{"id":"https://openalex.org/W3033891414","doi":"https://doi.org/10.1145/3372278.3390702","title":"Multi-level Recognition on Falls from Activities of Daily Living","display_name":"Multi-level Recognition on Falls from Activities of Daily Living","publication_year":2020,"publication_date":"2020-06-02","ids":{"openalex":"https://openalex.org/W3033891414","doi":"https://doi.org/10.1145/3372278.3390702","mag":"3033891414"},"language":"en","primary_location":{"id":"doi:10.1145/3372278.3390702","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372278.3390702","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 International Conference on Multimedia Retrieval","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/A5108050442","display_name":"Jiawei Li","orcid":"https://orcid.org/0000-0003-3873-8003"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiawei Li","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X"},"institutions":[{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":["PengCheng Laboratory, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"PengCheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063165486","display_name":"Qianggang Ding","orcid":"https://orcid.org/0000-0001-8415-0476"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianggang Ding","raw_affiliation_strings":["Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5108050442"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.4885,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.65127378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"464","last_page":"471"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9983999729156494,"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":0.9955999851226807,"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/computer-science","display_name":"Computer science","score":0.7356061935424805},{"id":"https://openalex.org/keywords/activities-of-daily-living","display_name":"Activities of daily living","score":0.6538544297218323},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.65240079164505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6279077529907227},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5600876808166504},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5272644758224487},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5198922157287598},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49383339285850525},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4103519320487976},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40547290444374084},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1035788357257843}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7356061935424805},{"id":"https://openalex.org/C79544238","wikidata":"https://www.wikidata.org/wiki/Q423243","display_name":"Activities of daily living","level":2,"score":0.6538544297218323},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.65240079164505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6279077529907227},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5600876808166504},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5272644758224487},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5198922157287598},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49383339285850525},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4103519320487976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40547290444374084},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1035788357257843},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3372278.3390702","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3372278.3390702","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.5400000214576721}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W645314983","https://openalex.org/W1522734439","https://openalex.org/W1947481528","https://openalex.org/W2028661866","https://openalex.org/W2074099390","https://openalex.org/W2076068958","https://openalex.org/W2079118768","https://openalex.org/W2100716893","https://openalex.org/W2118218997","https://openalex.org/W2121292082","https://openalex.org/W2186222003","https://openalex.org/W2323793574","https://openalex.org/W2462996230","https://openalex.org/W2507009361","https://openalex.org/W2559085405","https://openalex.org/W2604113307","https://openalex.org/W2606909710","https://openalex.org/W2778523960","https://openalex.org/W2796633859","https://openalex.org/W2902066034","https://openalex.org/W2952186347","https://openalex.org/W2963017553","https://openalex.org/W2963645879","https://openalex.org/W2963876278","https://openalex.org/W2964241613","https://openalex.org/W2967368420","https://openalex.org/W3145740684"],"related_works":["https://openalex.org/W2071210425","https://openalex.org/W3195649134","https://openalex.org/W4310840813","https://openalex.org/W4200627986","https://openalex.org/W2103158077","https://openalex.org/W2281498195","https://openalex.org/W2208191412","https://openalex.org/W2413724037","https://openalex.org/W2989668789","https://openalex.org/W4312127264"],"abstract_inverted_index":{"The":[0,197],"falling":[1],"accident":[2],"is":[3,27,122],"one":[4],"of":[5,32,174,210],"the":[6,30,48,56,65,74,78,89,131,157,167,171,191,208],"largest":[7],"threats":[8],"to":[9,14,41,72,101,140,165],"human":[10,24],"health,":[11],"which":[12,106,124],"leads":[13],"broken":[15],"bones,":[16],"head":[17],"injury,":[18],"or":[19],"even":[20],"death.":[21],"Therefore,":[22],"automatic":[23],"fall":[25,51,66,83,119,145,158,176,195],"recognition":[26,52,84,120,209],"vital":[28],"for":[29,47,64,130],"Activities":[31],"Daily":[33],"Living":[34],"(ADL).":[35],"In":[36],"this":[37],"paper,":[38],"we":[39,92,161],"try":[40],"define":[42],"multi-level":[43],"computer":[44],"vision":[45],"tasks":[46],"visually":[49],"observed":[50],"problem":[53],"and":[54,58,76,133,136,144,159,170,186,193,200,212],"study":[55,163],"methods":[57,75,85],"pipeline.":[59],"We":[60,182],"make":[61,141],"frame-level":[62],"labels":[63],"action":[67,177],"on":[68,88,156,189,207],"several":[69],"ADL":[70],"datasets":[71,199],"test":[73],"support":[77],"analysis.":[79],"While":[80],"current":[81,149],"deep-learning":[82],"usually":[86],"work":[87],"sequence-level":[90,118],"input,":[91],"propose":[93],"a":[94,103,111,117,153,175,179,204],"novel":[95],"Dynamic":[96],"Pose":[97],"Motion":[98],"(DPM)":[99],"representation":[100],"go":[102],"step":[104],"further,":[105],"can":[107],"be":[108],"captured":[109],"by":[110],"flexible":[112],"motion":[113,134],"extraction":[114],"module.":[115],"Besides,":[116],"pipeline":[121],"proposed,":[123],"has":[125,137],"an":[126],"explicit":[127],"two-branch":[128],"structure":[129],"appearance":[132],"feature,":[135],"canonical":[138],"LSTM":[139],"temporal":[142],"modeling":[143],"prediction.":[146],"Finally,":[147],"while":[148],"research":[150],"only":[151],"makes":[152],"binary":[154],"classification":[155],"ADL,":[160],"further":[162],"how":[164],"detect":[166],"start":[168],"time":[169,173],"end":[172],"in":[178],"video-level":[180],"task.":[181],"conduct":[183],"analysis":[184],"experiments":[185,202],"ablation":[187],"studies":[188],"both":[190],"simulated":[192],"real-life":[194],"datasets.":[196],"relabelled":[198],"extensive":[201],"form":[203],"new":[205],"baseline":[206],"falls":[211],"ADL.":[213]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
