{"id":"https://openalex.org/W7118911360","doi":"https://doi.org/10.1109/indin64977.2025.11279527","title":"WavI2I: Wavelet-Driven Inertial Imaging for Robust Industrial Human Activity Recognition","display_name":"WavI2I: Wavelet-Driven Inertial Imaging for Robust Industrial Human Activity Recognition","publication_year":2025,"publication_date":"2025-07-12","ids":{"openalex":"https://openalex.org/W7118911360","doi":"https://doi.org/10.1109/indin64977.2025.11279527"},"language":null,"primary_location":{"id":"doi:10.1109/indin64977.2025.11279527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","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/A5065624859","display_name":"Ming Xia","orcid":"https://orcid.org/0000-0002-6552-3377"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ming Xia","raw_affiliation_strings":["Beihang University,School of Electronics and Information Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Electronics and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090707691","display_name":"Deyou Zhang","orcid":"https://orcid.org/0000-0001-9621-561X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deyou Zhang","raw_affiliation_strings":["Beihang University,School of Electronics and Information Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Electronics and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122176628","display_name":"Zhuoyuan She","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoyuan She","raw_affiliation_strings":["Beihang University,School of Electronics and Information Engineering,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Electronics and Information Engineering,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375425","display_name":"Jiale Wang","orcid":"https://orcid.org/0000-0001-6530-7060"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiale Wang","raw_affiliation_strings":["Beihang University,School of Space and Earth Sciences,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Space and Earth Sciences,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015550050","display_name":"Chuang Shi","orcid":"https://orcid.org/0000-0002-4272-5881"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Shi","raw_affiliation_strings":["Beihang University,School of Space and Earth Sciences,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beihang University,School of Space and Earth Sciences,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5065624859"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6820798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.7276999950408936,"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":0.7276999950408936,"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":0.13019999861717224,"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/T11325","display_name":"Inertial Sensor and Navigation","score":0.02590000070631504,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/activity-recognition","display_name":"Activity recognition","score":0.7468000054359436},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5418000221252441},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5396000146865845},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.517799973487854},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49149999022483826},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.47279998660087585},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47189998626708984},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4625000059604645},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.4505999982357025}],"concepts":[{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.7468000054359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6682000160217285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.661300003528595},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5418000221252441},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.517799973487854},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49149999022483826},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47189998626708984},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.4505999982357025},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41760000586509705},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3531999886035919},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C95722684","wikidata":"https://www.wikidata.org/wiki/Q2622756","display_name":"Continuous wavelet transform","level":5,"score":0.2865999937057495},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.26969999074935913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26600000262260437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/indin64977.2025.11279527","is_oa":false,"landing_page_url":"https://doi.org/10.1109/indin64977.2025.11279527","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 23rd International Conference on Industrial Informatics (INDIN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49510854482650757,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2558720333","https://openalex.org/W2930650313","https://openalex.org/W3001960185","https://openalex.org/W3017181332","https://openalex.org/W3093478721","https://openalex.org/W3100777112","https://openalex.org/W3133837447","https://openalex.org/W4210713738","https://openalex.org/W4214514125","https://openalex.org/W4282981352","https://openalex.org/W4380607148","https://openalex.org/W4396708177"],"related_works":[],"abstract_inverted_index":{"Wearable":[0],"sensor\u2013based":[1],"human":[2],"activity":[3,32],"recognition":[4,120],"(HAR)":[5],"is":[6,17],"essential":[7],"for":[8],"navigation":[9],"and":[10,23,30,37,105,139],"industrial":[11],"automation,":[12],"where":[13],"real-time":[14],"data":[15,27],"processing":[16],"critical.":[18],"However,":[19],"the":[20,46,65,111,124],"inherent":[21],"nonlinearity":[22],"noise":[24],"in":[25,133],"sensor":[26,73],"make":[28],"accurate":[29],"fast":[31],"detection":[33],"a":[34,56,94,99],"challenging":[35],"task,":[36],"traditional":[38],"feature":[39,90],"extraction":[40],"techniques":[41],"often":[42],"struggle":[43],"to":[44,70,114],"capture":[45],"underlying":[47],"dynamic":[48],"changes.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53,92],"introduce":[54],"WavI2I,":[55],"novel":[57],"framework":[58],"that":[59,128],"enhances":[60],"HAR":[61],"by":[62,82],"first":[63],"applying":[64],"Continuous":[66],"Wavelet":[67],"Transform":[68],"(CWT)":[69],"convert":[71],"inertial":[72],"signals":[74],"into":[75],"time-frequency":[76],"planes,":[77],"which":[78],"are":[79],"then":[80],"processed":[81],"Convolutional":[83],"Neural":[84],"Networks":[85],"(CNNs).":[86],"To":[87],"further":[88],"optimize":[89],"extraction,":[91],"incorporate":[93],"nonlinear":[95],"scale":[96],"generator,":[97],"ensuring":[98],"balanced":[100],"focus":[101],"on":[102,123],"both":[103],"high-":[104],"low-frequency":[106],"components.":[107],"These":[108],"innovations":[109],"strengthen":[110],"CNN\u2019s":[112],"ability":[113],"identify":[115],"critical":[116],"features,":[117],"thereby":[118],"improving":[119],"accuracy.":[121],"Experiments":[122],"UCI-HAR":[125],"dataset":[126],"confirm":[127],"WavI2I":[129],"surpasses":[130],"existing":[131],"methods":[132],"terms":[134],"of":[135],"accuracy,":[136],"computational":[137],"efficiency,":[138],"model":[140],"simplicity.":[141]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}