{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T05:37:17Z","timestamp":1787636237039,"version":"build-2736575974"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Basic Science Research Program through the NRF","award":["2022R1A6A3A0108756411"],"award-info":[{"award-number":["2022R1A6A3A0108756411"]}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A2C100933711"],"award-info":[{"award-number":["2022R1A2C100933711"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1A2C100933711"],"award-info":[{"award-number":["2022R1A2C100933711"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-75445-7","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T14:04:07Z","timestamp":1728569047000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Deep learning-based detection of affected body parts in Parkinson\u2019s disease and freezing of gait using time-series imaging"],"prefix":"10.1038","volume":"14","author":[{"given":"Hwayoung","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungtae","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhong","family":"Youm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Myung","family":"Cheon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"75445_CR1","doi-asserted-by":"publisher","first-page":"2055","DOI":"10.1016\/S0140-6736(09)60492-X","volume":"373","author":"AJ Lees","year":"2009","unstructured":"Lees, A. J., Hardy, J. & Revesz, T. Parkinson\u2019s disease. Lancet 373, 2055\u20132066. https:\/\/doi.org\/10.1016\/S0140-6736(09)60492-X (2009).","journal-title":"Lancet"},{"key":"75445_CR2","doi-asserted-by":"publisher","unstructured":"Mahadevan, N. et al. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. NPJ Digit. Med. 3 https:\/\/doi.org\/10.1038\/s41746-019-0217-7\u00a0(2020).","DOI":"10.1038\/s41746-019-0217-7"},{"key":"75445_CR3","doi-asserted-by":"publisher","first-page":"105068","DOI":"10.1016\/j.neubiorev.2023.105068","volume":"147","author":"AS Monaghan","year":"2023","unstructured":"Monaghan, A. S. et al. Cognition and freezing of gait in Parkinson\u2019s disease: a systematic review and meta-analysis. Neurosci. Biobehav Rev. 147, 105068. https:\/\/doi.org\/10.1016\/j.neubiorev.2023.105068 (2023).","journal-title":"Neurosci. Biobehav Rev."},{"key":"75445_CR4","doi-asserted-by":"publisher","first-page":"2192","DOI":"10.1002\/mds.21659","volume":"22","author":"O Moore","year":"2007","unstructured":"Moore, O., Peretz, C. & Giladi, N. Freezing of gait affects quality of life of peoples with Parkinson\u2019s disease beyond its relationships with mobility and gait. Mov. Disord. 22, 2192\u20132195. https:\/\/doi.org\/10.1002\/mds.21659 (2007).","journal-title":"Mov. Disord"},{"key":"75445_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1159\/000314963","volume":"57","author":"T Herman","year":"2011","unstructured":"Herman, T., Giladi, N. & Hausdorff, J. M. Properties of the \u2018timed up and go\u2019 test: more than meets the eye. Gerontology 57, 203\u2013210. https:\/\/doi.org\/10.1159\/000314963 (2011).","journal-title":"Gerontology"},{"key":"75445_CR6","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1212\/WNL.0000000000001155","volume":"84","author":"CG Canning","year":"2015","unstructured":"Canning, C. G. et al. Exercise for falls prevention in Parkinson disease: a randomized controlled trial. Neurology 84, 304\u2013312. https:\/\/doi.org\/10.1212\/WNL.0000000000001155 (2015).","journal-title":"Neurology"},{"key":"75445_CR7","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1136\/jnnp.73.3.267","volume":"73","author":"L Rocchi","year":"2002","unstructured":"Rocchi, L., Chiari, L. & Horak, F. B. Effects of deep brain stimulation and levodopa on postural sway in Parkinson\u2019s disease. J. Neurol. Neurosurg. Psychiatry. 73, 267\u2013274. https:\/\/doi.org\/10.1136\/jnnp.73.3.267 (2002).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"issue":"Suppl 2","key":"75445_CR8","doi-asserted-by":"publisher","first-page":"S423","DOI":"10.1002\/mds.21927","volume":"23","author":"N Giladi","year":"2008","unstructured":"Giladi, N. & Nieuwboer, A. Understanding and treating freezing of gait in parkinsonism, proposed working definition, and setting the stage. Mov. Disord. 23 (Suppl 2), S423\u2013S425. https:\/\/doi.org\/10.1002\/mds.21927 (2008).","journal-title":"Mov. Disord"},{"key":"75445_CR9","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1186\/s12984-021-00958-5","volume":"18","author":"G Shalin","year":"2021","unstructured":"Shalin, G., Pardoel, S., Lemaire, E. D., Nantel, J. & Kofman, J. Prediction and detection of freezing of gait in Parkinson\u2019s disease from plantar pressure data using long short-term memory neural-networks. J. Neuroeng. Rehabil. 18, 167. https:\/\/doi.org\/10.1186\/s12984-021-00958-5 (2021).","journal-title":"J. Neuroeng. Rehabil"},{"key":"75445_CR10","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1002\/mds.27709","volume":"34","author":"M Mancini","year":"2019","unstructured":"Mancini, M. et al. Clinical and methodological challenges for assessing freezing of gait: future perspectives. Mov. Disord. 34, 783\u2013790. https:\/\/doi.org\/10.1002\/mds.27709 (2019).","journal-title":"Mov. Disord"},{"key":"75445_CR11","doi-asserted-by":"publisher","unstructured":"Channa, A., Popescu, N. & Ciobanu, V. Wearable solutions for patients with Parkinson\u2019s disease and neurocognitive disorder: a systematic review. Sens. (Basel). 20 https:\/\/doi.org\/10.3390\/s20092713\u00a0(2020).","DOI":"10.3390\/s20092713"},{"key":"75445_CR12","doi-asserted-by":"publisher","first-page":"1628","DOI":"10.1002\/mds.25628","volume":"28","author":"W Maetzler","year":"2013","unstructured":"Maetzler, W., Domingos, J., Srulijes, K., Ferreira, J. J. & Bloem, B. R. Quantitative wearable sensors for objective assessment of Parkinson\u2019s disease. Mov. Disord. 28, 1628\u20131637. https:\/\/doi.org\/10.1002\/mds.25628 (2013).","journal-title":"Mov. Disord"},{"key":"75445_CR13","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s40860-020-00114-1","volume":"6","author":"H Alaskar","year":"2020","unstructured":"Alaskar, H. et al. A data science approach for reliable classification of neuro-degenerative diseases using gait patterns. J. Reliab. Intell. Environ. 6, 233\u2013247. https:\/\/doi.org\/10.1007\/s40860-020-00114-1 (2020).","journal-title":"J. Reliab. Intell. Environ."},{"key":"75445_CR14","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.medengphy.2021.03.005","volume":"91","author":"B E","year":"2021","unstructured":"E, B., Elumalai, D. B., K, U. & V. K. & Data-driven gait analysis for diagnosis and severity rating of Parkinson\u2019s disease. Med. Eng. Phys. 91, 54\u201364. https:\/\/doi.org\/10.1016\/j.medengphy.2021.03.005 (2021).","journal-title":"Med. Eng. Phys."},{"key":"75445_CR15","doi-asserted-by":"publisher","first-page":"17269","DOI":"10.1038\/s41598-019-53656-7","volume":"9","author":"RZU Rehman","year":"2019","unstructured":"Rehman, R. Z. U. et al. Selecting clinically relevant gait characteristics for classification of early Parkinson\u2019s disease: a comprehensive machine learning approach. Sci. Rep. 9, 17269. https:\/\/doi.org\/10.1038\/s41598-019-53656-7 (2019).","journal-title":"Sci. Rep."},{"key":"75445_CR16","doi-asserted-by":"publisher","unstructured":"di Biase, L. et al. Gait analysis in Parkinson\u2019s disease: an overview of the most accurate markers for diagnosis and symptoms monitoring. Sens. (Basel). 20 https:\/\/doi.org\/10.3390\/s20123529\u00a0(2020).","DOI":"10.3390\/s20123529"},{"key":"75445_CR17","doi-asserted-by":"publisher","unstructured":"Rehman, R. Z. U. et al. Comparison of walking protocols and gait assessment systems for machine learning-based classification of Parkinson\u2019s disease. Sens. (Basel). 19 https:\/\/doi.org\/10.3390\/s19245363\u00a0(2019).","DOI":"10.3390\/s19245363"},{"key":"75445_CR18","doi-asserted-by":"publisher","unstructured":"Rehman, R. Z. U. et al. Turning detection during gait: algorithm validation and influence of sensor location and turning characteristics in the classification of Parkinson\u2019s disease. Sens. (Basel). 20 https:\/\/doi.org\/10.3390\/s20185377\u00a0(2020).","DOI":"10.3390\/s20185377"},{"key":"75445_CR19","doi-asserted-by":"publisher","first-page":"e0244396","DOI":"10.1371\/journal.pone.0244396","volume":"16","author":"T Varrecchia","year":"2021","unstructured":"Varrecchia, T. et al. An artificial neural network approach to detect presence and severity of Parkinson\u2019s disease via gait parameters. PLOS ONE. 16, e0244396. https:\/\/doi.org\/10.1371\/journal.pone.0244396 (2021).","journal-title":"PLOS ONE"},{"key":"75445_CR20","doi-asserted-by":"publisher","unstructured":"Buckley, C. et al. The role of movement analysis in diagnosing and monitoring neurodegenerative conditions: insights from gait and postural control. Brain Sci. 9 https:\/\/doi.org\/10.3390\/brainsci9020034\u00a0(2019).","DOI":"10.3390\/brainsci9020034"},{"key":"75445_CR21","doi-asserted-by":"publisher","unstructured":"Trabassi, D. et al. Machine learning approach to support the detection of Parkinson\u2019s disease in IMU-based Gait analysis. Sens. (Basel). 22. https:\/\/doi.org\/10.3390\/s22103700\u00a0(2022).","DOI":"10.3390\/s22103700"},{"key":"75445_CR22","doi-asserted-by":"publisher","first-page":"1642","DOI":"10.1007\/s00415-017-8424-0","volume":"264","author":"AL Silva de Lima","year":"2017","unstructured":"Silva de Lima, A. L. et al. Freezing of gait and fall detection in Parkinson\u2019s disease using wearable sensors: a systematic review. J. Neurol. 264, 1642\u20131654. https:\/\/doi.org\/10.1007\/s00415-017-8424-0 (2017).","journal-title":"J. Neurol."},{"key":"75445_CR23","doi-asserted-by":"publisher","unstructured":"Jiang, Y., Hernandez, V., Venture, G., Kuli\u0107, D. & Chen, K. A Data-driven approach to predict fatigue in exercise based on motion data from wearable sensors or force plate. Sens. (Basel). 21. https:\/\/doi.org\/10.3390\/s21041499\u00a0(2021).","DOI":"10.3390\/s21041499"},{"key":"75445_CR24","doi-asserted-by":"publisher","unstructured":"Kwon, H. et al. An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients. Sens. (Basel). 23. https:\/\/doi.org\/10.3390\/s23041766\u00a0(2023).","DOI":"10.3390\/s23041766"},{"key":"75445_CR25","doi-asserted-by":"publisher","first-page":"2144","DOI":"10.1002\/mds.28631","volume":"36","author":"A Mirelman","year":"2021","unstructured":"Mirelman, A. et al. Detecting sensitive mobility features for Parkinson\u2019s disease stages via machine learning. Mov. Disord. 36, 2144\u20132155. https:\/\/doi.org\/10.1002\/mds.28631 (2021).","journal-title":"Mov. Disord"},{"key":"75445_CR26","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/s41746-022-00568-y","volume":"5","author":"AS Chandrabhatla","year":"2022","unstructured":"Chandrabhatla, A. S., Pomeraniec, I. J. & Ksendzovsky, A. Coevolution of machine learning and digital technologies to improve monitoring of Parkinson\u2019s disease motor symptoms. NPJ Digit. Med. 5, 32. https:\/\/doi.org\/10.1038\/s41746-022-00568-y (2022).","journal-title":"NPJ Digit. Med."},{"key":"75445_CR27","doi-asserted-by":"publisher","first-page":"e0171764","DOI":"10.1371\/journal.pone.0171764","volume":"12","author":"D Rodr\u00edguez-Mart\u00edn","year":"2017","unstructured":"Rodr\u00edguez-Mart\u00edn, D. et al. Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer. PLOS ONE. 12, e0171764. https:\/\/doi.org\/10.1371\/journal.pone.0171764 (2017). (Pubmed:28199357).","journal-title":"PLOS ONE"},{"key":"75445_CR28","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.compbiomed.2017.03.020","volume":"84","author":"A Sam\u00e0","year":"2017","unstructured":"Sam\u00e0, A. et al. Estimating bradykinesia severity in Parkinson\u2019s disease by analyzing gait through a waist-worn sensor. Comput. Biol. Med. 84, 114\u2013123. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.03.020 (2017).","journal-title":"Comput. Biol. Med."},{"key":"75445_CR29","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1109\/TNSRE.2020.2969649","volume":"28","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y. et al. Prediction of freezing of gait in patients with Parkinson\u2019s disease by Iidentifying Iimpaired Ggait patterns. IEEE Trans. Neural Syst. Rehabil Eng. 28, 591\u2013600. https:\/\/doi.org\/10.1109\/TNSRE.2020.2969649 (2020).","journal-title":"IEEE Trans. Neural Syst. Rehabil Eng."},{"key":"75445_CR30","doi-asserted-by":"publisher","first-page":"4294","DOI":"10.1109\/JSEN.2022.3142750","volume":"22","author":"Y Yan","year":"2022","unstructured":"Yan, Y. et al. Topological descriptors of gait nonlinear dynamics toward freezing-of-gait episodes recognition in Parkinson\u2019s disease. IEEE Sens. J. 22, 4294\u20134304. https:\/\/doi.org\/10.1109\/JSEN.2022.3142750 (2022).","journal-title":"IEEE Sens. J."},{"key":"75445_CR31","doi-asserted-by":"publisher","unstructured":"Corr\u00e0, M. F., Warmerdam, E., Vila-Ch\u00e3, N., Maetzler, W. & Maia, L. Wearable health technology to quantify the functional impact of peripheral neuropathy on mobility in Parkinson\u2019s disease: a systematic review. Sens. (Basel). 20. https:\/\/doi.org\/10.3390\/s20226627\u00a0(2020).","DOI":"10.3390\/s20226627"},{"key":"75445_CR32","doi-asserted-by":"publisher","unstructured":"Correno, M. B., Hansen, C., Carlin, T. & Vuillerme, N. Objective measurement of walking activity using wearable technologies in people with Parkinson disease: a systematic review. Sens. (Basel). 22. https:\/\/doi.org\/10.3390\/s22124551\u00a0(2022).","DOI":"10.3390\/s22124551"},{"key":"75445_CR33","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TNSRE.2019.2933626","volume":"27","author":"A Arami","year":"2019","unstructured":"Arami, A., Poulakakis-Daktylidis, A., Tai, Y. F. & Burdet, E. Prediction of gait freezing in parkinsonian patients: a binary classification augmented with time series prediction. IEEE Trans. Neural Syst. Rehabil Eng. 27, 1909\u20131919. https:\/\/doi.org\/10.1109\/TNSRE.2019.2933626 (2019).","journal-title":"IEEE Trans. Neural Syst. Rehabil Eng."},{"key":"75445_CR34","doi-asserted-by":"publisher","unstructured":"Sigcha, L. et al. Deep learning approaches for detecting freezing of gait in Parkinson\u2019s disease patients through on-body acceleration sensors. Sens. (Basel). 20 https:\/\/doi.org\/10.3390\/s20071895\u00a0(2020).","DOI":"10.3390\/s20071895"},{"key":"75445_CR35","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/s41597-021-00831-z","volume":"8","author":"G Vergara-Diaz","year":"2021","unstructured":"Vergara-Diaz, G. et al. Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson\u2019s disease. Sci. Data. 8, 47. https:\/\/doi.org\/10.1038\/s41597-021-00831-z (2021).","journal-title":"Sci. Data"},{"key":"75445_CR36","doi-asserted-by":"publisher","unstructured":"Atri, R. et al. Deep learning for daily monitoring of Parkinson\u2019s disease outside the clinic using wearable sensors. Sens. (Basel). 22. https:\/\/doi.org\/10.3390\/s22186831\u00a0(2022).","DOI":"10.3390\/s22186831"},{"key":"75445_CR37","doi-asserted-by":"publisher","unstructured":"Aich, S. et al. A supervised machine learning approach to detect the on\/off state in Parkinson\u2019s disease using wearable based gait signals. Diagnostics (Basel). 10 https:\/\/doi.org\/10.3390\/diagnostics10060421\u00a0(2020).","DOI":"10.3390\/diagnostics10060421"},{"key":"75445_CR38","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1038\/s41746-022-00674-x","volume":"5","author":"V Ramesh","year":"2022","unstructured":"Ramesh, V. & Bilal, E. Detecting motor symptom fluctuations in Parkinson\u2019s disease with generative adversarial networks. NPJ Digit. Med. 5, 138. https:\/\/doi.org\/10.1038\/s41746-022-00674-x (2022).","journal-title":"NPJ Digit. Med."},{"key":"75445_CR39","doi-asserted-by":"publisher","unstructured":"Haji Ghassemi, N. et al. Segmentation of gait sequences in sensor-based movement analysis: a comparison of methods in Parkinson\u2019s disease. Sens. (Basel). 18 https:\/\/doi.org\/10.3390\/s18010145\u00a0(2018).","DOI":"10.3390\/s18010145"},{"key":"75445_CR40","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s12984-021-00883-7","volume":"18","author":"N Roth","year":"2021","unstructured":"Roth, N. et al. Hidden Markov model based stride segmentation on unsupervised free-living gait data in Parkinson\u2019s disease patients. J. Neuroeng. Rehabil. 18, 93. https:\/\/doi.org\/10.1186\/s12984-021-00883-7 (2021).","journal-title":"J. Neuroeng. Rehabil"},{"key":"75445_CR41","doi-asserted-by":"publisher","first-page":"1497","DOI":"10.1212\/wnl.57.8.1497","volume":"57","author":"AJ Hughes","year":"2001","unstructured":"Hughes, A. J., Daniel, S. E. & Lees, A. J. Improved accuracy of clinical diagnosis of Lewy body Parkinson\u2019s disease. Neurology. 57, 1497\u20131499. https:\/\/doi.org\/10.1212\/wnl.57.8.1497 (2001).","journal-title":"Neurology"},{"key":"75445_CR42","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1212\/wnl.17.5.427","volume":"17","author":"MM Hoehn","year":"1967","unstructured":"Hoehn, M. M. & Yahr, M. D. Parkinsonism: onset, progression and mortality. Neurology. 17, 427\u2013442. https:\/\/doi.org\/10.1212\/wnl.17.5.427 (1967).","journal-title":"Neurology"},{"key":"75445_CR43","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1002\/mds.20213","volume":"19","author":"CG Goetz","year":"2004","unstructured":"Goetz, C. G. et al. Movement Disorder Society Task Force report on the Hoehn and Yahr staging scale: status and recommendations. Mov. Disord. 19, 1020\u20131028. https:\/\/doi.org\/10.1002\/mds.20213 (2004).","journal-title":"Mov. Disord"},{"key":"75445_CR44","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/0022-3956(75)90026-6","volume":"12","author":"MF Folstein","year":"1975","unstructured":"Folstein, M. F., Folstein, S. E. & McHugh, P. R. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr Res. 12, 189\u2013198. https:\/\/doi.org\/10.1016\/0022-3956(75)90026-6 (1975).","journal-title":"J. Psychiatr Res."},{"key":"75445_CR45","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/j.gaitpost.2009.07.108","volume":"30","author":"A Nieuwboer","year":"2009","unstructured":"Nieuwboer, A. et al. Reliability of the new freezing of gait questionnaire: agreement between patients with Parkinson\u2019s disease and their carers. Gait Posture. 30, 459\u2013463. https:\/\/doi.org\/10.1016\/j.gaitpost.2009.07.108 (2009).","journal-title":"Gait Posture"},{"key":"75445_CR46","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1186\/s12984-021-00975-4","volume":"18","author":"H Park","year":"2021","unstructured":"Park, H. et al. Classification of Parkinson\u2019s disease with freezing of gait based on 360 degrees turning analysis using 36 kinematic features. J. Neuroeng. Rehabil. 18, 177. https:\/\/doi.org\/10.1186\/s12984-021-00975-4 (2021).","journal-title":"J. Neuroeng. Rehabil"},{"key":"75445_CR47","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.neuroscience.2012.01.024","volume":"207","author":"J Spildooren","year":"2012","unstructured":"Spildooren, J. et al. Turning and unilateral cueing in Parkinson\u2019s disease patients with and without freezing of gait. Neuroscience. 207, 298\u2013306. https:\/\/doi.org\/10.1016\/j.neuroscience.2012.01.024 (2012).","journal-title":"Neuroscience"},{"key":"75445_CR48","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/S1474-4422(06)70549-X","volume":"5","author":"R Djaldetti","year":"2006","unstructured":"Djaldetti, R., Ziv, I. & Melamed, E. The mystery of motor asymmetry in Parkinson\u2019s disease. Lancet Neurol. 5, 796\u2013802. https:\/\/doi.org\/10.1016\/S1474-4422(06)70549-X (2006).","journal-title":"Lancet Neurol."},{"key":"75445_CR49","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1212\/01.WNL.0000163993.82388.C8","volume":"64","author":"RJ Uitti","year":"2005","unstructured":"Uitti, R. J., Baba, Y., Whaley, N. R., Wszolek, Z. K. & Putzke, J. D. Parkinson disease: handedness predicts asymmetry. Neurology. 64, 1925\u20131930. https:\/\/doi.org\/10.1212\/01.WNL.0000163993.82388.C8 (2005).","journal-title":"Neurology"},{"key":"75445_CR50","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1002\/j.1939-4640.1992.tb01632.x","volume":"13","author":"RO Davis","year":"1992","unstructured":"Davis, R. O. & Katz, D. F. Standardization and comparability of CASA instruments. J. Androl. 13, 81\u201386. https:\/\/doi.org\/10.1002\/j.1939-4640.1992.tb01632.x (1992).","journal-title":"J. Androl."},{"key":"75445_CR51","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1209\/0295-5075\/4\/9\/004","volume":"4","author":"JP Eckmann","year":"1987","unstructured":"Eckmann, J. P., Kamphorst, S. O. & Ruelle, D. Recurrence plots of dynamical systems. Europhys. Lett. 4, 973\u2013977. https:\/\/doi.org\/10.1209\/0295-5075\/4\/9\/004 (1987).","journal-title":"Europhys. Lett."},{"key":"75445_CR52","unstructured":"Wang, Z. & Oates, T. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks in Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence, (2015)."},{"key":"75445_CR53","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1136\/neurintsurg-2015-011697","volume":"8","author":"VH Tank","year":"2016","unstructured":"Tank, V. H. et al. Drug eluting stents versus bare metal stents for the treatment of extracranial vertebral artery disease: a meta-analysis. J. Neurointerv Surg. 8, 770\u2013774. https:\/\/doi.org\/10.1136\/neurintsurg-2015-011697 (2016).","journal-title":"J. Neurointerv Surg."},{"key":"75445_CR54","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"75445_CR55","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1602.07360","author":"FN Iandola","year":"2016","unstructured":"Iandola, F. N. et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <\u20090.5\u00a0MB model size. arXiv Preprint:1602 07360 (DOI. https:\/\/doi.org\/10.48550\/arXiv.1602.07360 (2016).","journal-title":"arXiv Preprint:1602 07360 (DOI"},{"key":"75445_CR56","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.gaitpost.2019.09.001","volume":"74","author":"M Bertoli","year":"2019","unstructured":"Bertoli, M., Croce, U. D., Cereatti, A. & Mancini, M. Objective measures to investigate turning impairments and freezing of gait in people with Parkinson\u2019s disease. Gait Posture. 74, 187\u2013193. https:\/\/doi.org\/10.1016\/j.gaitpost.2019.09.001 (2019).","journal-title":"Gait Posture"},{"key":"75445_CR57","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1097\/NPT.0000000000000242","volume":"42","author":"D Conradsson","year":"2018","unstructured":"Conradsson, D., Paquette, C. & Franz\u00e9n, E. Turning stability in individuals with Parkinson disease. J. Neurol. Phys. Ther. 42, 241\u2013247. https:\/\/doi.org\/10.1097\/NPT.0000000000000242 (2018).","journal-title":"J. Neurol. Phys. Ther."},{"key":"75445_CR58","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.gaitpost.2018.10.018","volume":"67","author":"A Weiss","year":"2019","unstructured":"Weiss, A. et al. The transition between turning and sitting in patients with Parkinson\u2019s disease: a wearable device detects an unexpected sequence of events. Gait Posture. 67, 224\u2013229. https:\/\/doi.org\/10.1016\/j.gaitpost.2018.10.018 (2019).","journal-title":"Gait Posture"},{"key":"75445_CR59","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1002\/mds.20115","volume":"19","author":"BR Bloem","year":"2004","unstructured":"Bloem, B. R., Hausdorff, J. M., Visser, J. E. & Giladi, N. Falls and freezing of gait in Parkinson\u2019s disease: a review of two interconnected, episodic phenomena. Mov. Disord. 19, 871\u2013884. https:\/\/doi.org\/10.1002\/mds.20115 (2004).","journal-title":"Mov. Disord"},{"key":"75445_CR60","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3233\/NRE-151236","volume":"37","author":"M Mancini","year":"2015","unstructured":"Mancini, M. et al. Continuous monitoring of turning in Parkinson\u2019s disease: rehabilitation potential. NeuroRehabilitation 37, 3\u201310. https:\/\/doi.org\/10.3233\/NRE-151236 (2015).","journal-title":"NeuroRehabilitation"},{"issue":"Suppl 2","key":"75445_CR61","doi-asserted-by":"publisher","first-page":"S444","DOI":"10.1002\/mds.21984","volume":"23","author":"M Plotnik","year":"2008","unstructured":"Plotnik, M. & Hausdorff, J. M. The role of gait rhythmicity and bilateral coordination of stepping in the pathophysiology of freezing of gait in Parkinson\u2019s disease. Mov. Disord. 23 (Suppl 2), S444\u2013S450. https:\/\/doi.org\/10.1002\/mds.21984 (2008).","journal-title":"Mov. Disord"},{"key":"75445_CR62","doi-asserted-by":"publisher","first-page":"21143","DOI":"10.1038\/s41598-021-00543-9","volume":"11","author":"M Godi","year":"2021","unstructured":"Godi, M., Arcolin, I., Giardini, M., Corna, S. & Schieppati, M. A pathophysiological model of gait captures the details of the impairment of pace\/rhythm, variability and asymmetry in parkinsonian patients at distinct stages of the disease. Sci. Rep. 11, 21143. https:\/\/doi.org\/10.1038\/s41598-021-00543-9 (2021).","journal-title":"Sci. Rep."},{"key":"75445_CR63","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1111\/nyas.14860","volume":"1516","author":"E Troisi Lopez","year":"2022","unstructured":"Troisi Lopez, E. et al. The kinectome: a comprehensive kinematic map of human motion in health and disease. Ann. N Y Acad. Sci. 1516, 247\u2013261. https:\/\/doi.org\/10.1111\/nyas.14860 (2022).","journal-title":"Ann. N Y Acad. Sci."},{"key":"75445_CR64","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1002\/mds.28462","volume":"36","author":"MHG Monje","year":"2021","unstructured":"Monje, M. H. G. et al. Motor onset topography and progression in Parkinson\u2019s disease: the upper limb is first. Mov. Disord. 36, 905\u2013915. https:\/\/doi.org\/10.1002\/mds.28462 (2021).","journal-title":"Mov. Disord"},{"key":"75445_CR65","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/TBME.2011.2149521","volume":"58","author":"A Sant\u2019Anna","year":"2011","unstructured":"Sant\u2019Anna, A., Salarian, A. & Wickstr\u00f6m, N. A new measure of movement symmetry in early Parkinson\u2019s disease patients using symbolic processing of inertial sensor data. IEEE Trans. Bio Med. Eng. 58, 2127\u20132135. https:\/\/doi.org\/10.1109\/TBME.2011.2149521 (2011).","journal-title":"IEEE Trans. Bio Med. Eng."},{"key":"75445_CR66","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1097\/NPT.0000000000000286","volume":"44","author":"S Stuart","year":"2020","unstructured":"Stuart, S. & Mancini, M. Prefrontal cortical activation with open and closed-loop tactile cueing when walking and turning in Parkinson disease: a pilot study. J. Neurol. Phys. Ther. 44, 121\u2013131. https:\/\/doi.org\/10.1097\/NPT.0000000000000286 (2020).","journal-title":"J. Neurol. Phys. Ther."},{"key":"75445_CR67","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1017\/s0317167100014591","volume":"40","author":"S Akram","year":"2013","unstructured":"Akram, S., Frank, J. S. & Jog, M. Parkinson\u2019s disease and segmental coordination during turning: I. Standing turns. Can. J. Neurol. Sci. 40, 512\u2013519. https:\/\/doi.org\/10.1017\/s0317167100014591 (2013). (Pubmed:23786733).","journal-title":"Can. J. Neurol. Sci."},{"key":"75445_CR68","doi-asserted-by":"publisher","unstructured":"Contreras, A. & Grandas, F. Risk of falls in Parkinson\u2019s disease: a cross-sectional study of 160 patients. Parkinsons Dis. 362572.\u00a0https:\/\/doi.org\/10.1155\/2012\/362572\u00a0(2012).","DOI":"10.1155\/2012\/362572"},{"key":"75445_CR69","doi-asserted-by":"publisher","unstructured":"Slemen\u0161ek, J. et al. Human gait activity recognition machine learning methods. Sens. (Basel). 23 https:\/\/doi.org\/10.3390\/s23020745\u00a0(2023).","DOI":"10.3390\/s23020745"},{"key":"75445_CR70","doi-asserted-by":"publisher","unstructured":"Sigcha, L. et al. Improvement of performance in freezing of gait detection in Parkinson\u2019s disease using transformer networks and a single waist-worn triaxial accelerometer. Eng. Appl. Artif. Intell. 116 https:\/\/doi.org\/10.1016\/j.engappai.2022.105482 (2022).","DOI":"10.1016\/j.engappai.2022.105482"},{"key":"75445_CR71","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.compbiomed.2018.02.007","volume":"95","author":"HB Kim","year":"2018","unstructured":"Kim, H. B. et al. Wrist sensor-based tremor severity quantification in Parkinson\u2019s disease using convolutional neural network. Comput. Biol. Med. 95, 140\u2013146. https:\/\/doi.org\/10.1016\/j.compbiomed.2018.02.007 (2018).","journal-title":"Comput. Biol. Med."},{"key":"75445_CR72","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.knosys.2017.10.017","volume":"139","author":"J Camps","year":"2018","unstructured":"Camps, J. et al. Deep learning for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit. Knowl. Based Syst. 139, 119\u2013131. https:\/\/doi.org\/10.1016\/j.knosys.2017.10.017 (2018).","journal-title":"Knowl. Based Syst."},{"key":"75445_CR73","doi-asserted-by":"publisher","first-page":"255","DOI":"10.3233\/JPD-130282","volume":"4","author":"Y Okuma","year":"2014","unstructured":"Okuma, Y. Freezing of gait and falls in Parkinson\u2019s disease. J. Parkinsons Dis. 4, 255\u2013260. https:\/\/doi.org\/10.3233\/JPD-130282 (2014).","journal-title":"J. Parkinsons Dis."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-75445-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-75445-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-75445-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T17:01:48Z","timestamp":1728838908000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-75445-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["75445"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-75445-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"30 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"23732"}}