{"id":"https://openalex.org/W4385187201","doi":"https://doi.org/10.1109/iwcmc58020.2023.10182728","title":"Efficient Fall Detection using Bidirectional Long Short-Term Memory","display_name":"Efficient Fall Detection using Bidirectional Long Short-Term Memory","publication_year":2023,"publication_date":"2023-06-19","ids":{"openalex":"https://openalex.org/W4385187201","doi":"https://doi.org/10.1109/iwcmc58020.2023.10182728"},"language":"en","primary_location":{"id":"doi:10.1109/iwcmc58020.2023.10182728","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iwcmc58020.2023.10182728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Wireless Communications and Mobile Computing (IWCMC)","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/A5076580590","display_name":"Gael S. Mubibya","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gael S. Mubibya","raw_affiliation_strings":["Universit&#x00E9; de Moncton,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; de Moncton,Department of Computer Science","institution_ids":["https://openalex.org/I154799132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111912045","display_name":"Jalal Almhana","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jalal Almhana","raw_affiliation_strings":["Universit&#x00E9; de Moncton,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; de Moncton,Department of Computer Science","institution_ids":["https://openalex.org/I154799132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053727169","display_name":"Zikuan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I106938459","display_name":"University of New Brunswick","ror":"https://ror.org/05nkf0n29","country_code":"CA","type":"education","lineage":["https://openalex.org/I106938459"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Zikuan Liu","raw_affiliation_strings":["University of New-Brunswick"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New-Brunswick","institution_ids":["https://openalex.org/I106938459"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.7859,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73887608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"983","last_page":"988"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9997000098228455,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.9765999913215637,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9664000272750854,"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/computer-science","display_name":"Computer science","score":0.739175021648407},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6637091636657715},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5368751883506775},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5149453282356262},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5027875900268555},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49982666969299316},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.4119386076927185},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3531856834888458},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.08647719025611877}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.739175021648407},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6637091636657715},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5368751883506775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5149453282356262},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5027875900268555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49982666969299316},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.4119386076927185},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3531856834888458},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.08647719025611877},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwcmc58020.2023.10182728","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iwcmc58020.2023.10182728","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Wireless Communications and Mobile Computing (IWCMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2035675395","https://openalex.org/W2145026237","https://openalex.org/W2146291834","https://openalex.org/W2164147466","https://openalex.org/W2213087239","https://openalex.org/W2482368507","https://openalex.org/W2565299706","https://openalex.org/W2622527139","https://openalex.org/W2738611661","https://openalex.org/W2797632833","https://openalex.org/W2891151898","https://openalex.org/W2898658045","https://openalex.org/W2945362095","https://openalex.org/W2950883524","https://openalex.org/W2959763773","https://openalex.org/W2974089011","https://openalex.org/W2979637742","https://openalex.org/W2980068604","https://openalex.org/W2980700634","https://openalex.org/W2988129624","https://openalex.org/W3003341797","https://openalex.org/W3033490827","https://openalex.org/W3081186834","https://openalex.org/W3082661649","https://openalex.org/W3096092407","https://openalex.org/W3174230873","https://openalex.org/W3216540786","https://openalex.org/W4292230836","https://openalex.org/W4302575380"],"related_works":["https://openalex.org/W2765080098","https://openalex.org/W2385749422","https://openalex.org/W2009888974","https://openalex.org/W2355290145","https://openalex.org/W2353465659","https://openalex.org/W3023105672","https://openalex.org/W2355539379","https://openalex.org/W4231410700","https://openalex.org/W4237770763","https://openalex.org/W2347752811"],"abstract_inverted_index":{"Falls":[0,104],"are":[1],"one":[2],"of":[3,8,76,93,120,135,210],"the":[4,11,21,30,77,91,133,151,181],"most":[5,75],"common":[6],"causes":[7],"injury":[9],"among":[10],"elderly.":[12],"As":[13,68],"a":[14,98,169],"result,":[15],"fall":[16,56,71,81,95,100,136,146,225],"detection":[17,82],"has":[18,110],"received":[19],"in":[20,160,164],"last":[22],"decade":[23],"considerable":[24],"attention":[25],"from":[26,36],"both":[27,191],"academia":[28],"and":[29,105,141,147,155,183,193,212,216,219,221,226],"healthcare":[31],"industry.":[32],"Accelerometer":[33],"data,":[34],"collected":[35],"simulated":[37,118],"falls,":[38],"were":[39],"widely":[40],"used":[41,61,84,159],"with":[42,51,214],"classical":[43,152],"machine":[44],"learning":[45],"(ML)":[46],"algorithms":[47,154],"as":[48,50,186],"well":[49],"threshold-based":[52,156],"methods":[53,157],"to":[54,62,131,145],"identify":[55],"situations":[57],"that":[58,205],"can":[59],"be":[60],"launch":[63],"an":[64,208],"alert":[65],"for":[66,224],"help.":[67],"collecting":[69],"real":[70,94],"data":[72,86],"is":[73],"challenging,":[74],"research":[78,162],"papers":[79],"on":[80],"have":[83],"limited":[85],"which":[87,143,176],"do":[88],"not":[89],"reflect":[90],"complexity":[92],"situations.":[96],"Fortunately,":[97],"comprehensive":[99],"dataset":[101,115,130],"called":[102],"\u201cSimulated":[103],"Daily":[106],"Living":[107],"Activities":[108],"Dataset\u201d":[109],"recently":[111],"become":[112],"available.":[113],"This":[114],"includes":[116],"1827":[117],"falls":[119],"20":[121],"different":[122],"types.":[123],"In":[124],"this":[125,129,165],"paper,":[126,166],"we":[127,167,177],"use":[128],"evaluate":[132],"possibility":[134],"detection,":[137],"more":[138],"precisely,":[139],"impact":[140,182],"pre-impact":[142,184],"correspond":[144],"pre-fall,":[148],"respectively.":[149,229],"Unlike":[150],"ML":[153],"commonly":[158],"previous":[161],"works,":[163],"implement":[168],"bidirectional":[170],"long":[171],"short-term":[172],"memory":[173],"(Bi-LSTM)":[174],"algorithm":[175],"believe":[178],"better":[179],"reflects":[180],"context":[185],"it":[187],"takes":[188],"into":[189],"consideration":[190],"backward":[192],"forward":[194],"sequence":[195],"information":[196],"at":[197],"every":[198],"time":[199],"step.":[200],"Our":[201],"experimental":[202],"results":[203,231],"showed":[204],"Bi-LSTM":[206],"achieves":[207],"accuracy":[209],"99.97%":[211],"99.95%,":[213],"99.80%":[215],"99.30%":[217],"sensitivity,":[218],"100%":[220],"99.99%":[222],"specificity":[223],"pre-fall":[227],"detections,":[228],"These":[230],"largely":[232],"exceed":[233],"previously":[234],"published":[235],"results.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
