{"id":"https://openalex.org/W4313121376","doi":"https://doi.org/10.1145/3544794.3558460","title":"Virtual IMU Data Augmentation by Spring-Joint Model for Motion Exercises Recognition without Using Real Data","display_name":"Virtual IMU Data Augmentation by Spring-Joint Model for Motion Exercises Recognition without Using Real Data","publication_year":2022,"publication_date":"2022-09-11","ids":{"openalex":"https://openalex.org/W4313121376","doi":"https://doi.org/10.1145/3544794.3558460"},"language":"en","primary_location":{"id":"doi:10.1145/3544794.3558460","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3544794.3558460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Symposium on Wearable Computers","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/A5001725176","display_name":"Chengshuo Xia","orcid":"https://orcid.org/0000-0002-3937-2077"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chengshuo Xia","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Japan"],"raw_orcid":"https://orcid.org/0000-0002-3937-2077","affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053371666","display_name":"Yuta Sugiura","orcid":"https://orcid.org/0000-0003-3735-4809"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuta Sugiura","raw_affiliation_strings":["Graduate School of Science and Technology, Keio University, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3735-4809","affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Keio University, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5001725176"],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":1.5306,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.84233987,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"79","last_page":"83"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.996399998664856,"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":0.996399998664856,"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":0.9930999875068665,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9836999773979187,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"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":0.814138650894165},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7160338759422302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5864899754524231},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5602512359619141},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5513607263565063},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.528822124004364},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.5156477689743042},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.49216240644454956},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4600489139556885},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.45405369997024536},{"id":"https://openalex.org/keywords/virtual-reality","display_name":"Virtual reality","score":0.44137388467788696},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18990811705589294}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.814138650894165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7160338759422302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5864899754524231},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5602512359619141},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5513607263565063},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.528822124004364},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.5156477689743042},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.49216240644454956},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4600489139556885},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.45405369997024536},{"id":"https://openalex.org/C194969405","wikidata":"https://www.wikidata.org/wiki/Q170519","display_name":"Virtual reality","level":2,"score":0.44137388467788696},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18990811705589294},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3544794.3558460","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3544794.3558460","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Symposium on Wearable Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G442453887","display_name":null,"funder_award_id":"PRESTO JPMJPR2134","funder_id":"https://openalex.org/F4320334789","funder_display_name":"Japan Science and Technology Agency"}],"funders":[{"id":"https://openalex.org/F4320334789","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2520469238","https://openalex.org/W2620664872","https://openalex.org/W2625824506","https://openalex.org/W2898889947","https://openalex.org/W2923162815","https://openalex.org/W2952180663","https://openalex.org/W2962896489","https://openalex.org/W2972527328","https://openalex.org/W3007002323","https://openalex.org/W3083323811","https://openalex.org/W3084542156","https://openalex.org/W3086632078","https://openalex.org/W3101667008","https://openalex.org/W3163120578","https://openalex.org/W3188960566","https://openalex.org/W4200241294","https://openalex.org/W4200504110","https://openalex.org/W4214489558","https://openalex.org/W4308086464","https://openalex.org/W6820640554"],"related_works":["https://openalex.org/W2091018038","https://openalex.org/W2225378543","https://openalex.org/W9839718","https://openalex.org/W3110613631","https://openalex.org/W4287122200","https://openalex.org/W2040913503","https://openalex.org/W3016838864","https://openalex.org/W2766841671","https://openalex.org/W2904593369","https://openalex.org/W3135709989"],"abstract_inverted_index":{"A":[0],"conventional":[1],"motion":[2,9,54,109,136],"exercises":[3,55,110],"recognition":[4,44,56,111],"system":[5,19,45,57],"only":[6],"tracks":[7],"designated":[8],"types,":[10],"and":[11,39,104],"it":[12],"enables":[13],"users":[14],"cannot":[15],"use":[16],"a":[17,29,52,65,71,108,118],"customized":[18],"according":[20],"to":[21,32,74,106],"personal":[22],"needs.":[23],"The":[24,86,113],"virtual":[25,59,66,77,88],"IMU":[26,60,67],"data":[27,94,137],"provides":[28],"new":[30],"opportunity":[31],"reduce":[33],"the":[34,42,76,81,101,134],"cost":[35],"of":[36,131,138],"training":[37],"datasets":[38],"flexibly":[40],"design":[41,51,117],"activity":[43],"using":[46,58],"online":[47,83],"resources.":[48],"To":[49],"better":[50],"user-customized":[53],"data,":[61],"this":[62],"paper":[63],"proposes":[64],"sensor":[68],"module":[69],"with":[70,93,123],"spring-joint":[72,102],"model":[73,103],"augment":[75],"acceleration":[78,89,97],"signal":[79,90],"from":[80,95],"limited":[82,124],"2D":[84],"video.":[85],"original":[87],"is":[91],"extended":[92],"different":[96],"distributions":[98],"generated":[99],"by":[100],"used":[105],"train":[107],"system.":[112],"proposed":[114],"method":[115],"can":[116],"classifier":[119],"for":[120],"three":[121],"motions":[122],"video":[125],"resources,":[126],"showing":[127],"an":[128],"average":[129],"accuracy":[130],"85.5":[132],"on":[133],"real":[135],"seven":[139],"individuals.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}