engappai.2026.115136_b15","series-title":"2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"1","article-title":"Deep learning-based sarcopenia classification through gait video analysis with a single mobile camera","author":"Jamsrandorj","year":"2025"},{"key":"10.1016\/j.engappai.2026.115136_b16","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.gaitpost.2025.01.011","article-title":"Quantitative analysis of gait dysfunction in sarcopenia patients: Based on spatiotemporal parameters and kinematic performance","volume":"118","author":"Jiang","year":"2025","journal-title":"Gait & Posture"},{"issue":"1","key":"10.1016\/j.engappai.2026.115136_b17","doi-asserted-by":"crossref","first-page":"10915","DOI":"10.1038\/s41598-025-95101-y","article-title":"Gait kinematic and kinetic characteristics among older adults with varying degrees of frailty: a cross-sectional study","volume":"15","author":"Jiang","year":"2025","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.engappai.2026.115136_b18","doi-asserted-by":"crossref","first-page":"421","DOI":"10.14283\/jfa.2024.64","article-title":"Evaluation of handgrip strength asymmetry to assess sarcopenia in older patients with chronic low back pain: A retrospective cross-sectional study","volume":"13","author":"Kim","year":"2024","journal-title":"J. Frailty Aging"},{"key":"10.1016\/j.engappai.2026.115136_b19","series-title":"Health-llm: Large language models for health prediction via wearable sensor data","author":"Kim","year":"2024"},{"key":"10.1016\/j.engappai.2026.115136_b20","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.engappai.2026.115136_b21","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115136_b22","series-title":"European Conference on Computer Vision","first-page":"47","article-title":"Temporal convolutional networks: A unified approach to action segmentation","author":"Lea","year":"2016"},{"key":"10.1016\/j.engappai.2026.115136_b23","doi-asserted-by":"crossref","unstructured":"Li, Zechen, Deldari, Shohreh, Chen, Linyao, Xue, Hao, Salim, Flora D, 2025a. Sensorllm: Aligning large language models with motion sensors for human activity recognition. In: Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. pp. 354\u2013379.","DOI":"10.18653\/v1\/2025.emnlp-main.19"},{"key":"10.1016\/j.engappai.2026.115136_b24","first-page":"4815","article-title":"Prototypical calibrating ambiguous samples for micro-action recognition","volume":"vol. 39","author":"Li","year":"2025"},{"key":"10.1016\/j.engappai.2026.115136_b25","doi-asserted-by":"crossref","unstructured":"Li, Kun, Guo, Dan, Chen, Guoliang, Liu, Feiyang, Wang, Meng, 2023. Data augmentation for human behavior analysis in multi-person conversations. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 9516\u20139520.","DOI":"10.1145\/3581783.3612856"},{"key":"10.1016\/j.engappai.2026.115136_b26","series-title":"2019 International Radar Conference","first-page":"1","article-title":"Bigru network for human activity recognition in high resolution range profile","author":"Li","year":"2019"},{"key":"10.1016\/j.engappai.2026.115136_b27","doi-asserted-by":"crossref","first-page":"3844","DOI":"10.1109\/TMM.2025.3535385","article-title":"Repetitive action counting with hybrid temporal relation modeling","volume":"27","author":"Li","year":"2025","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.engappai.2026.115136_b28","series-title":"International Conference on Pattern Recognition","first-page":"318","article-title":"3D pose-based evaluation of the risk of sarcopenia","author":"Liao","year":"2024"},{"issue":"13","key":"10.1016\/j.engappai.2026.115136_b29","doi-asserted-by":"crossref","first-page":"15330","DOI":"10.1109\/JSEN.2021.3073569","article-title":"Assessment of shoulder range of motion using a wearable inertial sensor network","volume":"21","author":"Lin","year":"2021","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.engappai.2026.115136_b30","doi-asserted-by":"crossref","first-page":"107293","DOI":"10.1109\/ACCESS.2022.3209825","article-title":"A review on machine learning styles in computer vision\u2014techniques and future directions","volume":"10","author":"Mahadevkar","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.115136_b31","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Gonz\u00e1lez, Angel, Villamizar, Michael, Odobez, Jean-Marc, 2021. Pose transformers (potr): Human motion prediction with non-autoregressive transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 2276\u20132284.","DOI":"10.1109\/ICCVW54120.2021.00257"},{"issue":"1","key":"10.1016\/j.engappai.2026.115136_b32","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1186\/s12877-024-05475-3","article-title":"Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review","volume":"24","author":"Perez-Lasierra","year":"2024","journal-title":"BMC Geriatr."},{"key":"10.1016\/j.engappai.2026.115136_b33","series-title":"Gait analysis: normal and pathological function","author":"Perry","year":"2024"},{"issue":"3","key":"10.1016\/j.engappai.2026.115136_b34","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1109\/TMRB.2021.3091526","article-title":"Continuous identification of freezing of gait in parkinson\u2019s patients using artificial neural networks and instrumented shoes","volume":"3","author":"Prado","year":"2021","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"issue":"10","key":"10.1016\/j.engappai.2026.115136_b35","doi-asserted-by":"crossref","first-page":"afac220","DOI":"10.1093\/ageing\/afac220","article-title":"Sarcopenia definition, diagnosis and treatment: consensus is growing","volume":"51","author":"Sayer","year":"2022","journal-title":"Age Ageing"},{"key":"10.1016\/j.engappai.2026.115136_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.106988","article-title":"Gaitnet: An end-to-end network for gait based human identification","volume":"96","author":"Song","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.115136_b37","doi-asserted-by":"crossref","unstructured":"Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, Rabinovich, Andrew, 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.engappai.2026.115136_b38","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.gaitpost.2021.10.028","article-title":"Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities","volume":"91","author":"Tang","year":"2022","journal-title":"Gait Posture"},{"issue":"8","key":"10.1016\/j.engappai.2026.115136_b39","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nature Med."},{"key":"10.1016\/j.engappai.2026.115136_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110108","article-title":"From silos to synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare","volume":"191","author":"Tuly","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.engappai.2026.115136_b41","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115136_b42","article-title":"Multimodal fusion-based human action recognition using wi-fi CSI and smartwatch sensors","author":"Wang","year":"2026","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.engappai.2026.115136_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.yexmp.2025.104992","article-title":"Research progress of sarcopenia: Diagnostic advancements, molecular mechanisms, and therapeutic strategies","volume":"143","author":"Wang","year":"2025","journal-title":"Exp. Mol. Pathol."},{"key":"10.1016\/j.engappai.2026.115136_b44","article-title":"Dual-branch CNN-mlp-dropout network for multi-class scene recognition: Fusing multi-sensor time-domain inputs and time-frequency representations","author":"Wang","year":"2026","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"10.1016\/j.engappai.2026.115136_b45","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/s43020-024-00146-5","article-title":"Multi-frequency smartphone positioning performance evaluation: insights into A-GNSS PPP-b2b services and beyond","volume":"5","author":"Wang","year":"2024","journal-title":"Satell. Navig."},{"key":"10.1016\/j.engappai.2026.115136_b46","article-title":"Multimodal sensor fusion-based lightweight pedestrian wearable system for continuous health monitoring and location tracking","author":"Wang","year":"2025","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.engappai.2026.115136_b47","article-title":"Multi-risk-level sarcopenia-prone screening via machine learning classification of sit-to-stand motion metrics from wearable sensors","author":"Wang","year":"2025","journal-title":"Adv. Intell. Syst."},{"key":"10.1016\/j.engappai.2026.115136_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.arr.2020.101200","article-title":"Sarcopenia\u2013molecular mechanisms and open questions","volume":"65","author":"Wiedmer","year":"2021","journal-title":"Ageing Res. Rev."},{"key":"10.1016\/j.engappai.2026.115136_b49","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"vol. 32","author":"Yan","year":"2018"},{"key":"10.1016\/j.engappai.2026.115136_b50","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"vol. 32","author":"Yan","year":"2018"},{"key":"10.1016\/j.engappai.2026.115136_b51","doi-asserted-by":"crossref","unstructured":"Yang, Yiding, Ren, Zhou, Li, Haoxiang, Zhou, Chunluan, Wang, Xinchao, Hua, Gang, 2021. Learning dynamics via graph neural networks for human pose estimation and tracking. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 8074\u20138084.","DOI":"10.1109\/CVPR46437.2021.00798"},{"key":"10.1016\/j.engappai.2026.115136_b52","article-title":"Epidemiology of sarcopenia: Prevalence, risk factors, and consequences","volume":"144","author":"Yuan","year":"2023","journal-title":"Metabolism"},{"issue":"2","key":"10.1016\/j.engappai.2026.115136_b53","article-title":"VisMocap: Interactive visualization and analysis for multi-source motion capture data.","volume":"9","author":"Zhan","year":"2025","journal-title":"Vis. Inform."},{"key":"10.1016\/j.engappai.2026.115136_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103422","article-title":"A comprehensive review of sEMG-IMU sensor fusion for upper limb movements pattern recognition","volume":"125","author":"Zhang","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.115136_b55","article-title":"Assessing sarcopenia-prone risk through daily activity of gait with AI-powered wearable IoT sensors","author":"Zhang","year":"2025","journal-title":"IEEE Internet Things J."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626014193?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626014193?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T06:42:56Z","timestamp":1781592176000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626014193"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":55,"alternative-id":["S0952197626014193"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115136","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Home-based sarcopenia diagnosis via multimodal gait analysis and personalized large language model intervention","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115136","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115136"}}