{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:33:26Z","timestamp":1790620406510,"version":"4.1.0"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The diagnosis of Parkinson\u2019s disease relies heavily on the subjective assessment of physicians, which depends on their individual experience and training, potentially leading to inconsistent diagnostic results. Therefore, developing an objective and efficient diagnostic method is essential to improve the accuracy and timeliness of Parkinson\u2019s disease diagnosis. In this study, we utilized the PhysioNet dataset, a time-series dataset comprising data from 93 Parkinson\u2019s patients and 73 healthy individuals. The dataset contains vertical ground reaction forces recorded from 16 sensors (8 per foot) during a 2-minute test at a sampling rate of 100 Hz. To address challenges such as limited dataset size, high labeling noise, and high intra-class variability, we performed data preprocessing and applied various data augmentation techniques, including jittering, scaling, rotation, permutation, magnitude warping, time warping, cropping, and linear residuals. These methods were evaluated using one-dimensional-convolutional neural network (1D-ConvNet) and one-dimensional Transformer networks. By conducting 10-fold cross-validation, we observed significant improvements in classification performance. The best data augmentation strategy achieved 90.8% accuracy, 92.0% precision, 91.0% recall, and a 91.0% F1 score in assessing disease severity. These results highlight the importance of selecting appropriate data augmentation techniques for time-series data to improve model generalization and diagnostic reliability, while also offering new insights for researchers working with sensor device data. Our results demonstrate that data-enhanced methods can significantly boost the performance of machine-learning models in the field of Parkinson\u2019s disease diagnosis.<\/jats:p>","DOI":"10.1371\/journal.pone.0319826","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T23:50:33Z","timestamp":1743637833000},"page":"e0319826","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Efficient quantification of Parkinson\u2019s disease severity using augmented time-series data"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9545-5443","authenticated-orcid":true,"given":"Hua","family":"Huo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shupei","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongfang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningya","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"pone.0319826.ref001"},{"key":"pone.0319826.ref002"},{"key":"pone.0319826.ref003","doi-asserted-by":"crossref","first-page":"120541","DOI":"10.1016\/j.eswa.2023.120541","article-title":"Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson\u2019s disease: A systematic review","volume":"229","author":"L Sigcha","year":"2023","journal-title":"Expert Syst Appl"},{"issue":"2","key":"pone.0319826.ref004","doi-asserted-by":"crossref","first-page":"485","DOI":"10.4103\/NRR.NRR-D-24-00207","article-title":"Movement analysis in the diagnosis and management of Parkinson\u2019s disease","volume":"20","author":"J Burtscher","year":"2025","journal-title":"Neural Regen Res"},{"issue":"2","key":"pone.0319826.ref005","first-page":"1","article-title":"Evaluation of wearable sensor devices in Parkinson\u2019s disease: a review of current status and future prospects","volume":"2020","author":"R Lu","year":"2020","journal-title":"Parkinson\u2019s Dis"},{"issue":"1","key":"pone.0319826.ref006","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1038\/s41531-023-00585-y","article-title":"Overview on wearable sensors for the management of Parkinson\u2019s disease","volume":"9","author":"C Moreau","year":"2023","journal-title":"NPJ Parkinsons Dis"},{"issue":"1","key":"pone.0319826.ref007","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s41746-024-01217-2","article-title":"Digital biomarkers for precision diagnosis and monitoring in Parkinson\u2019s disease","volume":"7","author":"Y-M Sun","year":"2024","journal-title":"NPJ Digit Med"},{"key":"pone.0319826.ref008","doi-asserted-by":"crossref","first-page":"35219","DOI":"10.1109\/ACCESS.2022.3162844","article-title":"Parkinsons\u2019s disease management via wearable sensors: A systematic review","volume":"10","author":"H Mughal","year":"2022","journal-title":"IEEE Access"},{"issue":"1","key":"pone.0319826.ref009","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1186\/s12984-021-00958-5","article-title":"Prediction and detection of freezing of gait in Parkinson\u2019s disease from plantar pressure data using long short-term memory neural-networks","volume":"18","author":"G Shalin","year":"2021","journal-title":"J Neuroeng Rehabil"},{"issue":"22","key":"pone.0319826.ref010","doi-asserted-by":"crossref","first-page":"10189","DOI":"10.3390\/app142210189","article-title":"Evaluating motor symptoms in Parkinson\u2019s disease through wearable sensors: a systematic review of digital biomarkers","volume":"14","author":"C Polvorinos-Fern\u00e1ndez","year":"2024","journal-title":"Appl Sci"},{"key":"pone.0319826.ref011","doi-asserted-by":"crossref","first-page":"109347","DOI":"10.1016\/j.patcog.2023.109347","article-title":"A comprehensive survey of image augmentation techniques for deep learning","volume":"137","author":"M Xu","year":"2023","journal-title":"Pattern Recognit"},{"key":"pone.0319826.ref012"},{"key":"pone.0319826.ref013"},{"issue":"9","key":"pone.0319826.ref014","doi-asserted-by":"crossref","first-page":"885","DOI":"10.4085\/1062-6050-500-19","article-title":"training load and its role in injury prevention, part I: back to the future","volume":"55","author":"FM Impellizzeri","year":"2020","journal-title":"J Athl Train"},{"issue":"15","key":"pone.0319826.ref015","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1136\/bjsports-2016-096697","article-title":"An injury prevention pyramid for elite sports teams","volume":"52","author":"PA Coles","year":"2018","journal-title":"Br J Sports Med"},{"issue":"7","key":"pone.0319826.ref016","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1001\/jamaneurol.2018.0809","article-title":"Using smartphones and machine learning to quantify Parkinson disease severity: the mobile Parkinson disease score","volume":"75","author":"A Zhan","year":"2018","journal-title":"JAMA Neurol"},{"key":"pone.0319826.ref017"},{"key":"pone.0319826.ref018"},{"key":"pone.0319826.ref019"},{"issue":"10","key":"pone.0319826.ref020","doi-asserted-by":"crossref","first-page":"e0183989","DOI":"10.1371\/journal.pone.0183989","article-title":"Wearable sensors objectively measure gait parameters in Parkinson\u2019s disease","volume":"12","author":"JCM Schlachetzki","year":"2017","journal-title":"PLoS One"},{"key":"pone.0319826.ref021","doi-asserted-by":"crossref","first-page":"113075","DOI":"10.1016\/j.eswa.2019.113075","article-title":"Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait","volume":"143","author":"I El Maachi","year":"2020","journal-title":"Expert Syst Appl"},{"key":"pone.0319826.ref022"},{"key":"pone.0319826.ref023","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/TMM.2021.3060280","article-title":"Associated spatio-temporal capsule network for gait recognition","volume":"24","author":"A Zhao","year":"2022","journal-title":"IEEE Trans Multimedia"},{"issue":"5","key":"pone.0319826.ref024","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1007\/s12553-022-00698-z","article-title":"A deep learning approach for parkinson\u2019s disease severity assessment","volume":"12","author":"T A\u015furo\u011flu","year":"2022","journal-title":"Health Technol"},{"key":"pone.0319826.ref025","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3284944","article-title":"Detection and classification of early stages of parkinson\u2019s disease through wearable sensors and machine learning","volume":"72","author":"A Shcherbak","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"pone.0319826.ref026","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1186\/s12984-020-00756-5","article-title":"Classification of Parkinson\u2019s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach","volume":"17","author":"S Moon","year":"2020","journal-title":"J Neuroeng Rehabil"},{"issue":"1","key":"pone.0319826.ref027","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1038\/s41531-023-00581-2","article-title":"Identification of motor progression in Parkinson\u2019s disease using wearable sensors and machine learning","volume":"9","author":"C Sotirakis","year":"2023","journal-title":"NPJ Parkinsons Dis"},{"key":"pone.0319826.ref028"},{"key":"pone.0319826.ref029"},{"issue":"9","key":"pone.0319826.ref030","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1109\/TASLP.2015.2438544","article-title":"Data augmentation for deep neural network acoustic modeling","volume":"23","author":"X Cui","year":"2015","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"pone.0319826.ref031","article-title":"TTS-GAN: A Transformer-Based Time-Series Generative Adversarial Network","author":"X Li","year":"2022","journal-title":"In: Proceedings of the international conference on artificial intelligence in medicine"},{"key":"pone.0319826.ref032"},{"key":"pone.0319826.ref033"},{"key":"pone.0319826.ref034","article-title":"Data augmentation for time series classification using convolutional neural networks","author":"A Le Guennec","year":"2016","journal-title":"In: ECML\/PKDD workshop on AALTD"},{"key":"pone.0319826.ref035","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.neunet.2021.01.001","article-title":"Multi-scale attention convolutional neural network for time series classification","volume":"136","author":"W Chen","year":"2021","journal-title":"Neural Netw"},{"key":"pone.0319826.ref036"},{"key":"pone.0319826.ref037"},{"key":"pone.0319826.ref038","article-title":"Data augmentation of wearable sensor data for Parkinson\u2019s disease monitoring using convolutional neural networks","author":"TT Um","year":"2017","journal-title":"In: Proceedings of the 19th ACM international conference on multimodal interaction"},{"issue":"21","key":"pone.0319826.ref039","doi-asserted-by":"crossref","first-page":"9004","DOI":"10.3390\/s23219004","article-title":"A deep learning approach for automatic and objective grading of the motor impairment severity in Parkinson\u2019s disease for use in tele-assessments","volume":"23","author":"M Singh","year":"2023","journal-title":"Sensors (Basel)"},{"issue":"23","key":"pone.0319826.ref040","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"AL Goldberger","year":"2000","journal-title":"Circulation"},{"key":"pone.0319826.ref041"},{"issue":"8","key":"pone.0319826.ref042","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1111\/j.1460-9568.2007.05810.x","article-title":"Rhythmic auditory stimulation modulates gait variability in Parkinson\u2019s disease","volume":"26","author":"JM Hausdorff","year":"2007","journal-title":"Eur J Neurosci"},{"issue":"9","key":"pone.0319826.ref043","first-page":"1177","article-title":"Treadmill walking as an external pacemaker to improve gait rhythm and stability in Parkinson\u2019s disease","volume":"20","author":"P Frenkel-Toledo","year":"2010","journal-title":"Movement Disorders"},{"issue":"6","key":"pone.0319826.ref044","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/JBHI.2018.2865218","article-title":"IMU-based classification of Parkinson\u2019s disease from gait: a sensitivity analysis on sensor location and feature selection","volume":"22","author":"C Caramia","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"pone.0319826.ref045","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2020.587057","article-title":"Parkinson\u2019s disease diagnosis and severity assessment using ground reaction forces and neural networks","volume":"11","author":"S Veeraragavan","year":"2020","journal-title":"Front Physiol."},{"issue":"4","key":"pone.0319826.ref046","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1007\/s00521-023-09193-6","article-title":"1D-convolutional transformer for Parkinson disease diagnosis from gait","volume":"36","author":"S Naimi","year":"2023","journal-title":"Neural Comput & Applic"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0319826","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T23:51:40Z","timestamp":1743637900000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0319826"}},"subtitle":[],"editor":[{"given":"Luigi","family":"Borz\u00ec","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,4,2]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0319826","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,2]]}}}