{"id":"https://openalex.org/W4409076530","doi":"https://doi.org/10.1109/jiot.2025.3555985","title":"Efficient Spatiotemporal-Structural Masking for Dynamic Human Activity Recognition With Optimized Computation","display_name":"Efficient Spatiotemporal-Structural Masking for Dynamic Human Activity Recognition With Optimized Computation","publication_year":2025,"publication_date":"2025-04-01","ids":{"openalex":"https://openalex.org/W4409076530","doi":"https://doi.org/10.1109/jiot.2025.3555985"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2025.3555985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3555985","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","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/A5113378288","display_name":"Nanfu Ye","orcid":null},"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":"Nanfu Ye","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0000-9734-4077","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/A5109766690","display_name":"Di Xiong","orcid":"https://orcid.org/0009-0009-6445-7863"},"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":"Di Xiong","raw_affiliation_strings":["School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0009-6445-7863","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":["Department of Instrument Science and Engineering, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1982-6780","affiliations":[{"raw_affiliation_string":"Department 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":2.2966,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8758643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"12","issue":"13","first_page":"24738","last_page":"24749"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9650999903678894,"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":0.9650999903678894,"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":0.9498000144958496,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.91839998960495,"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":0.783806562423706},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.7116659879684448},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.6439356207847595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4780184030532837},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4557272493839264},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35640227794647217}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.783806562423706},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.7116659879684448},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.6439356207847595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4780184030532837},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4557272493839264},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35640227794647217},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2025.3555985","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2025.3555985","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5117308779","display_name":null,"funder_award_id":"202005AC160005","funder_id":"https://openalex.org/F4320335950","funder_display_name":"Cultivating Plan Program for the Leader in Science and Technology of Yunnan Province"},{"id":"https://openalex.org/G8016481535","display_name":null,"funder_award_id":"62373194","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G918101682","display_name":null,"funder_award_id":"YNWR-QNBJ-2019-188","funder_id":"https://openalex.org/F4320329867","funder_display_name":"Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320329867","display_name":"Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province","ror":null},{"id":"https://openalex.org/F4320335950","display_name":"Cultivating Plan Program for the Leader in Science and Technology of Yunnan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2017634428","https://openalex.org/W2026297770","https://openalex.org/W2270470215","https://openalex.org/W2342792048","https://openalex.org/W2548559732","https://openalex.org/W2552313070","https://openalex.org/W2555209581","https://openalex.org/W2736191430","https://openalex.org/W2759690896","https://openalex.org/W2888077475","https://openalex.org/W2894702700","https://openalex.org/W2947898397","https://openalex.org/W2949052757","https://openalex.org/W2963393494","https://openalex.org/W2965144482","https://openalex.org/W3006654525","https://openalex.org/W3015014633","https://openalex.org/W3021673939","https://openalex.org/W3035678286","https://openalex.org/W3044326989","https://openalex.org/W3109632933","https://openalex.org/W3134255974","https://openalex.org/W3164845984","https://openalex.org/W3176629891","https://openalex.org/W3211681816","https://openalex.org/W3215341388","https://openalex.org/W4206397907","https://openalex.org/W4286681770","https://openalex.org/W4292722085","https://openalex.org/W4312617592","https://openalex.org/W4322154588","https://openalex.org/W4365421169","https://openalex.org/W4367360721","https://openalex.org/W4377696093","https://openalex.org/W4385780451","https://openalex.org/W4387068240","https://openalex.org/W4387350614","https://openalex.org/W6696429117","https://openalex.org/W6704286305","https://openalex.org/W6729448088","https://openalex.org/W6768742326","https://openalex.org/W6856153088"],"related_works":["https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W3081694532","https://openalex.org/W1969211203","https://openalex.org/W2053286651","https://openalex.org/W4231775656","https://openalex.org/W1517958729","https://openalex.org/W2181743346","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Recently,":[0],"deep":[1,75],"convolutional":[2],"neural":[3],"networks":[4],"(CNNs)":[5],"have":[6],"achieved":[7],"outstanding":[8],"success":[9],"in":[10,53],"sensor-based":[11],"human":[12],"activity":[13,49,68,146,160],"recognition":[14],"(HAR)":[15],"scenario,":[16],"but":[17],"at":[18],"the":[19,104],"cost":[20],"of":[21,42,108,130],"huge":[22],"computational":[23,101,174,207],"complexity,":[24],"thereby":[25,158],"restricting":[26],"their":[27],"practical":[28],"deployment":[29],"on":[30,103,177,184],"resource-limited":[31],"wearable":[32],"devices.":[33],"This":[34],"may":[35,220],"be":[36],"partly":[37],"attributed":[38],"to":[39,59,66,95,112,153,205,216],"static":[40,60,201,217],"nature":[41],"most":[43,105],"existing":[44],"CNNs,":[45,76],"which":[46,77],"process":[47],"all":[48,233],"samples":[50],"uniformly,":[51],"resulting":[52],"structural":[54,72,115,133,168],"and":[55,116,134,167,193,250],"data":[56,117],"redundancy.":[57],"Comparing":[58],"networks,":[61],"one":[62],"promising":[63,92],"strategy":[64,93],"is":[65,94,196],"accelerate":[67],"inference":[69],"by":[70,99,198],"exploiting":[71],"redundancy":[73,98,118],"within":[74],"selectively":[78],"activates":[79],"computation":[80],"units":[81],"such":[82],"as":[83],"convolution":[84],"channels":[85],"while":[86,209],"handling":[87],"different":[88],"samples.":[89],"The":[90,162,236],"other":[91],"explore":[96],"spatiotemporal":[97,135,166],"concentrating":[100],"effort":[102],"informative":[106],"regions":[107],"sensor":[109],"data.":[110],"How":[111],"simultaneously":[113],"leverage":[114],"still":[119],"remains":[120],"largely":[121],"overlooked.":[122],"In":[123],"this":[124],"article,":[125],"from":[126],"a":[127,150,199],"new":[128],"perspective":[129],"exploring":[131],"both":[132],"redundancy,":[136],"we":[137],"introduce":[138],"an":[139,226,245],"efficient":[140],"spatiotemporal-structural":[141,163],"masker":[142,164],"network":[143],"(SSMNet)":[144],"for":[145],"recognition.":[147],"It":[148],"utilizes":[149],"dual-mask":[151],"mechanism":[152],"make":[154],"dynamic,":[155],"sample-specific":[156],"decisions,":[157],"accelerating":[159],"inference.":[161],"integrates":[165],"decisions":[169],"through":[170],"masks,":[171],"dynamically":[172],"allocating":[173],"resources":[175],"based":[176],"input":[178],"with":[179,225],"minimal":[180],"overhead.":[181],"Extensive":[182],"experiments":[183],"three":[185,234],"public":[186],"HAR":[187],"benchmark":[188],"datasets,":[189],"namely,":[190],"WISDM,":[191],"UniMiB-SHAR,":[192],"PAMAP2.":[194],"SSMNet":[195],"guided":[197],"high-accuracy":[200],"model,":[202],"allowing":[203],"it":[204,219],"reduce":[206,221],"costs":[208],"maintaining":[210],"state-of-the-art":[211],"performance.":[212],"For":[213],"example,":[214],"comparing":[215],"baselines,":[218],"nearly":[222],"40%":[223],"FLOPs":[224],"accuracy":[227,249],"drop":[228],"smaller":[229],"than":[230],"1%,":[231],"across":[232],"datasets":[235],"detailed":[237],"analyses":[238],"affirm":[239],"that":[240],"our":[241],"method":[242],"can":[243],"strike":[244],"optimal":[246],"tradeoff":[247],"between":[248],"efficiency.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}
