{"id":"https://openalex.org/W7117457888","doi":"https://doi.org/10.1145/3714394.3754402","title":"Preliminary Investigation of Knowledge Transfer from Skeleton to IMU for Robust Activity Recognition in Industrial Settings","display_name":"Preliminary Investigation of Knowledge Transfer from Skeleton to IMU for Robust Activity Recognition in Industrial Settings","publication_year":2025,"publication_date":"2025-10-12","ids":{"openalex":"https://openalex.org/W7117457888","doi":"https://doi.org/10.1145/3714394.3754402"},"language":null,"primary_location":{"id":"doi:10.1145/3714394.3754402","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3714394.3754402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 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/A5121458984","display_name":"Hongyin Qiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hongyin Qiao","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013317917","display_name":"Hamada Rizk","orcid":"https://orcid.org/0000-0002-8278-8801"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamada Rizk","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111447705","display_name":"Takuya Maekawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Takuya Maekawa","raw_affiliation_strings":["The University of Osaka, Osaka, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Osaka, Osaka, Japan","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5121458984"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.64948464,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"221","last_page":"225"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.867900013923645,"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":0.867900013923645,"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":0.09040000289678574,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.004999999888241291,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.682699978351593},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6783999800682068},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6194999814033508},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5572999715805054},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5249000191688538},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.4422999918460846},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.4260999858379364},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.39719998836517334}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7254999876022339},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.682699978351593},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6783999800682068},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6194999814033508},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5972999930381775},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5572999715805054},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.546999990940094},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5249000191688538},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.4422999918460846},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.4260999858379364},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.38040000200271606},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3391000032424927},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C2777846634","wikidata":"https://www.wikidata.org/wiki/Q9621","display_name":"Human skeleton","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3714394.3754402","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3714394.3754402","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5422495603561401,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2270470215","https://openalex.org/W2514114051","https://openalex.org/W4284897674"],"related_works":[],"abstract_inverted_index":{"Skeleton-based":[0],"models":[1,118],"have":[2],"demonstrated":[3],"superior":[4],"performance":[5,114],"in":[6,41,45,98],"human":[7],"activity":[8],"recognition":[9],"(HAR)":[10],"tasks":[11],"due":[12,59],"to":[13,16,28,60,93],"their":[14],"ability":[15],"capture":[17],"rich":[18],"spatial":[19],"and":[20,102],"global":[21],"contextual":[22],"information.":[23,104],"Moreover,":[24],"skeleton":[25,52,87],"data":[26,34,53,69,88],"tend":[27],"exhibit":[29],"greater":[30],"robustness":[31],"than":[32],"IMU":[33,68,96],"under":[35,119],"various":[36],"perturbations,":[37],"such":[38],"as":[39,49],"changes":[40],"motion":[42,122],"speed.":[43],"However,":[44],"many":[46],"real-world":[47],"scenarios--such":[48],"industrial":[50],"environments--obtaining":[51],"during":[54,91],"inference":[55],"is":[56],"often":[57],"infeasible":[58],"occlusions":[61],"or":[62],"camera":[63],"placement":[64],"constraints,":[65],"leaving":[66],"only":[67,90],"available":[70,89],"at":[71],"test":[72],"time.":[73],"To":[74],"address":[75],"this":[76],"limitation,":[77],"we":[78],"propose":[79],"a":[80],"cross-modal":[81],"knowledge":[82],"transfer":[83],"framework":[84],"that":[85,108],"leverages":[86],"training":[92],"guide":[94],"the":[95,113],"model":[97],"learning":[99],"key":[100],"temporal":[101],"structural":[103],"Experimental":[105],"results":[106],"demonstrate":[107],"our":[109],"method":[110],"significantly":[111],"enhances":[112],"of":[115],"IMU-based":[116],"HAR":[117],"accelerated":[120],"worker":[121],"scenario.":[123]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-12-29T00:00:00"}