{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T19:54:31Z","timestamp":1771271671416,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":47,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4700200"],"award-info":[{"award-number":["2022YFB4700200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373202"],"award-info":[{"award-number":["62373202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019065","name":"Tianjin Municipal Science and Technology Program","doi-asserted-by":"publisher","award":["23JCYBJC01200"],"award-info":[{"award-number":["23JCYBJC01200"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-02150-8","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:36:14Z","timestamp":1765618574000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deep learning-enabled accurate assessment of gait impairments in Parkinson\u2019s disease using smartphone videos"],"prefix":"10.1038","volume":"9","author":[{"given":"Jianda","family":"Han","sequence":"first","affiliation":[]},{"given":"Zhihua","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Jialing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shaohua","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fahd","family":"Baig","sequence":"additional","affiliation":[]},{"given":"Peipei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ravi","family":"Vaidyanathan","sequence":"additional","affiliation":[]},{"given":"Francesca","family":"Morgante","sequence":"additional","affiliation":[]},{"given":"Weiguang","family":"Huo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"2150_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41572-021-00280-3","volume":"7","author":"D Aarsland","year":"2021","unstructured":"Aarsland, D. et al. Parkinson disease-associated cognitive impairment. Nat. Rev. Dis. Prim. 7, 47 (2021).","journal-title":"Nat. Rev. Dis. Prim."},{"key":"2150_CR2","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/S1474-4422(19)30044-4","volume":"18","author":"A Mirelman","year":"2019","unstructured":"Mirelman, A. et al. Gait impairments in Parkinson\u2019s disease. Lancet Neurol. 18, 697\u2013708 (2019).","journal-title":"Lancet Neurol."},{"key":"2150_CR3","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1001\/jama.2019.22360","volume":"323","author":"MJ Armstrong","year":"2020","unstructured":"Armstrong, M. J. & Okun, M. S. Diagnosis and treatment of Parkinson disease: a review. JAMA 323, 548\u2013560 (2020).","journal-title":"JAMA"},{"key":"2150_CR4","doi-asserted-by":"publisher","first-page":"685","DOI":"10.3390\/brainsci11060685","volume":"11","author":"R Forte","year":"2021","unstructured":"Forte, R., Tocci, N. & De Vito, G. The impact of exercise intervention with rhythmic auditory stimulation to improve gait and mobility in Parkinson Disease: an umbrella review. Brain Sci. 11, 685 (2021).","journal-title":"Brain Sci."},{"key":"2150_CR5","doi-asserted-by":"publisher","first-page":"23","DOI":"10.3389\/fnagi.2019.00023","volume":"11","author":"Z Mao","year":"2019","unstructured":"Mao, Z. et al. Comparison of efficacy of deep brain stimulation of different targets in Parkinson\u2019s disease: a network meta-analysis. Front. Aging Neurosci. 11, 23 (2019).","journal-title":"Front. Aging Neurosci."},{"key":"2150_CR6","doi-asserted-by":"publisher","first-page":"2444","DOI":"10.1109\/JBHI.2019.2952618","volume":"24","author":"F Demrozi","year":"2019","unstructured":"Demrozi, F., Bacchin, R., Tamburin, S., Cristani, M. & Pravadelli, G. Toward a wearable system for predicting freezing of gait in people affected by Parkinson\u2019s disease. IEEE J. Biomed. Health Inf. 24, 2444\u20132451 (2019).","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"2150_CR7","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1109\/JBHI.2022.3144917","volume":"26","author":"A Sabo","year":"2022","unstructured":"Sabo, A., Mehdizadeh, S., Iaboni, A. & Taati, B. Estimating parkinsonism severity in natural gait videos of older adults with dementia. IEEE J. Biomed. Health Inf. 26, 2288\u20132298 (2022).","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"2150_CR8","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1002\/mds.26572","volume":"31","author":"BR Bloem","year":"2016","unstructured":"Bloem, B. R. et al. M easurement instruments to assess posture, gait, and balance in p arkinson\u2019s disease: Critique and recommendations. Mov. Disord. 31, 1342\u20131355 (2016).","journal-title":"Mov. Disord."},{"key":"2150_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.parkreldis.2017.12.017","volume":"48","author":"EI Baron","year":"2018","unstructured":"Baron, E. I., Miller Koop, M., Streicher, M. C., Rosenfeldt, A. B. & Alberts, J. L. Altered kinematics of arm swing in Parkinson\u2019s disease patients indicates declines in gait under dual-task conditions. Parkinsonism Relat. Disord. 48, 61\u201367 (2018).","journal-title":"Parkinsonism Relat. Disord."},{"key":"2150_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neuroscience.2014.01.041","volume":"265","author":"L Rochester","year":"2014","unstructured":"Rochester, L., Galna, B., Lord, S. & Burn, D. The nature of dual-task interference during gait in incident Parkinson\u2019s disease. Neurosci 265, 83\u201394 (2014).","journal-title":"Neurosci"},{"key":"2150_CR11","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1002\/mds.26269","volume":"30","author":"C Curtze","year":"2015","unstructured":"Curtze, C., Nutt, J. G., Carlson-Kuhta, P., Mancini, M. & Horak, F. B. Levodopa is a double-edged sword for balance and gait in people with Parkinson\u2019s disease. Mov. Disord. 30, 1361\u20131370 (2015).","journal-title":"Mov. Disord."},{"key":"2150_CR12","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1002\/mds.27299","volume":"33","author":"O Samotus","year":"2018","unstructured":"Samotus, O., Parrent, A. & Jog, M. Spinal cord stimulation therapy for gait dysfunction in advanced Parkinson\u2019s disease patients. Mov. Disord. 33, 783\u2013792 (2018).","journal-title":"Mov. Disord."},{"key":"2150_CR13","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1177\/0269215515588836","volume":"30","author":"C Schlick","year":"2016","unstructured":"Schlick, C. et al. Visual cues combined with treadmill training to improve gait performance in Parkinson\u2019s disease: a pilot randomized controlled trial. Clin. Rehabil. 30, 463\u2013471 (2016).","journal-title":"Clin. Rehabil."},{"key":"2150_CR14","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1590\/S0004-282X2010000100018","volume":"68","author":"RdM Roiz","year":"2010","unstructured":"Roiz, Rd. M. et al. Gait analysis comparing Parkinson\u2019s disease with healthy elderly subjects. Arq. Neuro Psiquiatr. 68, 81\u201386 (2010).","journal-title":"Arq. Neuro Psiquiatr."},{"key":"2150_CR15","doi-asserted-by":"crossref","unstructured":"Pachoulakis, I. & Kourmoulis, K. Building a gait analysis framework for Parkinson\u2019s disease patients: motion capture and skeleton 3d representation. In 2014 International Conference on Telecommunications and Multimedia (TEMU), 220\u2013225 (IEEE, 2014).","DOI":"10.1109\/TEMU.2014.6917764"},{"key":"2150_CR16","doi-asserted-by":"publisher","first-page":"28","DOI":"10.11138\/FNeur\/2017.32.1.028","volume":"32","author":"M Pistacchi","year":"2017","unstructured":"Pistacchi, M. et al. Gait analysis and clinical correlations in early Parkinson\u2019s disease. Funct. Neurol. 32, 28\u201334 (2017).","journal-title":"Funct. Neurol."},{"key":"2150_CR17","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41746-022-00607-8","volume":"5","author":"M Burq","year":"2022","unstructured":"Burq, M. et al. Virtual exam for Parkinson\u2019s disease enables frequent and reliable remote measurements of motor function. NPJ Digit. Med. 5, 65 (2022).","journal-title":"NPJ Digit. Med."},{"key":"2150_CR18","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s41746-019-0169-y","volume":"2","author":"JJ Elm","year":"2019","unstructured":"Elm, J. J. et al. Feasibility and utility of a clinician dashboard from wearable and mobile application Parkinson\u2019s disease data. NPJ Digit. Med. 2, 95 (2019).","journal-title":"NPJ Digit. Med."},{"key":"2150_CR19","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41746-023-00905-9","volume":"6","author":"MS Islam","year":"2023","unstructured":"Islam, M. S. et al. Using ai to measure Parkinson\u2019s disease severity at home. NPJ Digit. Med. 6, 156 (2023).","journal-title":"NPJ Digit. Med."},{"key":"2150_CR20","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1038\/s41591-022-01932-x","volume":"28","author":"Y Yang","year":"2022","unstructured":"Yang, Y. et al. Artificial intelligence-enabled detection and assessment of Parkinson\u2019s disease using nocturnal breathing signals. Nat. Med. 28, 2207\u20132215 (2022).","journal-title":"Nat. Med."},{"key":"2150_CR21","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1001\/jamaneurol.2018.0809","volume":"75","author":"A Zhan","year":"2018","unstructured":"Zhan, A. et al. Using smartphones and machine learning to quantify Parkinson Disease severity: the mobile Parkinson Disease score. JAMA Neurol. 75, 876\u2013880 (2018).","journal-title":"JAMA Neurol."},{"key":"2150_CR22","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41531-023-00454-8","volume":"9","author":"G Morinan","year":"2023","unstructured":"Morinan, G. et al. Computer vision quantification of whole-body parkinsonian bradykinesia using a large multi-site population. NPJ Parkinsons Dis. 9, 10 (2023).","journal-title":"NPJ Parkinsons Dis."},{"key":"2150_CR23","doi-asserted-by":"publisher","first-page":"2048","DOI":"10.1038\/s41591-023-02440-2","volume":"29","author":"A-K Schalkamp","year":"2023","unstructured":"Schalkamp, A.-K., Peall, K. J., Harrison, N. A. & Sandor, C. Wearable movement-tracking data identify Parkinson\u2019s disease years before clinical diagnosis. Nat. Med. 29, 2048\u20132056 (2023).","journal-title":"Nat. Med."},{"key":"2150_CR24","doi-asserted-by":"publisher","first-page":"eabd7865","DOI":"10.1126\/scitranslmed.abd7865","volume":"13","author":"R Powers","year":"2021","unstructured":"Powers, R. et al. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson\u2019s disease. Sci. Transl. Med. 13, eabd7865 (2021).","journal-title":"Sci. Transl. Med."},{"key":"2150_CR25","doi-asserted-by":"publisher","first-page":"eadc9669","DOI":"10.1126\/scitranslmed.adc9669","volume":"14","author":"Y Liu","year":"2022","unstructured":"Liu, Y. et al. Monitoring gait at home with radio waves in Parkinson\u2019s disease: a marker of severity, progression, and medication response. Sci. Transl. Med. 14, eadc9669 (2022).","journal-title":"Sci. Transl. Med."},{"key":"2150_CR26","doi-asserted-by":"crossref","unstructured":"Endo, M. et al. Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases. Nat. Mach. Intell. 6, 1034\u20131045 (2024).","DOI":"10.1038\/s42256-024-00882-y"},{"key":"2150_CR27","doi-asserted-by":"publisher","first-page":"2912","DOI":"10.1109\/TNSRE.2023.3291359","volume":"31","author":"Q Zeng","year":"2023","unstructured":"Zeng, Q. et al. Video-based quantification of gait impairments in Parkinson\u2019s disease using skeleton-silhouette fusion convolution network. IEEE Trans. Neural Syst. Rehabilit. Eng. 31, 2912\u20132922 (2023).","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"2150_CR28","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1109\/TMM.2021.3068609","volume":"24","author":"R Guo","year":"2021","unstructured":"Guo, R., Shao, X., Zhang, C. & Qian, X. Multi-scale sparse graph convolutional network for the assessment of parkinsonian gait. IEEE Trans. Multimed. 24, 1583\u20131594 (2021).","journal-title":"IEEE Trans. Multimed."},{"key":"2150_CR29","doi-asserted-by":"publisher","first-page":"102179","DOI":"10.1016\/j.media.2021.102179","volume":"73","author":"M Lu","year":"2021","unstructured":"Lu, M. et al. Quantifying Parkinson\u2019s disease motor severity under uncertainty using MDS-UPDRS videos. Med. Image Anal. 73, 102179 (2021).","journal-title":"Med. Image Anal."},{"key":"2150_CR30","doi-asserted-by":"crossref","unstructured":"Kim, K., Lyu, S., Mantri, S. & Dunn, T. W. Tulip: Multi-camera 3d precision assessment of Parkinson\u2019s disease. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 22551\u201322562 (IEEE, 2024).","DOI":"10.1109\/CVPR52733.2024.02128"},{"key":"2150_CR31","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1109\/TNSRE.2024.3352004","volume":"32","author":"H Tian","year":"2024","unstructured":"Tian, H. et al. Cross-spatiotemporal graph convolution networks for skeleton-based parkinsonian gait MDS-UPDRS score estimation. IEEE Trans. Neural Syst. Rehabilit. Eng. 32, 412\u2013421 (2024).","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"2150_CR32","doi-asserted-by":"crossref","unstructured":"Krajushkina, A. et al. Gait analysis based approach for Parkinson\u2019s disease modeling with decision tree classifiers. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3720\u20133725 (IEEE, 2018).","DOI":"10.1109\/SMC.2018.00630"},{"key":"2150_CR33","first-page":"1","volume":"18","author":"DM Peters","year":"2021","unstructured":"Peters, D. M. et al. Utilization of wearable technology to assess gait and mobility post-stroke: a systematic review. J. Neuroeng. Rehabilit. 18, 1\u201318 (2021).","journal-title":"J. Neuroeng. Rehabilit."},{"key":"2150_CR34","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.gaitpost.2011.08.014","volume":"35","author":"SW Muir","year":"2012","unstructured":"Muir, S. W. et al. Gait assessment in mild cognitive impairment and alzheimer\u2019s disease: the effect of dual-task challenges across the cognitive spectrum. Gait Posture 35, 96\u2013100 (2012).","journal-title":"Gait Posture"},{"key":"2150_CR35","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1080\/14737175.2023.2229954","volume":"23","author":"A Guerra","year":"2023","unstructured":"Guerra, A., D\u2019Onofrio, V., Ferreri, F., Bologna, M. & Antonini, A. Objective measurement versus clinician-based assessment for Parkinson\u2019s disease. Expert Rev. Neurother. 23, 689\u2013702 (2023).","journal-title":"Expert Rev. Neurother."},{"key":"2150_CR36","doi-asserted-by":"crossref","unstructured":"Vasquez-Correa, J. C. et al. Multi-view representation learning via gcca for multimodal analysis of Parkinson\u2019s disease. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2966\u20132970 (IEEE, 2017).","DOI":"10.1109\/ICASSP.2017.7952700"},{"key":"2150_CR37","doi-asserted-by":"crossref","unstructured":"Endo, M. et al. Gaitforemer: Self-supervised pre-training of transformers via human motion forecasting for few-shot gait impairment severity estimation. In Medical Image Computing and Computer Assisted Intervention (MICCAI), 130\u2013139 (Springer Nature Switzerland, 2022).","DOI":"10.1007\/978-3-031-16452-1_13"},{"key":"2150_CR38","doi-asserted-by":"crossref","unstructured":"Lu, M. et al. Vision-based estimation of MDS-UPDRS gait scores for assessing Parkinson\u2019s disease motor severity. In Medical Image Computing and Computer Assisted Intervention (MICCAI), 637\u2013647 (Springer, 2020).","DOI":"10.1007\/978-3-030-59716-0_61"},{"key":"2150_CR39","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1002\/mds.26720","volume":"31","author":"A Mirelman","year":"2016","unstructured":"Mirelman, A. et al. Arm swing as a potential new prodromal marker of parkinson\u2019s disease. Mov. Disord. 31, 1527\u20131534 (2016).","journal-title":"Mov. Disord."},{"key":"2150_CR40","doi-asserted-by":"publisher","first-page":"100309","DOI":"10.1016\/j.prdoa.2025.100309","volume":"12","author":"J Mei","year":"2025","unstructured":"Mei, J. et al. The comparison of gait disorders among different motor subtypes in Parkinson\u2019s disease patients during the early and middle stages. Clin. Parkinsonism Relat. Disord. 12, 100309 (2025).","journal-title":"Clin. Parkinsonism Relat. Disord."},{"key":"2150_CR41","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1002\/mds.22340","volume":"23","author":"CG Goetz","year":"2008","unstructured":"Goetz, C. G. et al. Movement disorder society-sponsored revision of the unified parkinson\u2019s disease rating scale (mds-updrs): scale presentation and clinimetric testing results. Mov. Disord. 23, 2129\u20132170 (2008).","journal-title":"Mov. Disord."},{"key":"2150_CR42","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1002\/mds.10473","volume":"18","author":"CG Goetz","year":"2003","unstructured":"Goetz, C. G. et al. The unified parkinson\u2019s disease rating scale (UPDRS): status and recommendations. Mov. Disord. 18, 738\u2013750 (2003).","journal-title":"Mov. Disord."},{"key":"2150_CR43","doi-asserted-by":"publisher","first-page":"100278","DOI":"10.1016\/j.prdoa.2024.100278","volume":"11","author":"L Kenny","year":"2024","unstructured":"Kenny, L. et al. Inter-rater reliability of hand motor function assessment in parkinson\u2019s disease: Impact of clinician training. Clin. Parkinsonism Relat. Disord. 11, 100278 (2024).","journal-title":"Clin. Parkinsonism Relat. Disord."},{"key":"2150_CR44","doi-asserted-by":"publisher","first-page":"1472956","DOI":"10.3389\/fneur.2024.1472956","volume":"15","author":"W Yin","year":"2024","unstructured":"Yin, W. et al. Gait analysis in the early stage of parkinson\u2019s disease with a machine learning approach. Front. Neurol. 15, 1472956 (2024).","journal-title":"Front. Neurol."},{"key":"2150_CR45","doi-asserted-by":"crossref","unstructured":"Kim, K., Lyu, S., Mantri, S. & Dunn, T. W. Tulip: Multi-camera 3d precision assessment of parkinson\u2019s disease. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22551\u201322562 (IEEE, 2024).","DOI":"10.1109\/CVPR52733.2024.02128"},{"key":"2150_CR46","doi-asserted-by":"publisher","first-page":"90","DOI":"10.21037\/atm.2016.03.09","volume":"4","author":"K Yang","year":"2016","unstructured":"Yang, K. et al. Objective and quantitative assessment of motor function in parkinson\u2019s disease\u2013from the perspective of practical applications. Ann. Transl. Med. 4, 90 (2016).","journal-title":"Ann. Transl. Med."},{"key":"2150_CR47","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s13311-016-0488-5","volume":"14","author":"P Narayanaswami","year":"2017","unstructured":"Narayanaswami, P. The spectrum of functional rating scales in neurology clinical trials. Neurotherapeutics 14, 161\u2013175 (2017).","journal-title":"Neurotherapeutics"},{"key":"2150_CR48","doi-asserted-by":"crossref","unstructured":"Johnson, S. & Everingham, M. Learning effective human pose estimation from inaccurate annotation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1465\u20131472 (IEEE, 2011).","DOI":"10.1109\/CVPR.2011.5995318"},{"key":"2150_CR49","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.inffus.2021.09.016","volume":"78","author":"Y Celik","year":"2022","unstructured":"Celik, Y., Stuart, S., Woo, W. L., Sejdic, E. & Godfrey, A. Multi-modal gait: A wearable, algorithm and data fusion approach for clinical and free-living assessment. Inf. Fusion 78, 57\u201370 (2022).","journal-title":"Inf. Fusion"},{"key":"2150_CR50","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TNSRE.2021.3089613","volume":"29","author":"L Formstone","year":"2021","unstructured":"Formstone, L. et al. Quantification of motor function post-stroke using novel combination of wearable inertial and mechanomyographic sensors. IEEE Trans. Neural Syst. Rehabilit. Eng. 29, 1158\u20131167 (2021).","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"2150_CR51","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TNSRE.2025.3540842","volume":"33","author":"Z Guo","year":"2025","unstructured":"Guo, Z., Wang, Z., Wang, Y., Huo, W. & Han, J. Continuous estimation of swallowing motion with emg and mmg signals. IEEE Trans. Neural Syst. Rehabilit. Eng. 33, 787\u2013797 (2025).","journal-title":"IEEE Trans. Neural Syst. Rehabilit. Eng."},{"key":"2150_CR52","doi-asserted-by":"crossref","unstructured":"Yang, Z., Zeng, A., Yuan, C. & Li, Y. Effective whole-body pose estimation with two-stages distillation. In Proceeding of the IEEE International Conference on Computer Vision (ICCV), 4210\u20134220 (IEEE, 2023).","DOI":"10.1109\/ICCVW60793.2023.00455"},{"key":"2150_CR53","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.-E. & Sheikh, Y. Realtime multi-person 2d pose estimation using part affinity fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7291\u20137299 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.143"},{"key":"2150_CR54","doi-asserted-by":"publisher","first-page":"5296","DOI":"10.1109\/TITS.2023.3239101","volume":"45","author":"L Xu","year":"2023","unstructured":"Xu, L. et al. Zoomnas: Searching for whole-body human pose estimation in the wild. IEEE Trans. Pattern Anal. Mach. Intell. 45, 5296\u20135313 (2023).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2150_CR55","doi-asserted-by":"publisher","first-page":"561","DOI":"10.3233\/IDA-2007-11508","volume":"11","author":"S Salvador","year":"2007","unstructured":"Salvador, S. & Chan, P. Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11, 561\u2013580 (2007).","journal-title":"Intell. Data Anal."},{"key":"2150_CR56","unstructured":"Koch, G. et al. Siamese neural networks for one-shot image recognition. In International Conference on Machine Learning (ICML) Workshops, vol. 2, 1\u201330 (Lille, 2015)."},{"key":"2150_CR57","unstructured":"Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), vol. 30 (Curran, 2017)."},{"key":"2150_CR58","doi-asserted-by":"crossref","unstructured":"Zhu, W. et al. Motionbert: A unified perspective on learning human motion representations. In Proc. IEEE International Conference on Computer Vision (ICCV), 15085\u201315099 (IEEE, 2023).","DOI":"10.1109\/ICCV51070.2023.01385"},{"key":"2150_CR59","doi-asserted-by":"crossref","unstructured":"Yan, S., Xiong, Y. & Lin, D. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Proc. AAAI Conference on Artificial Intelligence (AAAI), vol. 32 (AAAI Press, 2018).","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"2150_CR60","unstructured":"Rebuffi, S.-A. et al. Data augmentation can improve robustness. In Advances in Neural Information Processing Systems (NeurIPS), vol. 34, 29935\u201329948 (Curran, 2021)."},{"key":"2150_CR61","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","volume":"3","author":"B Li","year":"2022","unstructured":"Li, B., Hou, Y. & Che, W. Data augmentation approaches in natural language processing: a survey. Ai Open 3, 71\u201390 (2022).","journal-title":"Ai Open"},{"key":"2150_CR62","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618\u2013626 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.74"},{"key":"2150_CR63","doi-asserted-by":"publisher","first-page":"3732","DOI":"10.1016\/j.jbiomech.2015.08.018","volume":"48","author":"K-LH Hoang","year":"2015","unstructured":"Hoang, K.-L. H. & Mombaur, K. Adjustments to de leva-anthropometric regression data for the changes in body proportions in elderly humans. J. Biomech. 48, 3732\u20133736 (2015).","journal-title":"J. Biomech."},{"key":"2150_CR64","doi-asserted-by":"crossref","unstructured":"Vinutha, H. P., Poornima, B. & Sagar, B. M. Detection of Outliers Using Interquartile Range Technique from Intrusion Dataset. In Information and Decision Sciences, 511\u2013518 (Springer, Singapore, 2018).","DOI":"10.1007\/978-981-10-7563-6_53"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02150-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02150-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02150-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:03:43Z","timestamp":1769695423000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-02150-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2150"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-02150-8","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"17 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"F.M. declares the following competing interests: Research support from NIHR, Innovate UK; Speaking honoraria from Abbvie, Medtronic, Boston Scientific, Bial, Merz; Travel grants from the International Parkinson\u2019s disease and Movement Disorder Society; Advisory board fees from Merz and Boston Scientific; Consultancies fees from Boston Scientific, Merz and Bial; Research support from Boston Scientific, Merz; Royalties for the book \u201cDisorders of Movement\u201d from Springer; member of the editorial board of Movement Disorders, Movement Disorders Clinical Practice, European Journal of Neurology, European Journal of Neurology. Patents describing the assessment methods documented in this article have been filed with the China National Intellectual Property Administration by Nankai University and Tianjin Huanhu Hospital. W.H., J.W., P.L., J.H., and Z.T. are inventors on the patent application: 2025113177385. W.H., J.H., and Z.T. are inventors on the patent application: 2025116591122. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"98"}}