[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126561-en":3,"doc-seo-126561-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126561,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Early Detection of Parkinson’s Disease using Motor Symptoms and Machine Learning","Parkinson's disease affects about 1 in 1000 people, especially those over 60, and reliable early diagnosis remains difficult due to the lack of objective tests. This work investigates early-occurring motor and gait parameters using an economical wearable-device concept. A subset of the Parkinson’s Progression Markers Initiative (PPMI) Gait dataset is used for feature selection with multiple machine-learning algorithms. Influential features support real-time early detection, achieving 91.9% accuracy.","Early Detection of Parkinson’s Disease using Motor symptoms and Machine Learning  \nPoojaa C 1, John Sahaya Rani Alex 1*  \n1 School ofElectronics Engineering, Vellore Institute of Technology, Chennai, India  \nABSTRACT  \nParkinson's disease (PD) has been found to affect 1 out of every 1000 people, being more inclined towards the population above 60 years. Leveraging wearable-systems to find accurate biomarkers for diagnosis has become the need of the hour, especially for aneurodegenerative condition like Parkinson’s. This work aims at focusing on early-occurring, common symptoms, such as motor and gait related parameters to arrive at a quantitative analysis on the feasibility of an economical and a robust wearable device. A subset of the Parkinson’s Progression Markers Initiative (PPMI), PPMI Gait dataset has been utilised for feature-selection after a thorough analysis with various Machine Learning algorithms. Identified influential features has then been used to test real-time data for early detection of Parkinson Syndrome, with a model accuracy of 91.9%  \nKeywords: Parkinson Disease, gait, motor symptoms, wearable device, PPMI.  \n1. INTRODUCTION  \nParkinson’s disease, a fast-growing condition, in elderly people, with the defining criteria including but not limited to rest tremor, rigidity, and impaired gait mostly along with the presence of bradykinesia. However, the clinical presentation is complicated and includes many non-motor symptoms as well [ 1] . Over 8.5 million people worldwide are estimated to have Parkinson's disease (PD) as of 2019. This global prevalence has doubled in the previous 25 years, making it one of the  \nmost common neurological disorders [2] . Oneof the most common and earliest symptoms is the motor symptoms in which people tend to develop a Parkinsonian Gait which is characterised by slow movement, bent posture, small and quick steps, and reduced swinging in their arms. Although these can be considered the pillars of diagnostic criteria, increasing gait difficulties and postural instability makes it hard to specify a distinguished biomarker [3] .  \nThe probable rate of misdiagnosis or an inaccurate diagnosis for a PD patient can be as high as 20%, especially if the diagnosis is done by a non-specialist since there is no objective test for it yet [4] . This brings into question the clinical assessments which can be influenced by various factors. In this case, wearable sensors, medical decision support tools, and research on specific neurodegenerative biomarkers can prove to be extremely useful and help in early diagnosis. With consumers embracing wearable technology, data acquired from these devices can be developed efficiently to be used as a secondary diagnostic tool. Moreover, automated analytics, when carried out effectively, can help physicians intervene sooner to diagnose the patient [5] .  \nIn this work, extensive Data Analysis was conducted on the Parkinson’s Progression Marker Initiative (PPMI) Data, which was then used to extract high-accuracy feature sets. Furthermore, an inexpensive but efficient wearable device system has been designed to capture data from patients to predict whether they have PD.  \n2. REVIEW OF LITERATURE  \nIn the past decade, the advancement of wearable sensors and devices has made it increasingly easier to adopt non-intrusive ways to monitor patients in controlled laboratory conditions as well as outside them. Additionally, the exponential growth of Machine Learning and Data Analytics techniques only makes it easier to develop robust and reliable systems for patients affected by various diseases. Significant work has been devoted to studying and detecting motor-movements and motion-related symptoms using wearable devices.  \nAsma Channa et al made a Systematic Review on the use of wearable devices in aiding the diagnosis, rehabilitation, assessment, and monitoring of patients with Parkinson's Disease or other neurocognitive disorders. The paper looks at 46 studies publi","cbCaideeZVSVCzSs","https://ap.wps.com/l/cbCaideeZVSVCzSs","pdf",444892,3,1,7,"English","en",105,"# Abstract\n# Introduction\n# Review of Literature","[{\"question\":\"Why is early detection of Parkinson’s disease important and challenging?\",\"answer\":\"Early diagnosis is needed because Parkinson’s presentation is complex and may include non-motor symptoms. Misdiagnosis can reach around 20% when assessed by non-specialists since no objective test exists yet.\"},{\"question\":\"What data and features are used in this study for detection?\",\"answer\":\"The study uses a subset of the Parkinson’s Progression Markers Initiative (PPMI) Gait dataset. It performs feature selection after analyzing multiple machine-learning algorithms to identify influential features.\"},{\"question\":\"What performance result is reported for the early detection model?\",\"answer\":\"The proposed approach for early detection using the selected features reports an accuracy of 91.9% for identifying Parkinson syndrome.\"}]","Early Detection of Parkinson’s Disease using Motor Symptoms and Machine Learning | PDF",1785933338,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"early-detection-of-parkinsons-disease-using-motor-symptoms-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/early-detection-of-parkinsons-disease-using-motor-symptoms-and-machine-learning/126561/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early detection of Parkinson’s disease important and challenging?","Question",{"text":76,"@type":77},"Early diagnosis is needed because Parkinson’s presentation is complex and may include non-motor symptoms. Misdiagnosis can reach around 20% when assessed by non-specialists since no objective test exists yet.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data and features are used in this study for detection?",{"text":81,"@type":77},"The study uses a subset of the Parkinson’s Progression Markers Initiative (PPMI) Gait dataset. It performs feature selection after analyzing multiple machine-learning algorithms to identify influential features.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance result is reported for the early detection model?",{"text":85,"@type":77},"The proposed approach for early detection using the selected features reports an accuracy of 91.9% for identifying Parkinson syndrome.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]