{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:39:49Z","timestamp":1791913189443,"version":"4.3.4"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>wearable movement sensor technology shows promise for objective assessment of Parkinson\u2019s disease (PD) motor symptoms, but optimal machine learning approaches and feature sets for accurate PD detection remain unclear. This study provides a comprehensive evaluation of classification algorithms, feature contributions, and optimization techniques for PD detection using wearable movement sensor data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We compared twelve diverse machine learning classifiers on motion sensor data, conducted systematic feature ablation studies across statistical, frequency-domain, dynamic, and complexity feature categories, optimized Random Forest parameters using three meta-heuristic algorithms, which is Particle Swarm Optimization (PSO), Improved Satin Swarm Algorithm (ISSA), and Enhanced Whale Optimization Algorithm (EWOA), and performed SHAP value analysis to identify the most influential features and their impact patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Random Forest demonstrated superior performance (86.7% accuracy) among all classifiers. Statistical features contributed most significantly to classification performance, while complexity, dynamic, and frequency domain features provided complementary information. PSO-optimized Random Forest achieved 87.65% accuracy, outperforming other optimization approaches. SHAP analysis identified entropy-based measures and standard deviations as the most influential features, with accelerometer-derived complexity measures driving high-probability PD predictions and gyroscope-derived measurements dominating low-probability outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Ensemble-based methods effectively capture the complex, non-linear relationship between movement characteristics and PD diagnosis. Comprehensive feature extraction frameworks incorporating multiple movement dimensions significantly enhance detection accuracy. The asymmetric feature influence patterns for positive versus negative predictions align with clinical understanding of PD as a disorder characterized by altered movement complexity and variability. These findings provide a foundation for developing accurate, interpretable wearable monitoring systems for Parkinson\u2019s disease detection and management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphys.2025.1737585","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T06:37:19Z","timestamp":1767595039000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Advancing Parkinson\u2019s disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics"],"prefix":"10.3389","volume":"16","author":[{"given":"Jun-Zhi","family":"Xiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin-Yong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Bin","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James A.","family":"Esquivel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Qun","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"023115","DOI":"10.1063\/1.4942352","article-title":"Entropy-based complexity measures for gait data of patients with parkinson\u2019s disease","volume":"26","author":"Afsar","year":"2016","journal-title":"Chaos An Interdiscip. J. Nonlinear Sci."},{"key":"B2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.4103\/abr.abr_254_21","article-title":"Evaluating the effect of parkinson\u2019s disease on jitter and shimmer speech features","volume":"10","author":"Akbarzadeh-T","year":"2021","journal-title":"Adv. Biomed. Res."},{"key":"B3","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/ATSIP62566.2024.10638866","article-title":"Performance analysis of parkinson detection techniques","volume-title":"2024 IEEE 7th international conference on advanced technologies, signal and image processing (ATSIP)","author":"Ammous","year":"2024"},{"key":"B4","doi-asserted-by":"publisher","first-page":"597","DOI":"10.18535\/ijsrm\/v9i05.ec03","article-title":"Implementing explainable AI in healthcare: techniques for interpretable machine learning models in clinical decision-making","volume":"9","author":"Arjunan","year":"2021","journal-title":"Int. J. Sci. Res. Manag. (IJSRM)"},{"key":"B5","doi-asserted-by":"crossref","DOI":"10.1109\/IECBES.2018.8626737","article-title":"Multimodal human activity recognition from wearable inertial sensors using machine learning","volume-title":"2018 IEEE-EMBS conference on biomedical engineering and sciences (IECBES)","author":"Badawi","year":"2018"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1109\/jbhi.2019.2943866","article-title":"Joohi jimenez-Shahed multilevel features for sensor-based assessment of motor fluctuation in parkinson\u2019s disease subjects","volume":"24","author":"Behnaz","year":"2019","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"B7","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.3390\/s24072195","article-title":"NCER-PD consortium sensor-based quantification of MDS-UPDRS III subitems in parkinson\u2019s disease using machine learning","volume":"24","author":"Bremm","year":"2024","journal-title":"Sensors Basel, Switz."},{"key":"B8","doi-asserted-by":"publisher","first-page":"158999","DOI":"10.1109\/access.2024.3487001","article-title":"Enhancing parkinson\u2019s disease detection and diagnosis: a survey of integrative approaches across diverse modalities","volume":"12","author":"Dhivyaa","year":"2024","journal-title":"IEEE Access"},{"key":"B9","doi-asserted-by":"publisher","first-page":"6534","DOI":"10.3390\/s20226534","article-title":"Motion capture data analysis in the instantaneous frequency-domain using hilbert-huang transform","volume":"20","author":"Dong","year":"2020","journal-title":"Sensors"},{"key":"B10","doi-asserted-by":"publisher","first-page":"4903","DOI":"10.1007\/s10994-022-06296-4","article-title":"A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning","volume":"113","author":"Elreedy","year":"2023","journal-title":"Mach. Learn."},{"key":"B11","first-page":"631","article-title":"Detection of parkinson\u2019s disease using machine learning techniques for voice and handwriting features","volume-title":"Advances in intelligent systems and computing","author":"Goel","year":"2020"},{"key":"B12","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1080\/14737175.2023.2229954","article-title":"Objective measurement versus clinician-based assessment for parkinson\u2019s disease","volume":"23","author":"Guerra","year":"2023","journal-title":"Expert Rev. Neurother."},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.36948\/ijfmr.2024.v06i06.30010","article-title":"Jayanna hallur explainable artificial intelligence (XAI) in healthcare: enhancing transparency and trust","volume":"6","author":"Inukonda","year":"2024","journal-title":"Int. J. For Multidiscip. Res."},{"key":"B14","doi-asserted-by":"publisher","first-page":"e25469","DOI":"10.1016\/j.heliyon.2024.e25469","article-title":"A review of machine learning and deep learning algorithms for parkinson\u2019s disease detection using handwriting and voice datasets","volume":"10","author":"Islam","year":"2024","journal-title":"Heliyon"},{"key":"B15","doi-asserted-by":"publisher","first-page":"6670","DOI":"10.3390\/s20226670","article-title":"A study of accelerometer and gyroscope measurements in physical life-log activities detection systems","volume":"20","author":"Jalal","year":"2020","journal-title":"Sensors"},{"key":"B16","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.future.2020.11.020","article-title":"Handwriting dynamics assessment using deep neural network for early identification of Parkinson\u2019s disease","volume":"117","author":"Kamran","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"B17","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1002\/ana.26905","article-title":"Towards a global view of Parkinson\u2019s disease genetics","volume":"95","author":"Khani","year":"2024","journal-title":"Ann. Neurology"},{"key":"B18","doi-asserted-by":"publisher","first-page":"633752","DOI":"10.3389\/fnagi.2021.633752","article-title":"Machine learning for the diagnosis of Parkinson\u2019s disease: a review of literature","volume":"13","author":"Mei","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"B19","first-page":"151","volume-title":"Is there a better way to assess parkinsonian motor symptoms? experimental and modelling approach","author":"Nair","year":"2021"},{"key":"B20","unstructured":"Effect of dynamic feature for human activity recognition using smartphone sensors\n          \n          \n            \n              Nakano\n              K.\n            \n            \n              Chakraborty\n              B.\n            \n          \n          \n          2023"},{"key":"B21","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/jtehm.2024.3355432","article-title":"NeuroDiag: software for automated diagnosis of parkinson\u2019s disease using handwriting","volume":"12","author":"Ngo","year":"2024","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/access.2021.3119035","article-title":"Exploiting spectral and cepstral handwriting features on diagnosing Parkinson\u2019s disease","volume":"9","author":"Nolazco-Flores","year":"2021","journal-title":"IEEE Access"},{"key":"B23","doi-asserted-by":"publisher","first-page":"S548","DOI":"10.1002\/mds.22062","article-title":"Functional organization of the basal ganglia: therapeutic implications for Parkinson\u2019s disease","volume":"23","author":"Obeso","year":"2008","journal-title":"Mov. Disord."},{"key":"B24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ssitcon62437.2024.10796148","article-title":"Detecting early signs of Parkinson\u2019s disease: a machine learning-based approach for diagnostic assistance","author":"Palsapure","year":"2024","journal-title":"First Int. Conf. Softw. Syst. Inf. Technol. (SSITCON)"},{"key":"B25","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3389\/fnhum.2014.00168","article-title":"Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences","volume":"8","author":"Peng","year":"2014","journal-title":"Front. Hum. Neurosci."},{"key":"B26","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.7507\/1001-5515.202303043","article-title":"Wearable devices: perspectives on assessing and monitoring human physiological status","volume":"40","author":"Peng","year":"2023","journal-title":"PubMed"},{"key":"B27","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1123\/mc.2013-0025","article-title":"Parkinson\u2019s disease is associated with greater regularity of repetitive voluntary movements","volume":"18","author":"Powell","year":"2014","journal-title":"Mot. Control"},{"key":"B28","doi-asserted-by":"publisher","first-page":"eabd7865","DOI":"10.1126\/scitranslmed.abd7865","article-title":"Smartwatch inertial sensors continuously monitor real-world motor fluctuations in parkinson\u2019s disease","volume":"13","author":"Powers","year":"2021","journal-title":"Sci. Transl. Med."},{"key":"B29","doi-asserted-by":"publisher","first-page":"72","DOI":"10.3389\/fncom.2018.00072","article-title":"Optimizing clinical assessments in Parkinson\u2019s disease through the use of wearable movement sensors and data driven modeling","volume":"12","author":"Ramdhani","year":"2018","journal-title":"Front. Comput. Neurosci."},{"key":"B30","doi-asserted-by":"publisher","first-page":"555","DOI":"10.3389\/fnins.2017.00555","article-title":"How wearable movement sensors can support Parkinson\u2019s disease diagnosis and treatment: a systematic review","volume":"11","author":"Rovini","year":"2017","journal-title":"Front. Neurosci."},{"key":"B31","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1186\/s12984-020-00684-4","article-title":"Role of data measurement characteristics in the accurate detection of Parkinson\u2019s disease symptoms using wearable movement sensors","volume":"17","author":"Shawen","year":"2020","journal-title":"J. Neuroengineering Rehabilitation"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2412.17849","article-title":"Parkinson disease detection based on In-Air dynamics feature extraction and selection using machine learning","author":"Shin","year":"2024","journal-title":"arXiv Cornell Univ."},{"key":"B33","doi-asserted-by":"publisher","first-page":"56","DOI":"10.31987\/ijict.7.2.285","article-title":"Enhancing transparency in healthcare machine learning models using shap and deeplift a methodological approach","volume":"7","author":"Shobeiri","year":"2024","journal-title":"Iraqi J. Inf. Commun. Technol."},{"key":"B34","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/S1474-4422(23)00109-6","article-title":"Assessment of heterogeneity among participants in the Parkinson\u2019s progression markers initiative cohort using \u03b1-Synuclein seed amplification: a cross-sectional study","volume":"22","author":"Siderowf","year":"2023","journal-title":"Lancet Neurology"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41531-023-00581-2","article-title":"Identification of motor progression in Parkinson\u2019s disease using wearable movement sensors and machine learning","volume":"9","author":"Sotirakis","year":"2023","journal-title":"Npj Parkinson\u2019s Dis."},{"key":"B36","doi-asserted-by":"publisher","first-page":"103","DOI":"10.3390\/biology9050103","article-title":"Clinical features of Parkinson\u2019s disease: the evolution of critical symptoms","volume":"9","author":"V\u00e1radi","year":"2020","journal-title":"Biology"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.13026\/m0w9-zx22","article-title":"The Parkinsons disease smartwatch dataset","author":"Varghese","year":"2024"},{"key":"B38","doi-asserted-by":"publisher","first-page":"107494","DOI":"10.1016\/j.engappai.2023.107494","article-title":"Robust language independent voice data driven Parkinson\u2019s disease detection","volume":"129","author":"Veetil","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"B39","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1038\/s41746-024-01163-z","article-title":"Wearable movement sensor-based quantitative gait analysis in Parkinson\u2019s disease patients with different motor subtypes","volume":"7","author":"Zhang","year":"2024","journal-title":"Npj Digit. Med."}],"container-title":["Frontiers in Physiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2025.1737585\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T06:37:23Z","timestamp":1767595043000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fphys.2025.1737585\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":39,"alternative-id":["10.3389\/fphys.2025.1737585"],"URL":"https:\/\/doi.org\/10.3389\/fphys.2025.1737585","relation":{},"ISSN":["1664-042X"],"issn-type":[{"value":"1664-042X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]},"article-number":"1737585"}}