{"id":"https://openalex.org/W4362683598","doi":"https://doi.org/10.1109/tim.2023.3265128","title":"An Efficient Diverse-Branch Convolution Scheme for Sensor-Based Human Activity Recognition","display_name":"An Efficient Diverse-Branch Convolution Scheme for Sensor-Based Human Activity Recognition","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4362683598","doi":"https://doi.org/10.1109/tim.2023.3265128"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2023.3265128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3265128","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-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/A5015088330","display_name":"Chaolei Han","orcid":"https://orcid.org/0000-0002-0801-4867"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaolei Han","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-0801-4867","affiliations":[{"raw_affiliation_string":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076127071","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0001-8749-7459"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Zhang","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-8749-7459","affiliations":[{"raw_affiliation_string":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004588590","display_name":"Shige Xu","orcid":"https://orcid.org/0000-0002-4785-9547"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shige Xu","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-4785-9547","affiliations":[{"raw_affiliation_string":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365484","display_name":"Xing Wang","orcid":"https://orcid.org/0000-0001-8704-7861"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Wang","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-8704-7861","affiliations":[{"raw_affiliation_string":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005207362","display_name":"Hao Wu","orcid":"https://orcid.org/0000-0002-3696-9281"},"institutions":[{"id":"https://openalex.org/I189210763","display_name":"Yunnan University","ror":"https://ror.org/0040axw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I189210763"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wu","raw_affiliation_strings":["School of Information Science and Engineering, Yunnan University, Kunming, China"],"raw_orcid":"https://orcid.org/0000-0002-3696-9281","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Yunnan University, Kunming, China","institution_ids":["https://openalex.org/I189210763"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048327458","display_name":"Aiguo Song","orcid":"https://orcid.org/0000-0002-1982-6780"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Aiguo Song","raw_affiliation_strings":["School of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1982-6780","affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2345,"currency":"USD","value_usd":2345},"apc_paid":null,"fwci":1.9961,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.87257662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":100},"biblio":{"volume":"72","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":1.0,"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":1.0,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9939000010490417,"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"}},{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9837999939918518,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/inference","display_name":"Inference","score":0.7377979159355164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.669100284576416},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5594463348388672},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5257887840270996},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.515133261680603},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.50046706199646},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4731760323047638},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.44301629066467285},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.41679155826568604},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.4143872857093811},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41430655121803284},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3924550712108612},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16859650611877441},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.15498727560043335}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7377979159355164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.669100284576416},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5594463348388672},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5257887840270996},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.515133261680603},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.50046706199646},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4731760323047638},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.44301629066467285},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.41679155826568604},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.4143872857093811},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41430655121803284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3924550712108612},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16859650611877441},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.15498727560043335},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2023.3265128","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2023.3265128","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G4279245431","display_name":"\u9762\u5411Web API\u6316\u6398\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u6784\u5efa\u7814\u7a76","funder_award_id":"61962061","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5665768396","display_name":null,"funder_award_id":"BK20191371","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W2017634428","https://openalex.org/W2054780155","https://openalex.org/W2097117768","https://openalex.org/W2126511896","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2270470215","https://openalex.org/W2342792048","https://openalex.org/W2555209581","https://openalex.org/W2736191430","https://openalex.org/W2759690896","https://openalex.org/W2789868604","https://openalex.org/W2794045421","https://openalex.org/W2801383749","https://openalex.org/W2810685774","https://openalex.org/W2894702700","https://openalex.org/W2902449706","https://openalex.org/W2907255073","https://openalex.org/W2908273910","https://openalex.org/W2923336951","https://openalex.org/W2928165649","https://openalex.org/W2947403743","https://openalex.org/W2962949934","https://openalex.org/W2963273475","https://openalex.org/W2977757120","https://openalex.org/W2981609437","https://openalex.org/W2981850489","https://openalex.org/W3030949666","https://openalex.org/W3036708415","https://openalex.org/W3100736224","https://openalex.org/W3102652454","https://openalex.org/W3164845984","https://openalex.org/W3168997536","https://openalex.org/W3171038842","https://openalex.org/W3173400793","https://openalex.org/W3185580468","https://openalex.org/W3196745188","https://openalex.org/W4286681770","https://openalex.org/W6637373629","https://openalex.org/W6704286305","https://openalex.org/W6755310813","https://openalex.org/W6772060317","https://openalex.org/W6773650956"],"related_works":["https://openalex.org/W4315697128","https://openalex.org/W3102845713","https://openalex.org/W2971502891","https://openalex.org/W3205506801","https://openalex.org/W4280599700","https://openalex.org/W3183570023","https://openalex.org/W4382323155","https://openalex.org/W2995343971","https://openalex.org/W2982536526","https://openalex.org/W2992221004"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"networks":[2],"have":[3],"recently":[4],"achieved":[5],"significant":[6],"success":[7],"in":[8,182],"sensor-based":[9],"human":[10],"activity":[11,22,57,99,151],"recognition":[12,58],"(HAR).":[13],"Existing":[14],"works":[15],"are":[16,35,155],"mainly":[17],"devoted":[18],"to":[19,38,51,97,137,145],"extracting":[20],"multiscale":[21],"features":[23],"from":[24],"sensor":[25],"time":[26],"series":[27],"by":[28],"increasing":[29],"network":[30],"depth":[31],"or":[32],"width,":[33],"which":[34,79,173],"not":[36],"friendly":[37],"mobile":[39],"devices":[40],"with":[41],"limited":[42],"computing":[43],"resources.":[44],"It":[45],"still":[46],"remains":[47],"a":[48,74,109,138,167,180,205],"challenging":[49],"issue":[50],"strike":[52],"an":[53],"ideal":[54],"trade-off":[55],"between":[56],"performance":[59,133,183],"and":[60,65,95,141,166,191],"inference-time":[61,128,148,197],"costse.g.,":[62],"the":[63,82,131,186],"latency":[64],"memory":[66],"footprint.":[67],"To":[68],"address":[69],"this":[70],"issue,":[71],"we":[72],"propose":[73],"diverse-branch":[75],"convolution":[76,87,111],"(DBC)":[77],"scheme,":[78],"could":[80,104],"strengthen":[81],"representation":[83],"capacity":[84],"of":[85,92,189],"vanilla":[86],"via":[88],"exploiting":[89],"diverse":[90],"branches":[91],"different":[93],"scales":[94],"complexities":[96],"enrich":[98],"feature":[100],"space.":[101],"Then":[102],"DBC":[103,121],"be":[105,135],"equivalently":[106],"transformed":[107],"into":[108],"single":[110],"layer":[112],"after":[113],"training":[114],"for":[115,150,201],"HAR":[116,171,202],"deployment.":[117],"In":[118],"such":[119],"manner,":[120],"only":[122],"complicates":[123],"training-time":[124],"microstructure":[125],"while":[126,184],"preserving":[127],"macrostructure,":[129],"where":[130],"model":[132],"can":[134,178],"boosted":[136],"higher":[139],"level":[140],"then":[142],"converted":[143],"back":[144],"its":[146],"original":[147],"structure":[149],"recognition.":[152],"Extensive":[153],"experiments":[154],"conducted":[156],"on":[157,204],"three":[158],"publicly":[159],"available":[160],"benchmark":[161],"datasets,i.e.,":[162],"OPPORTUNITY,":[163],"UniMiB-SHAR,":[164],"WISDM,":[165],"self-collected":[168],"Weakly":[169],"Labeled":[170],"dataset,":[172],"verify":[174],"that":[175],"our":[176],"method":[177],"give":[179],"lift":[181],"maintaining":[185],"same":[187],"number":[188],"parameters":[190],"FLOPs":[192],"as":[193],"baselines.":[194],"Finally,":[195],"actual":[196],"cost":[198],"is":[199],"evaluated":[200],"deployment":[203],"Raspberry":[206],"Pi":[207],"platform.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}