{"id":"https://openalex.org/W7116956504","doi":"https://doi.org/10.1109/tmc.2025.3647110","title":"Fall Risk Prediction Method Based on Human Electrostatic Field and Stacking Ensemble Learning Algorithm","display_name":"Fall Risk Prediction Method Based on Human Electrostatic Field and Stacking Ensemble Learning Algorithm","publication_year":2025,"publication_date":"2025-12-23","ids":{"openalex":"https://openalex.org/W7116956504","doi":"https://doi.org/10.1109/tmc.2025.3647110"},"language":null,"primary_location":{"id":"doi:10.1109/tmc.2025.3647110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3647110","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Mobile Computing","raw_type":"journal-article"},"type":"article","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/A5038588772","display_name":"Sichao Qin","orcid":"https://orcid.org/0000-0002-8896-372X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sichao Qin","raw_affiliation_strings":["School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8896-372X","affiliations":[{"raw_affiliation_string":"School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100574046","display_name":"Jiaao Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210109015","display_name":"Analysis and Testing Centre","ror":"https://ror.org/01m9scr97","country_code":"CN","type":"facility","lineage":["https://openalex.org/I107851509","https://openalex.org/I4210109015","https://openalex.org/I4210127390","https://openalex.org/I4210151987"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaao Yan","raw_affiliation_strings":["Yichang Testing Technique Research Institute, Yichang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yichang Testing Technique Research Institute, Yichang, China","institution_ids":["https://openalex.org/I4210109015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112461987","display_name":"Ziyi Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyi Jiao","raw_affiliation_strings":["School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-7846-3841","affiliations":[{"raw_affiliation_string":"School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121040433","display_name":"Weijie Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weijie Yuan","raw_affiliation_strings":["School of Syster Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-2158-0046","affiliations":[{"raw_affiliation_string":"School of Syster Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5121113170","display_name":"Xi Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9734-1009","affiliations":[{"raw_affiliation_string":"School of Mechanical and Electrical Engineering, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.4791,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91641665,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"25","issue":"3","first_page":"4443","last_page":"4454"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.8902000188827515,"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":0.8902000188827515,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.041600000113248825,"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"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.011500000022351742,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gait","display_name":"Gait","score":0.7152000069618225},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6093999743461609},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5306000113487244},{"id":"https://openalex.org/keywords/gait-analysis","display_name":"Gait analysis","score":0.4740000069141388},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.47130000591278076},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.44999998807907104},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3874000012874603},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.3727000057697296}],"concepts":[{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7152000069618225},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6283000111579895},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6093999743461609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6092000007629395},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5306000113487244},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48190000653266907},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.4740000069141388},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.47130000591278076},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43810001015663147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3874000012874603},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.3727000057697296},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C191544260","wikidata":"https://www.wikidata.org/wiki/Q1238630","display_name":"Lyapunov exponent","level":3,"score":0.3003999888896942},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2840000092983246}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmc.2025.3647110","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmc.2025.3647110","pdf_url":null,"source":{"id":"https://openalex.org/S69141925","display_name":"IEEE Transactions on Mobile Computing","issn_l":"1536-1233","issn":["1536-1233","1558-0660","2161-9875"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Mobile Computing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.472010999917984}],"awards":[{"id":"https://openalex.org/G13836113","display_name":null,"funder_award_id":"GZB20240936","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7244855145","display_name":null,"funder_award_id":"2025M774209","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2032685497","https://openalex.org/W2034878282","https://openalex.org/W2040412667","https://openalex.org/W2056020968","https://openalex.org/W2063255558","https://openalex.org/W2075692911","https://openalex.org/W2076245909","https://openalex.org/W2088371259","https://openalex.org/W2093071643","https://openalex.org/W2154338003","https://openalex.org/W2158190397","https://openalex.org/W2160924004","https://openalex.org/W2322968233","https://openalex.org/W2335125833","https://openalex.org/W2594435757","https://openalex.org/W2753777934","https://openalex.org/W2784837049","https://openalex.org/W2789525912","https://openalex.org/W2801331634","https://openalex.org/W2805968309","https://openalex.org/W2894551204","https://openalex.org/W2938949242","https://openalex.org/W2954938449","https://openalex.org/W3026359008","https://openalex.org/W3029924594","https://openalex.org/W3087099853","https://openalex.org/W3129075715","https://openalex.org/W3132947666","https://openalex.org/W3151293530","https://openalex.org/W3203023227","https://openalex.org/W3205329434","https://openalex.org/W4224109388","https://openalex.org/W4256717407","https://openalex.org/W4306831872","https://openalex.org/W4317794926","https://openalex.org/W4362496427","https://openalex.org/W4365483659","https://openalex.org/W4376464606","https://openalex.org/W4386737405","https://openalex.org/W4392567210","https://openalex.org/W4400770844","https://openalex.org/W4401748666","https://openalex.org/W4402125693","https://openalex.org/W4404609353","https://openalex.org/W4406094139"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"fall":[1,29,219],"risk":[2,30,156,220],"prediction":[3,31,157,221],"is":[4,56,62,69,147,205],"crucial":[5],"for":[6,47,99,218],"early":[7],"intervention":[8],"and":[9,17,28,39,58,83,103,117,128,131,166,176,229],"prevention,":[10],"effectively":[11],"reducing":[12],"the":[13,18,35,52,124,151,172,177,186,203,225,231],"incidence":[14],"of":[15,89,210,227,233],"falls":[16,228],"associated":[19],"harm.":[20],"This":[21,212],"paper":[22],"proposes":[23],"a":[24,87,195,215],"non-contact":[25],"gait":[26,48,66,74,109,112,120,132],"detection":[27,49],"method":[32,140],"based":[33,50,114,122,141,159,184],"on":[34,51,115,123,142,160,185],"human":[36,53,101],"electrostatic":[37,54,65,73],"field":[38,55],"Stacking":[40,196],"ensemble":[41,197],"learning":[42,198],"algorithm.":[43],"A":[44,136],"theoretical":[45],"model":[46,199],"established,":[57],"an":[59,208],"experimental":[60],"scheme":[61],"designed.":[63],"The":[64,181],"measurement":[67],"system":[68],"used":[70,148],"to":[71,149],"collect":[72],"signals":[75],"from":[76],"healthy":[77,80],"young":[78],"individuals,":[79,82],"elderly":[81,84],"individuals":[85],"with":[86],"history":[88],"falls.":[90],"Gait":[91],"features,":[92],"including":[93],"28-dimensional":[94],"quantifiable":[95],"characteristics,":[96],"are":[97,168],"proposed":[98],"evaluating":[100],"balance":[102],"motor":[104],"abilities,":[105],"covering":[106],"four":[107],"aspects:":[108],"time":[110,133],"parameters,":[111],"symmetry":[113],"ratios":[116],"signal":[118],"similarity,":[119],"stability":[121],"maximum":[125],"Lyapunov":[126],"exponent":[127],"entropy":[129],"information,":[130],"parameter":[134],"variability.":[135],"hybrid":[137],"feature":[138,153,174,179,188],"reduction":[139],"Particle":[143],"Swarm":[144],"Optimization":[145],"(PSO)":[146],"obtain":[150],"optimal":[152,178,187],"subset.":[154,180],"Fall":[155],"models":[158],"single":[161,182],"classifiers":[162],"(DT,":[163],"SVM,":[164],"KNN,":[165],"NB)":[167],"constructed":[169],"using":[170,200],"both":[171],"original":[173],"set":[175],"classifier":[183],"subset":[189],"achieves":[190],"better":[191],"classification":[192],"performance.":[193],"Furthermore,":[194],"LightGBM":[201],"as":[202],"meta-learner":[204],"developed,":[206],"achieving":[207],"accuracy":[209],"97.78%.":[211],"study":[213],"provides":[214],"novel":[216],"approach":[217],"that":[222],"can":[223],"predict":[224],"likelihood":[226],"reduce":[230],"probability":[232],"their":[234],"occurrence.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-23T00:00:00"}