{"id":"https://openalex.org/W4310664652","doi":"https://doi.org/10.1109/jsen.2022.3213814","title":"A Novel Feature Extraction Method for Preimpact Fall Detection System Using Deep Learning and Wearable Sensors","display_name":"A Novel Feature Extraction Method for Preimpact Fall Detection System Using Deep Learning and Wearable Sensors","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4310664652","doi":"https://doi.org/10.1109/jsen.2022.3213814"},"language":"en","primary_location":{"id":"doi:10.1109/jsen.2022.3213814","doi":"https://doi.org/10.1109/jsen.2022.3213814","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsen.2022.3213814","pdf_url":null,"source":{"id":"https://openalex.org/S189694085","display_name":"IEEE Sensors Journal","issn_l":"1530-437X","issn":["1530-437X","1558-1748","2379-9153"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-2","ki-jl-2","norway-2","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310321027","host_organization_name":"IEEE Sensors Council","host_organization_lineage":["https://openalex.org/P4310321027","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Sensors Council","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 Sensors Journal","raw_type":"journal-article"},"volume":null,"type":"article","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/A5041103619","display_name":"Rahul Jain","orcid":"https://orcid.org/0000-0002-4969-6956","observed_orcids":["https://orcid.org/0000-0002-4969-6956"]},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rahul Jain","raw_affiliation_strings":["CSE Department, MANIT, Bhopal, India"],"raw_orcid":"https://orcid.org/0000-0002-4969-6956","affiliations":[{"raw_affiliation_string":"CSE Department, MANIT, Bhopal, India","institution_ids":["https://openalex.org/I91277730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064062259","display_name":"Vijay Bhaskar Semwal","orcid":"https://orcid.org/0000-0003-0767-6057","observed_orcids":["https://orcid.org/0000-0003-0767-6057"]},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vijay Bhaskar Semwal","raw_affiliation_strings":["CSE Department, MANIT, Bhopal, India"],"raw_orcid":"https://orcid.org/0000-0003-0767-6057","affiliations":[{"raw_affiliation_string":"CSE Department, MANIT, Bhopal, India","institution_ids":["https://openalex.org/I91277730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91277730"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":null,"fwci":6.9625,"has_fulltext":false,"cited_by_count":125,"citation_normalized_percentile":{"value":0.98237053,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"22","issue":"23","first_page":"22943","last_page":"22951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9970999956130981,"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":0.9970999956130981,"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":0.002300000051036477,"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":9.999999747378752e-05,"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/fall-detection","display_name":"fall detection","score":0.7540000081062317},{"id":"https://openalex.org/keywords/wearable-sensors","display_name":"wearable sensors","score":0.8029999732971191},{"id":"https://openalex.org/keywords/automatic-feature-extraction","display_name":"automatic feature extraction","score":0.828000009059906},{"id":"https://openalex.org/keywords/temporal-characteristics","display_name":"temporal features","score":0.8759999871253967},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8019999861717224},{"id":"https://openalex.org/keywords/long-short-term-memory-networks","display_name":"long short-term memory networks","score":0.9549999833106995},{"id":"https://openalex.org/keywords/elderly-healthcare","display_name":"elderly healthcare","score":0.7039999961853027},{"id":"https://openalex.org/keywords/internet-of-medical-things","display_name":"Internet of Medical Things","score":0.8149999976158142},{"id":"https://openalex.org/keywords/humanoid-robots","display_name":"humanoid robots","score":0.781000018119812},{"id":"https://openalex.org/keywords/robotic-exoskeleton","display_name":"robotic exoskeleton","score":0.7950000166893005},{"id":"https://openalex.org/keywords/dataset-fusion","display_name":"dataset fusion","score":0.7089999914169312}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7358185052871704},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7307115793228149},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6937720775604248},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.643102765083313},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6275883913040161},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5931881666183472},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5221734046936035},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4831402003765106},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.4733448326587677},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43935665488243103},{"id":"https://openalex.org/C2909930183","wikidata":"https://www.wikidata.org/wiki/Q333495","display_name":"Accidental fall","level":2,"score":0.418935090303421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3349868357181549},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.16247892379760742},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.1152421236038208},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jsen.2022.3213814","doi":"https://doi.org/10.1109/jsen.2022.3213814","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsen.2022.3213814","pdf_url":null,"source":{"id":"https://openalex.org/S189694085","display_name":"IEEE Sensors Journal","issn_l":"1530-437X","issn":["1530-437X","1558-1748","2379-9153"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-c","jufo-2","ki-jl-2","norway-2","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310321027","host_organization_name":"IEEE Sensors Council","host_organization_lineage":["https://openalex.org/P4310321027","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Sensors Council","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 Sensors Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6240000128746033,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322724","display_name":"Ministry of Education, India","ror":"https://ror.org/048xjjh50"},{"id":"https://openalex.org/F4320334771","display_name":"Science and Engineering Research Board","ror":"https://ror.org/03ffdsr55"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2050294282","https://openalex.org/W2069143585","https://openalex.org/W2071151816","https://openalex.org/W2089937542","https://openalex.org/W2099687935","https://openalex.org/W2143925687","https://openalex.org/W2319308093","https://openalex.org/W2543632018","https://openalex.org/W2766752841","https://openalex.org/W2797186212","https://openalex.org/W2802084776","https://openalex.org/W2939670326","https://openalex.org/W2941398656","https://openalex.org/W3002417609","https://openalex.org/W3011347095","https://openalex.org/W3026359008","https://openalex.org/W3172093704","https://openalex.org/W3185327951","https://openalex.org/W3212068445","https://openalex.org/W3216540786","https://openalex.org/W3217657661","https://openalex.org/W4200617618"],"related_works":["https://openalex.org/W4327518065","https://openalex.org/W4399485220","https://openalex.org/W4417131356","https://openalex.org/W4352981610","https://openalex.org/W4323536255","https://openalex.org/W2939670326","https://openalex.org/W4366090408","https://openalex.org/W4399059246","https://openalex.org/W4312645466","https://openalex.org/W3111543755"],"abstract_inverted_index":{"Fall":[0,125],"detection":[1,32,217,228],"and":[2,9,103,133,191,214,224,233,241],"prevention":[3],"are":[4],"crucial":[5],"in":[6,122],"elderly":[7],"healthcare":[8],"humanoid":[10,231],"robotic":[11],"research":[12],"as":[13,238],"they":[14],"help":[15],"mitigate":[16],"the":[17,43,61,96,112,124,149,154,177,181,194,202,205,220],"damaging":[18],"after-effects":[19],"of":[20,42,53,82,98,131,136,141,145,156,169,180,204],"falls.":[21,142],"In":[22],"this":[23],"work,":[24],"we":[25,66],"have":[26,67],"presented":[27],"a":[28,37,49,129,170],"deep-learning-based":[29],"preimpact":[30],"fall":[31,38,44,84,187,216,227],"system":[33],"(FDS)":[34],"that":[35,74,148],"detects":[36],"within":[39],"0.5":[40,54],"s":[41,55],"initiation":[45],"phase,":[46],"thus":[47],"providing":[48],"sufficient":[50],"lead":[51],"time":[52,179],"which":[56,159],"is":[57,109,160,173,198],"far":[58,161],"better":[59],"than":[60],"state-of-the-art.":[62],"To":[63],"achieve":[64],"this,":[65],"developed":[68],"an":[69,134,165],"automatic":[70],"feature":[71],"extraction":[72],"methodology":[73],"can":[75,209],"extract":[76],"temporal":[77,114],"features":[78],"from":[79],"all":[80],"types":[81,140],"human":[83],"data":[85],"collected":[86],"using":[87],"wearable":[88],"sensors.":[89],"A":[90,143,167],"deep":[91],"neural":[92,100],"classifier":[93,117],"based":[94],"on":[95,111],"ensemble":[97],"convolutional":[99],"networks":[101,107],"(CNNs)":[102],"long":[104],"short-term":[105],"memory":[106],"(LSTMs)":[108],"trained":[110],"extracted":[113],"features.":[115],"The":[116],"has":[118,151],"performed":[119],"exceptionally":[120],"well":[121],"detecting":[123],"Initiation":[126],"phase":[127],"with":[128],"sensitivity":[130,144],"99.24%":[132,146],"F1-score":[135],"98.79%":[137],"for":[138,164,193,219,225],"different":[139],"signifies":[147],"model":[150],"sufficiently":[152],"reduced":[153],"occurrence":[155],"false":[157],"negatives,":[158],"more":[162],"critical":[163],"FDS.":[166,182],"concept":[168],"transitional":[171],"window":[172],"introduced":[174],"to":[175,200,212,230],"improve":[176],"reaction":[178],"We":[183],"utilized":[184,211],"two":[185],"standard":[186],"datasets,":[188],"viz.":[189],"SisFall":[190],"KFall,":[192],"experimentation.":[195],"Dataset":[196],"fusion":[197],"employed":[199],"increase":[201],"generalizability":[203],"system.":[206],"This":[207],"work":[208],"be":[210],"design":[213],"develop":[215],"devices":[218,236],"Internet-of-Healthcare-Things":[221],"applications":[222],"(IoHT)":[223],"imparting":[226],"capabilities":[229],"robots":[232,240],"gait":[234],"rehabilitation":[235],"such":[237],"exoskeleton":[239],"smart":[242],"prosthetic":[243],"legs.":[244]},"counts_by_year":[{"year":2026,"cited_by_count":25},{"year":2025,"cited_by_count":33},{"year":2024,"cited_by_count":40},{"year":2023,"cited_by_count":27}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2022-12-15T00:00:00"}