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This study tests whether wearable sensor data combined with machine learning using automatic feature selection can estimate the MDS-UPDRS-III clinical rating and monitor longitudinal motor progression in Parkinson’s disease. Seventy-four participants were assessed seven times over 15 months with inertial sensors capturing walking and postural sway tasks. A Random Forest approach using automatically selected features most accurately estimated MDS-UPDRS-III and detected significant progression at group level, improving on conventional rating scales.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/identication-of-motor-progression-in-parkinsons-disease-using-wearable-sensors-and-machine-learning/290756/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/identication-of-motor-progression-in-parkinsons-disease-using-wearable-sensors-and-machine-learning/290756.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of the study?","Question",{"text":113,"@type":114},"To determine whether wearable sensor data combined with machine learning and automatic feature selection can estimate the MDS-UPDRS-III score and monitor motor symptom progression over time in people with Parkinson’s disease.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were participants assessed in the longitudinal design?",{"text":118,"@type":114},"Seventy-four patients visited the lab seven times at 3-month intervals, during which walking and postural sway tasks were recorded using six inertial measurement unit sensors.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning method performed best, and what did it detect?",{"text":122,"@type":114},"The Random Forest model provided the most accurate estimation of MDS-UPDRS-III among the tested algorithms. Its estimates showed statistically significant motor symptom progression within 15 months, while MDS-UPDRS-III did not capture change.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290756,1789642511,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":46,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","[www.nature.com/npjparkd](www.nature.com/npjparkd)  \nARTICLE OPEN   \nIdentification of motor progression in Parkinson’s disease using wearable sensors and machine learning  \nCharalampos Sotirakis 1, Zi Su1, Maksymilian A. Brzezicki 1, Niall Conway1, Lionel Tarassenko2, James J. FitzGerald1,3 and Chrystalina A. Antoniades 1 ✉  \n\n|  | Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson’s Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic featureselection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson’s Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic |  |\n| --- | --- | --- |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n|  |  |  |\n| accuracy. |  |  |\n|  | npj Parkinson’s Disease (2023)9:142; [https://doi.org/10.1038/s41531-023-00581-2](https://doi.org/10.1038/s41531-023-00581-2) |  |\n|  |  |  |\n\nINTRODUCTION  \nThe progression of Parkinson’s disease (PD) is currently monitored using clinical rating scales which are used to assess the cardinal motor and non-motor symptoms. The current gold standard is the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)1.  \nAccurate assessment with rating scales depends on the clinician’s experience and interpretation may be complicated by inter-rater disagreement2. Rating scale data are coarse-grained, and on an ordinal rather than interval scale. Collectively, these factors introduce variability and delay the confident detection of progression; at the group level in clinical studies, they may restrict the types of statistical analysis that can be performed. Objective measures that are on a continuous interval scale would be highly desirable both for the assessment of individual patients in clinical practice and for measuring the efficacy of therapeutic interventions in clinical trials.  \nIn recent years, wearable sensors have emerged as a promising tool for quantitative characterisation of motor status in PD3,4. The portability and affordability of wearable sensors make it possible to assess the spatio-temporal features of walking and balance in the laboratory or clinic, and remotely at the comfort of patients’homes. The ultimate vision is to use these detailed and personalised kinematic measurements to individualise disease diagnostic and prognostic tools and measure the effectiveness of treatment.  \nMost wearable devices used to monitor PD patients output ","cbCaifP7KivVxzky","https://ap.wps.com/l/cbCaifP7KivVxzky","pdf",1829663,"English","# Introduction\n## Clinical rating scales and their limitations\n## Wearable sensors for quantitative motor assessment\n## Machine learning and longitudinal tracking rationale","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To determine whether wearable sensor data combined with machine learning and automatic feature selection can estimate the MDS-UPDRS-III score and monitor motor symptom progression over time in people with Parkinson’s disease.\"},{\"question\":\"How were participants assessed in the longitudinal design?\",\"answer\":\"Seventy-four patients visited the lab seven times at 3-month intervals, during which walking and postural sway tasks were recorded using six inertial measurement unit sensors.\"},{\"question\":\"Which machine learning method performed best, and what did it detect?\",\"answer\":\"The Random Forest model provided the most accurate estimation of MDS-UPDRS-III among the tested algorithms. Its estimates showed statistically significant motor symptom progression within 15 months, while MDS-UPDRS-III did not capture change.\"}]","Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning | PDF"]