{"id":"https://openalex.org/W7130409873","doi":"https://doi.org/10.1109/tencon66050.2025.11375058","title":"Predicting Fall Risk in Parkinson's Patients Through Sequential Gait Analysis and Machine Learning","display_name":"Predicting Fall Risk in Parkinson's Patients Through Sequential Gait Analysis and Machine Learning","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7130409873","doi":"https://doi.org/10.1109/tencon66050.2025.11375058"},"language":"en","primary_location":{"id":"doi:10.1109/tencon66050.2025.11375058","doi":"https://doi.org/10.1109/tencon66050.2025.11375058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375058","pdf_url":null,"source":{"id":"https://openalex.org/S4363607986","display_name":"IEEE Region 10 Conference (TENCON)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126300099","display_name":"Gaurav Sharma","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I3129773123","display_name":"Bennett University","ror":"https://ror.org/00an5hx75","country_code":"IN","type":"education","lineage":["https://openalex.org/I3129773123"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gaurav Sharma","raw_affiliation_strings":["Bennett University,Greater Noida,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bennett University,Greater Noida,India","institution_ids":["https://openalex.org/I3129773123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126303653","display_name":"Nehvanshika","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nehvanshika","raw_affiliation_strings":["Indian Institute of Information Techonology,Lucknow,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Techonology,Lucknow,India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101808189","display_name":"Niharika Anand","orcid":"https://orcid.org/0000-0003-2939-1298","observed_orcids":["https://orcid.org/0000-0003-2939-1298"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niharika Anand","raw_affiliation_strings":["Indian Institute of Information Techonology,Lucknow,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Techonology,Lucknow,India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5146891277","display_name":"Gaurisha Singh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gaurisha Singh","raw_affiliation_strings":["Indian Institute of Information Techonology,Lucknow,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Information Techonology,Lucknow,India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1888","last_page":"1892"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fall-risk-prediction","display_name":"fall risk prediction","score":0.8510000109672546},{"id":"https://openalex.org/keywords/parkinson-s-disease","display_name":"Parkinson's disease","score":1.0},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"gait analysis","score":0.9430000185966492},{"id":"https://openalex.org/keywords/wearable-sensors","display_name":"wearable sensors","score":0.7739999890327454},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"inertial measurement unit","score":0.9990000128746033},{"id":"https://openalex.org/keywords/stride-variability","display_name":"stride-to-stride variability","score":0.7590000033378601},{"id":"https://openalex.org/keywords/gait-symmetry","display_name":"gait symmetry","score":0.7799999713897705},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.6000000238418579},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.8519999980926514},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9110000133514404},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.6510000228881836},{"id":"https://openalex.org/keywords/real-time-monitoring","display_name":"real-time monitoring","score":0.5410000085830688},{"id":"https://openalex.org/keywords/home-monitoring","display_name":"home monitoring","score":0.48500001430511475}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7670000195503235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.75},{"id":"https://openalex.org/C18007350","wikidata":"https://www.wikidata.org/wiki/Q7394815","display_name":"STRIDE","level":2,"score":0.6757000088691711},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6223999857902527},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.5952000021934509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.59170001745224},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.5105999708175659},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.4584999978542328},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.40070000290870667},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.3059000074863434},{"id":"https://openalex.org/C199190896","wikidata":"https://www.wikidata.org/wiki/Q3509276","display_name":"Learning classifier system","level":3,"score":0.2849000096321106},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2574000060558319},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon66050.2025.11375058","doi":"https://doi.org/10.1109/tencon66050.2025.11375058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375058","pdf_url":null,"source":{"id":"https://openalex.org/S4363607986","display_name":"IEEE Region 10 Conference (TENCON)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.9476000070571899}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1920394150","https://openalex.org/W2011702367","https://openalex.org/W2013337604","https://openalex.org/W2087048290","https://openalex.org/W2105382574","https://openalex.org/W2128290688","https://openalex.org/W2144954419","https://openalex.org/W2148927972","https://openalex.org/W2349408014","https://openalex.org/W2597642773","https://openalex.org/W2765167702","https://openalex.org/W2780291132","https://openalex.org/W2783082511","https://openalex.org/W2914029965","https://openalex.org/W2996654865","https://openalex.org/W3003958915","https://openalex.org/W3008571415","https://openalex.org/W3087099853","https://openalex.org/W3207340533","https://openalex.org/W4306831872","https://openalex.org/W4391092776","https://openalex.org/W4402401676","https://openalex.org/W4404914502"],"related_works":["https://openalex.org/W4405079421","https://openalex.org/W4411367603","https://openalex.org/W4306831872","https://openalex.org/W4409967440","https://openalex.org/W7153561550","https://openalex.org/W4411507146","https://openalex.org/W4295498041","https://openalex.org/W3105955104","https://openalex.org/W2800341923","https://openalex.org/W7135036781"],"abstract_inverted_index":{"Falls":[0],"pose":[1],"a":[2,95,108],"critical":[3],"threat":[4],"for":[5],"patients":[6],"with":[7,100,113,150,189],"Parkinson's":[8],"disease,":[9],"as":[10],"difficulties":[11],"in":[12,171,184],"walking":[13],"and":[14,31,38,52,60,80,89,105,115,119,144,186,196],"balance":[15],"greatly":[16],"increase":[17],"the":[18,158,167,176],"likelihood":[19],"of":[20,111,117,139,142,146,160,169,178],"falling.":[21],"This":[22],"study":[23],"aims":[24],"to":[25,69,85,165],"improve":[26],"fall":[27],"risk":[28,168],"prediction":[29],"accuracy":[30,110],"computational":[32],"efficiency":[33],"using":[34],"wearable":[35],"sensor":[36],"data":[37,46],"advanced":[39],"learning":[40,57,62,163],"models.":[41],"We":[42],"analyze":[43],"real-world":[44],"gait":[45,76],"collected":[47],"via":[48],"inertial":[49],"measurement":[50],"units":[51],"implement":[53],"both":[54],"classical":[55,93],"machine":[56],"(ML)":[58],"techniques":[59],"deep":[61,162],"frameworks":[63],"aimed":[64],"at":[65],"recognizing":[66],"those":[67],"susceptible":[68],"falls.":[70],"Feature":[71,102],"extraction":[72],"strategies":[73],"focusing":[74],"on":[75],"speed,":[77],"stride":[78,130],"variability,":[79],"symmetry":[81],"measures":[82],"were":[83],"applied":[84],"enhance":[86],"model":[87,126],"performance":[88,155],"reduce":[90],"dimensionality.":[91],"Among":[92],"approaches,":[94],"Random":[96],"Forest":[97],"classifier":[98],"optimized":[99],"Recursive":[101],"Elimination":[103],"(RFE)":[104],"XGBoost":[106],"achieved":[107],"balanced":[109,137],"77%,":[112],"sensitivity":[114,141],"specificity":[116,145],"70%":[118],"84%,":[120],"respectively.":[121],"A":[122],"long-short-term":[123],"memory":[124],"(LSTM)":[125],"that":[127],"uses":[128],"temporal":[129],"sequences":[131],"significantly":[132],"outperformed":[133],"traditional":[134],"models,":[135],"achieving":[136],"precision":[138],"95.97%,":[140],"93.29%":[143],"98.65%.":[147],"Comparative":[148],"analysis":[149],"baseline":[151],"literature":[152],"demonstrated":[153],"substantial":[154],"gains,":[156],"highlighting":[157],"ability":[159],"time-sensitive":[161],"methods":[164],"predict":[166],"falls":[170],"PD.":[172],"The":[173],"findings":[174],"underscore":[175],"feasibility":[177],"deploying":[179],"robust,":[180],"real-time":[181],"predictive":[182],"systems":[183],"clinical":[185],"home-monitoring":[187],"environments,":[188],"future":[190],"work":[191],"directed":[192],"towards":[193],"multimodal":[194],"integration":[195],"scalable":[197],"deployment.":[198]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-19T00:00:00"}