{"id":"https://openalex.org/W3090444339","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206637","title":"A Lightweight Neural-Net with Assistive Mobile Robot for Human Fall Detection System","display_name":"A Lightweight Neural-Net with Assistive Mobile Robot for Human Fall Detection System","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090444339","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206637","mag":"3090444339"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5103276500","display_name":"Wei Hong Chin","orcid":"https://orcid.org/0000-0002-8592-9315"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Wei Hong Chin","raw_affiliation_strings":["Faculty of Systems Design, Tokyo Metropolitan University, Hino, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Systems Design, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071908181","display_name":"Noel Nuo Wi Tay","orcid":"https://orcid.org/0000-0002-3236-922X"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Noel Nuo Wi Tay","raw_affiliation_strings":["Department of Artificial Intelligence, CSUN Technology Co., Ltd., Tokyo, japan","Faculty of Systems Design, Tokyo Metropolitan University, Hino, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence, CSUN Technology Co., Ltd., Tokyo, japan","institution_ids":[]},{"raw_affiliation_string":"Faculty of Systems Design, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074076109","display_name":"Naoyuki Kubota","orcid":"https://orcid.org/0000-0001-8829-037X"},"institutions":[{"id":"https://openalex.org/I69740276","display_name":"Tokyo Metropolitan University","ror":"https://ror.org/00ws30h19","country_code":"JP","type":"education","lineage":["https://openalex.org/I69740276"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Naoyuki Kubota","raw_affiliation_strings":["Faculty of Systems Design, Tokyo Metropolitan University, Hino, Japan"],"affiliations":[{"raw_affiliation_string":"Faculty of Systems Design, Tokyo Metropolitan University, Hino, Japan","institution_ids":["https://openalex.org/I69740276"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063587446","display_name":"Chu Kiong Loo","orcid":"https://orcid.org/0000-0001-7867-2665"},"institutions":[{"id":"https://openalex.org/I33849332","display_name":"University of Malaya","ror":"https://ror.org/00rzspn62","country_code":"MY","type":"education","lineage":["https://openalex.org/I33849332"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Chu Kiong Loo","raw_affiliation_strings":["Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia","institution_ids":["https://openalex.org/I33849332"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103276500"],"corresponding_institution_ids":["https://openalex.org/I69740276"],"apc_list":null,"apc_paid":null,"fwci":0.1954,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.50126033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994999766349792,"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":0.9994999766349792,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9934999942779541,"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/T12222","display_name":"IoT-based Smart Home Systems","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-science","display_name":"Computer science","score":0.7272469997406006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5771486163139343},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5740760564804077},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.5632236003875732},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5233216285705566},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4765346944332123},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.4319276809692383},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4180717170238495},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.3426523208618164},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3279423117637634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7272469997406006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5771486163139343},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5740760564804077},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.5632236003875732},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5233216285705566},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4765346944332123},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.4319276809692383},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4180717170238495},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.3426523208618164},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3279423117637634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206637","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206637","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2042058500","https://openalex.org/W2059584741","https://openalex.org/W2066005691","https://openalex.org/W2074099390","https://openalex.org/W2100716893","https://openalex.org/W2113886060","https://openalex.org/W2127426261","https://openalex.org/W2128628418","https://openalex.org/W2152599926","https://openalex.org/W2170672895","https://openalex.org/W2342662179","https://openalex.org/W2405044353","https://openalex.org/W2414900880","https://openalex.org/W2583784985","https://openalex.org/W2803242616","https://openalex.org/W2897223879","https://openalex.org/W2963163009","https://openalex.org/W2966983189","https://openalex.org/W2969896647","https://openalex.org/W2990503944","https://openalex.org/W4247295654"],"related_works":["https://openalex.org/W2906946336","https://openalex.org/W2146611987","https://openalex.org/W2938416672","https://openalex.org/W4389432327","https://openalex.org/W4289599435","https://openalex.org/W2132660247","https://openalex.org/W1986528036","https://openalex.org/W2098534227","https://openalex.org/W3126153333","https://openalex.org/W2155757393"],"abstract_inverted_index":{"Falls":[0],"are":[1,49,93,106],"a":[2,75,111,129,147,163,206,221],"major":[3],"health":[4],"issue,":[5],"particularly":[6],"among":[7],"the":[8,27,98,199],"elderly.":[9],"Increasing":[10],"fall":[11,38,46,59,137,148],"events":[12],"require":[13],"high":[14,212],"service":[15],"quality":[16],"and":[17,32,65,118,167,186,211],"dedicated":[18],"medical":[19],"treatment":[20],"which":[21,86,158],"is":[22,81],"an":[23,94,140],"economic":[24],"burden.":[25],"In":[26,124],"lack":[28],"of":[29,83,201,223],"appropriate":[30],"care":[31],"support,":[33],"serious":[34],"injuries":[35],"caused":[36],"by":[37],"will":[39],"cost":[40],"lives.":[41],"Therefore,":[42],"tracking":[43],"systems":[44,71],"with":[45,53,139,177],"detection":[47,60,138,152],"capabilities":[48],"required.":[50],"Static-view":[51],"sensors":[52,179],"machine":[54],"learning":[55,132],"techniques":[56],"for":[57,96,110,135,170,198],"human":[58,136],"have":[61],"been":[62,218],"widely":[63],"studied":[64],"achieved":[66],"significant":[67],"results.":[68],"However,":[69,102],"these":[70,193],"unable":[72,107],"to":[73,108,115,143,196,204],"monitor":[74],"person":[76,99],"if":[77],"he":[78],"or":[79],"she":[80],"out":[82],"viewing":[84],"angle":[85],"greatly":[87],"impedes":[88],"its":[89],"performance.":[90,213],"Mobile":[91],"robots":[92,105],"alternative":[95],"keeping":[97],"in":[100,121,162],"sight.":[101],"existing":[103],"mobile":[104],"operate":[109],"long":[112],"time":[113],"due":[114],"battery":[116],"issues":[117],"movement":[119],"constraints":[120],"complex":[122],"environments.":[123],"this":[125],"paper,":[126],"we":[127],"proposed":[128,151,190,215],"lightweight":[130],"deep":[131],"vision-based":[133],"model":[134],"assistive":[141,174],"robot":[142,175],"provide":[144],"assistance":[145],"when":[146],"happens.":[149],"The":[150,173,214],"system":[153,191,207],"requires":[154],"less":[155],"computational":[156],"power":[157],"can":[159,181],"be":[160],"implemented":[161],"low-cost":[164],"2D":[165],"camera":[166],"GPU":[168],"board":[169],"real-time":[171],"monitoring.":[172],"equipped":[176],"various":[178],"that":[180,208],"perform":[182],"SLAM,":[183],"obstacle":[184],"avoidance":[185],"navigation":[187],"autonomously.":[188],"Our":[189],"integrates":[192],"two":[194],"sub-systems":[195],"compensate":[197],"weakness":[200],"each":[202],"other":[203],"constitute":[205],"robust,":[209],"adaptable,":[210],"method":[216],"has":[217],"validated":[219],"through":[220],"series":[222],"experiments.":[224]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}