{"id":"https://openalex.org/W4402722038","doi":"https://doi.org/10.1145/3675094.3678445","title":"Synthetic Skeleton Data Generation using Large Language Model for Nurse Activity Recognition","display_name":"Synthetic Skeleton Data Generation using Large Language Model for Nurse Activity Recognition","publication_year":2024,"publication_date":"2024-09-22","ids":{"openalex":"https://openalex.org/W4402722038","doi":"https://doi.org/10.1145/3675094.3678445"},"language":"en","primary_location":{"id":"doi:10.1145/3675094.3678445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3675094.3678445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing","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/A5095899027","display_name":"Umang Dobhal","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Umang Dobhal","raw_affiliation_strings":["Dronacharya Collge of Engineering, Gurugram, India"],"raw_orcid":"https://orcid.org/0009-0004-9450-3668","affiliations":[{"raw_affiliation_string":"Dronacharya Collge of Engineering, Gurugram, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063050817","display_name":"C. Onder Francisco Campos Garcia","orcid":null},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Christina Garcia","raw_affiliation_strings":["Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0009-0008-8515-5955","affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan","institution_ids":["https://openalex.org/I207014233"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080895628","display_name":"Sozo Inoue","orcid":"https://orcid.org/0000-0003-1109-8130"},"institutions":[{"id":"https://openalex.org/I207014233","display_name":"Kyushu Institute of Technology","ror":"https://ror.org/02278tr80","country_code":"JP","type":"education","lineage":["https://openalex.org/I207014233"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sozo Inoue","raw_affiliation_strings":["Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0000-0003-1109-8130","affiliations":[{"raw_affiliation_string":"Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan","institution_ids":["https://openalex.org/I207014233"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5095899027"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.3756,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.93488658,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"493","last_page":"499"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9100000262260437,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9100000262260437,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6712685823440552},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.6705193519592285},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5139898657798767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4767250120639801},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.45887649059295654},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33795037865638733},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.19070208072662354},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.14091041684150696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6712685823440552},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.6705193519592285},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5139898657798767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4767250120639801},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.45887649059295654},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33795037865638733},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.19070208072662354},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.14091041684150696}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3675094.3678445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3675094.3678445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:irdb.nii.ac.jp:01216:0007233152","is_oa":false,"landing_page_url":"https://kyutech.repo.nii.ac.jp/records/2002191","pdf_url":null,"source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"UbiComp '24: Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"journal article"},{"id":"pmh:oai:kyutech.repo.nii.ac.jp:02002191","is_oa":false,"landing_page_url":"http://hdl.handle.net/10228/0002002191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"NA"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2954395498","https://openalex.org/W4206425545","https://openalex.org/W4378465287","https://openalex.org/W4386840094","https://openalex.org/W4388032353","https://openalex.org/W4390613194","https://openalex.org/W4391315935","https://openalex.org/W4394647887","https://openalex.org/W4395021849"],"related_works":["https://openalex.org/W2953562271","https://openalex.org/W2334655667","https://openalex.org/W2464530384","https://openalex.org/W2105938841","https://openalex.org/W4253358700","https://openalex.org/W3139863978","https://openalex.org/W2014897006","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W3204019825"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,66,103,126],"improve":[4],"nurse":[5,30],"activity":[6,60],"recognition":[7,61],"by":[8,74],"employing":[9],"a":[10,28,46],"Large":[11],"Language":[12],"Model":[13],"(LLM)":[14],"to":[15,69,118,170],"generate":[16,104,171],"synthetic":[17,77,105,142,172,197],"pose":[18],"estimation":[19],"data.":[20],"Keypoint":[21],"data":[22,53,132,143,173,198],"extracted":[23],"from":[24],"recorded":[25],"videos":[26],"of":[27,52,59,96,161,167,187],"single":[29],"performing":[31],"Endotracheal":[32],"suctioning":[33],"(ES)":[34],"activities":[35,98],"using":[36,110,146,154],"You":[37],"Only":[38],"Look":[39],"Once":[40],"v7":[41],"(YOLOv7)":[42],"is":[43,92,144],"used":[44],"as":[45],"database.":[47],"We":[48],"explore":[49],"the":[50,57,72,128,147,159,162,165,168,176,184],"issue":[51],"imbalances":[54],"that":[55],"hinder":[56],"effectiveness":[58],"algorithms.":[62],"To":[63,124],"counter":[64],"this,":[65],"utilize":[67],"LLMs":[68,195],"artificially":[70],"augment":[71],"dataset":[73],"generating":[75],"varied":[76],"samples":[78],"through":[79],"prompting":[80],"strategies":[81],"with":[82,156,164],"different":[83,134],"content":[84,190],"and":[85,99,113,133,141,189],"context.":[86],"A":[87],"Random":[88,111],"Forest":[89],"(RF)":[90],"classifier":[91],"trained":[93],"on":[94],"annotations":[95],"medical":[97],"corresponding":[100],"keypoints.":[101],"Additionally,":[102],"datasets":[106,169],"in":[107,191],"equal":[108],"volumes":[109],"Sampling":[112],"Generative":[114],"Adversarial":[115],"Networks":[116],"(GAN)":[117],"benchmark":[119],"against":[120],"our":[121],"LLM-based":[122],"approach.":[123],"evaluate,":[125],"compared":[127],"performance":[129],"between":[130,139],"baseline":[131],"augmentation":[135],"approaches.":[136],"The":[137,151],"similarity":[138],"original":[140],"measured":[145],"Kolmogorov-Smirnov":[148],"(K-S)":[149],"test.":[150],"proposed":[152],"approach":[153],"LLM":[155],"prompts":[157,192],"containing":[158],"explanation":[160],"task":[163],"description":[166],"has":[174],"improved":[175],"overall":[177],"ES":[178],"classification":[179],"performance.":[180],"Our":[181],"study":[182],"illustrates":[183],"critical":[185],"role":[186],"context":[188],"for":[193,196],"optimizing":[194],"generation.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}