{"id":"https://openalex.org/W4226108911","doi":"https://doi.org/10.1109/tim.2022.3158427","title":"Deformable Convolutional Networks for Multimodal Human Activity Recognition Using Wearable Sensors","display_name":"Deformable Convolutional Networks for Multimodal Human Activity Recognition Using Wearable Sensors","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226108911","doi":"https://doi.org/10.1109/tim.2022.3158427"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2022.3158427","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3158427","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/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/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/A5007736553","display_name":"Wenbo Huang","orcid":"https://orcid.org/0000-0002-6664-1172"},"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":"Wenbo Huang","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-6664-1172","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":null,"apc_paid":null,"fwci":6.558,"has_fulltext":false,"cited_by_count":70,"citation_normalized_percentile":{"value":0.97765151,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"71","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9887999892234802,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9836000204086304,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.748365044593811},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7203803658485413},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6986909508705139},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.68040931224823},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.662039577960968},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.529399037361145},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5203749537467957},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5005583763122559},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49292755126953125},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4894965887069702},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.4308890104293823},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42370766401290894},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42062050104141235},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41578999161720276},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3238222599029541},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32282885909080505},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.26797276735305786}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.748365044593811},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7203803658485413},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6986909508705139},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.68040931224823},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.662039577960968},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.529399037361145},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5203749537467957},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5005583763122559},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49292755126953125},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4894965887069702},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.4308890104293823},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42370766401290894},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42062050104141235},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41578999161720276},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3238222599029541},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32282885909080505},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26797276735305786},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2022.3158427","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2022.3158427","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":[],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1557701487","https://openalex.org/W1686810756","https://openalex.org/W1975428667","https://openalex.org/W1986805057","https://openalex.org/W2002261403","https://openalex.org/W2012557818","https://openalex.org/W2017634428","https://openalex.org/W2054780155","https://openalex.org/W2073401630","https://openalex.org/W2086385378","https://openalex.org/W2097117768","https://openalex.org/W2111737705","https://openalex.org/W2140944144","https://openalex.org/W2145343602","https://openalex.org/W2194775991","https://openalex.org/W2270470215","https://openalex.org/W2296311849","https://openalex.org/W2551239383","https://openalex.org/W2555209581","https://openalex.org/W2601564443","https://openalex.org/W2736191430","https://openalex.org/W2759690896","https://openalex.org/W2789868604","https://openalex.org/W2886692902","https://openalex.org/W2895429161","https://openalex.org/W2895688372","https://openalex.org/W2910141648","https://openalex.org/W2915687033","https://openalex.org/W2922509574","https://openalex.org/W2949196821","https://openalex.org/W2965144482","https://openalex.org/W2966926453","https://openalex.org/W2977757120","https://openalex.org/W2978575470","https://openalex.org/W2988019621","https://openalex.org/W2999753048","https://openalex.org/W3002278372","https://openalex.org/W3009411039","https://openalex.org/W3015014633","https://openalex.org/W3021673939","https://openalex.org/W3030949666","https://openalex.org/W3036546791","https://openalex.org/W3043588830","https://openalex.org/W3046349935","https://openalex.org/W3049146908","https://openalex.org/W3086667322","https://openalex.org/W3101690711","https://openalex.org/W3106268016","https://openalex.org/W3158825252","https://openalex.org/W3164845984","https://openalex.org/W3173506890","https://openalex.org/W3183457194","https://openalex.org/W3188277880","https://openalex.org/W3196692527","https://openalex.org/W3196745188","https://openalex.org/W3199905074","https://openalex.org/W3209735728","https://openalex.org/W6618372016","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6685048874","https://openalex.org/W6696429117","https://openalex.org/W6704286305","https://openalex.org/W6769209423"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W3105278570","https://openalex.org/W2117913171","https://openalex.org/W2582769230","https://openalex.org/W2797760888","https://openalex.org/W47322132"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2,22],"witnessed":[3],"significant":[4],"success":[5],"of":[6,41,69,173,233],"convolutional":[7,104],"neural":[8],"networks":[9],"(CNNs)":[10],"in":[11,205],"human":[12,108],"activity":[13,27,55,85,193,259],"recognition":[14,86,149,260],"(HAR)":[15],"using":[16],"wearable":[17],"sensors.":[18],"Nevertheless,":[19],"prior":[20],"works":[21],"an":[23,206],"obvious":[24],"drawback.":[25],"An":[26],"sample":[28],"may":[29],"contain":[30],"heterogeneous":[31],"sensor":[32,73],"modalities":[33,74],"from":[34,110],"different":[35,72,191],"body":[36],"parts.":[37],"Moreover,":[38],"the":[39,80,115,119,137,171,174,185,199,234,241,250,274],"significance":[40],"each":[42],"modality":[43],"will":[44],"change":[45],"over":[46,136],"time.":[47],"Because":[48],"a":[49,58,89,101,221],"normal":[50],"convolution":[51,276],"filter":[52,82,243],"usually":[53],"samples":[54],"data":[56],"at":[57],"fixed":[59],"regular":[60],"grid,":[61],"it":[62],"is":[63,79,180,244,255],"hard":[64],"to":[65,95,131,211,246,272],"capture":[66],"salient":[67],"features":[68],"activities":[70,109],"along":[71],"or":[75],"time":[76,232],"intervals.":[77],"What":[78],"best":[81],"form":[83],"for":[84,106,140,214,258],"still":[87,247],"remains":[88],"challenging":[90],"task.":[91],"In":[92],"this":[93,97],"article,":[94],"resolve":[96],"issue,":[98],"we":[99,146,228],"present":[100],"new":[102],"deformable":[103,235,242,275],"network":[105],"recognizing":[107],"intricate":[111],"sensory":[112,141],"data.":[113,142],"Specifically,":[114],"learned":[116],"offsets":[117],"and":[118,155,167,198,277],"feature":[120],"amplitudes":[121],"are":[122],"added":[123],"into":[124],"standard":[125],"convolution,":[126],"which":[127,182,209,254],"can":[128,202,264],"be":[129,188,203],"modulated":[130],"allow":[132],"more":[133],"free-form":[134],"deformation":[135,186],"sampling":[138,200],"grid":[139],"Comparing":[143],"previous":[144],"results,":[145],"achieve":[147],"state-of-the-art":[148],"accuracies,":[150],"e.g.,":[151],"82.91&#x0025;,":[152],"80.02&#x0025;,":[153],"97.35&#x0025;,":[154],"99.21&#x0025;,":[156],"respectively,":[157],"on":[158,190,220],"several":[159],"benchmark":[160],"HAR":[161],"datasets,":[162],"including":[163],"OPPORTUNITY,":[164],"UNIMIB-SHAR,":[165],"USC-HAD,":[166],"WISDM,":[168],"hence":[169],"indicating":[170],"advantage":[172],"proposed":[175],"method.":[176],"The":[177,195,237],"visual":[178],"analysis":[179],"provided,":[181],"shows":[183],"that":[184,240],"could":[187],"conditioned":[189],"input":[192],"samples.":[194],"receptive":[196],"field":[197],"locations":[201],"adjusted":[204],"adaptive":[207],"manner,":[208],"leads":[210],"better":[212],"interpretability":[213],"deep":[215],"model":[216],"behaviors.":[217],"Installing":[218],"PyTorch":[219],"Raspberry":[222],"Pi":[223],"3":[224],"B":[225],"plus":[226],"system,":[227],"evaluate":[229],"actual":[230],"run":[231],"model.":[236],"results":[238],"show":[239],"able":[245],"maintain":[248],"almost":[249],"same":[251],"inference":[252],"time,":[253],"very":[256],"beneficial":[257],"tasks.":[261],"Our":[262],"work":[263],"promote":[265],"further":[266],"research":[267],"by":[268],"leveraging":[269],"intermodulating":[270],"information":[271],"connect":[273],"attention":[278],"modules.":[279]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":27},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
