{"id":"https://openalex.org/W2810219609","doi":"https://doi.org/10.1109/fskd.2017.8393052","title":"Reliable and practical fall prediction using artificial neural network","display_name":"Reliable and practical fall prediction using artificial neural network","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2810219609","doi":"https://doi.org/10.1109/fskd.2017.8393052","mag":"2810219609"},"language":"en","primary_location":{"id":"doi:10.1109/fskd.2017.8393052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393052","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-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/A5024191541","display_name":"William Engel","orcid":null},"institutions":[{"id":"https://openalex.org/I32480017","display_name":"Florida Polytechnic University","ror":"https://ror.org/01e5mdj42","country_code":"US","type":"education","lineage":["https://openalex.org/I32480017"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"William Engel","raw_affiliation_strings":["Florida Polytechnic University, Lakeland, USA"],"affiliations":[{"raw_affiliation_string":"Florida Polytechnic University, Lakeland, USA","institution_ids":["https://openalex.org/I32480017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088432110","display_name":"Wei Ding","orcid":"https://orcid.org/0000-0002-3383-551X"},"institutions":[{"id":"https://openalex.org/I32480017","display_name":"Florida Polytechnic University","ror":"https://ror.org/01e5mdj42","country_code":"US","type":"education","lineage":["https://openalex.org/I32480017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Ding","raw_affiliation_strings":["Florida Polytechnic University, Lakeland, USA"],"affiliations":[{"raw_affiliation_string":"Florida Polytechnic University, Lakeland, USA","institution_ids":["https://openalex.org/I32480017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024191541"],"corresponding_institution_ids":["https://openalex.org/I32480017"],"apc_list":null,"apc_paid":null,"fwci":2.3743,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.90078311,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1867","last_page":"1871"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.998199999332428,"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":0.998199999332428,"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":0.9940999746322632,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9926999807357788,"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/torso","display_name":"Torso","score":0.7904360294342041},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6277653574943542},{"id":"https://openalex.org/keywords/mobile-phone","display_name":"Mobile phone","score":0.5956951379776001},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5786897540092468},{"id":"https://openalex.org/keywords/fall-prevention","display_name":"Fall prevention","score":0.5760725140571594},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5711168646812439},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5510532855987549},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.43295755982398987},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38323187828063965},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.36435163021087646},{"id":"https://openalex.org/keywords/poison-control","display_name":"Poison control","score":0.2772541046142578},{"id":"https://openalex.org/keywords/human-factors-and-ergonomics","display_name":"Human factors and ergonomics","score":0.13985612988471985},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13379693031311035},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.07862138748168945}],"concepts":[{"id":"https://openalex.org/C523889960","wikidata":"https://www.wikidata.org/wiki/Q160695","display_name":"Torso","level":2,"score":0.7904360294342041},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6277653574943542},{"id":"https://openalex.org/C2777421447","wikidata":"https://www.wikidata.org/wiki/Q17517","display_name":"Mobile phone","level":2,"score":0.5956951379776001},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5786897540092468},{"id":"https://openalex.org/C2776516907","wikidata":"https://www.wikidata.org/wiki/Q5432181","display_name":"Fall prevention","level":4,"score":0.5760725140571594},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5711168646812439},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5510532855987549},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.43295755982398987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38323187828063965},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.36435163021087646},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2772541046142578},{"id":"https://openalex.org/C166735990","wikidata":"https://www.wikidata.org/wiki/Q1750812","display_name":"Human factors and ergonomics","level":3,"score":0.13985612988471985},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13379693031311035},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.07862138748168945},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fskd.2017.8393052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fskd.2017.8393052","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.6399999856948853,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1264653525","https://openalex.org/W1585031011","https://openalex.org/W2015469044","https://openalex.org/W2030574544","https://openalex.org/W2041213259","https://openalex.org/W2047988905","https://openalex.org/W2051576728","https://openalex.org/W2062393923","https://openalex.org/W2088371259","https://openalex.org/W2088665546","https://openalex.org/W2108772542","https://openalex.org/W2109227767","https://openalex.org/W2120357670","https://openalex.org/W2121318675","https://openalex.org/W2131256443","https://openalex.org/W2137687977","https://openalex.org/W2156809737","https://openalex.org/W2162020867","https://openalex.org/W2169501720","https://openalex.org/W2268914071","https://openalex.org/W2286961399"],"related_works":["https://openalex.org/W4381953457","https://openalex.org/W2037557144","https://openalex.org/W2285739514","https://openalex.org/W2058088690","https://openalex.org/W2086597735","https://openalex.org/W2052143774","https://openalex.org/W1984495143","https://openalex.org/W4308297792","https://openalex.org/W2158185825","https://openalex.org/W1606408717"],"abstract_inverted_index":{"The":[0,33,47],"growing":[1],"elder":[2],"population":[3],"has":[4],"inspired":[5],"remarkable":[6],"research":[7],"in":[8,30,73,83],"the":[9,36,56,74,84],"prevention":[10],"of":[11,38,94],"fall":[12,19,62,104,119],"injuries.":[13],"A":[14],"reliable":[15],"technique":[16,34,106],"to":[17,53,61,97],"predict":[18,54],"incidence,":[20],"along":[21],"with":[22,63],"a":[23,64,109,113],"corresponding":[24],"mobile":[25],"phone":[26],"app,":[27],"is":[28,59],"proposed":[29],"this":[31,88],"paper.":[32],"combines":[35],"benefits":[37],"traditional":[39],"medical":[40,75],"history":[41,76],"based":[42,77],"paradigm":[43],"and":[44],"non-historical":[45,85],"paradigm.":[46,86],"app":[48],"analyzes":[49],"single":[50],"leg":[51,91],"motion":[52,92,96],"if":[55],"carrying":[57],"individual":[58],"about":[60],"desirably":[65],"practical":[66],"alert":[67,101],"time,":[68],"not":[69,79],"too":[70,80],"long":[71],"like":[72,82],"paradigm,":[78],"short":[81],"Furthermore,":[87],"approach":[89],"utilizes":[90],"instead":[93],"torso":[95],"gain":[98],"considerable":[99],"longer":[100],"time.":[102],"This":[103],"prediction":[105],"will":[107],"be":[108],"perfect":[110],"fit":[111],"into":[112],"real":[114],"time":[115],"automated":[116],"system":[117],"for":[118],"prevention.":[120]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
