{"id":"https://openalex.org/W7124975144","doi":"https://doi.org/10.1109/tce.2026.3655723","title":"A Feature-Driven Hybrid Deep Learning for Human Activity Recognition in Holographic IoT Systems","display_name":"A Feature-Driven Hybrid Deep Learning for Human Activity Recognition in Holographic IoT Systems","publication_year":2026,"publication_date":"2026-01-19","ids":{"openalex":"https://openalex.org/W7124975144","doi":"https://doi.org/10.1109/tce.2026.3655723"},"language":null,"primary_location":{"id":"doi:10.1109/tce.2026.3655723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2026.3655723","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","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/A5045150925","display_name":"Sadhna Bijrothiya","orcid":null},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sadhna Bijrothiya","raw_affiliation_strings":["Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal (MANIT),, Bhopal, Madhya Pradesh, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal (MANIT),, Bhopal, Madhya Pradesh, India","institution_ids":["https://openalex.org/I91277730"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087358758","display_name":"Vaibhav Soni","orcid":"https://orcid.org/0000-0002-6472-5938"},"institutions":[{"id":"https://openalex.org/I91277730","display_name":"Maulana Azad National Institute of Technology","ror":"https://ror.org/026vtd268","country_code":"IN","type":"education","lineage":["https://openalex.org/I91277730"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vaibhav Soni","raw_affiliation_strings":["Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal (MANIT),, Bhopal, Madhya Pradesh, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Maulana Azad National Institute of Technology Bhopal (MANIT),, Bhopal, Madhya Pradesh, India","institution_ids":["https://openalex.org/I91277730"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5045150925"],"corresponding_institution_ids":["https://openalex.org/I91277730"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16238382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"72","issue":"1","first_page":"1292","last_page":"1299"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.53329998254776,"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":0.53329998254776,"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":0.22089999914169312,"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":0.01860000006854534,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7595000267028809},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7206000089645386},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7200999855995178},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.4869000017642975},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4790000021457672},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46799999475479126},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4675000011920929},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4528999924659729}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7595000267028809},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7416999936103821},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7206000089645386},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7200999855995178},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7166000008583069},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4790000021457672},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46799999475479126},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4675000011920929},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43050000071525574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40779998898506165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.39089998602867126},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.38909998536109924},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.37610000371932983},{"id":"https://openalex.org/C187590223","wikidata":"https://www.wikidata.org/wiki/Q527628","display_name":"Holography","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.260699987411499}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2026.3655723","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2026.3655723","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Human":[0],"Activity":[1],"Recognition":[2],"(HAR)":[3],"is":[4],"an":[5],"essential":[6],"component":[7],"of":[8,39,132,156,167],"intelligent":[9],"healthcare":[10,183],"and":[11,18,36,80,93,111,119,135,141,150,159,165],"holographic":[12,88,181],"IoT":[13],"ecosystems,":[14],"supporting":[15],"continuous":[16],"monitoring":[17,179],"real-time":[19],"behavioral":[20],"assessment.":[21],"Despite":[22],"recent":[23],"progress":[24],"in":[25,180],"deep":[26],"learning,":[27],"challenges":[28],"such":[29],"as":[30],"data":[31],"imbalance,":[32],"limited":[33],"feature":[34],"generalization,":[35],"insufficient":[37],"exploitation":[38],"multidimensional":[40],"contextual":[41,99],"dependencies":[42],"continue":[43],"to":[44,96],"affect":[45],"model":[46],"performance.":[47],"To":[48],"address":[49],"these":[50],"issues,":[51],"this":[52],"paper":[53],"presents":[54],"a":[55,60,66,72,81],"feature-driven":[56],"hybrid":[57],"framework":[58],"integrating":[59],"Multi-Head":[61,82],"Convolutional":[62,67],"Neural":[63],"Network":[64,78],"(MHCNN),":[65],"Block":[68],"Attention":[69,83],"Module":[70],"(CBAM),":[71],"Bidirectional":[73],"Dilated":[74],"Long":[75],"Short-Term":[76],"Memory":[77],"(BiDLSTM),":[79],"(MHA)":[84],"mechanism.":[85],"The":[86],"proposed":[87,169],"architecture":[89],"combines":[90],"spatial,":[91],"channel-wise,":[92],"temporal":[94,117,125],"representations":[95],"enable":[97],"comprehensive":[98],"learning.":[100],"Specifically,":[101],"MHCNN":[102],"extracts":[103],"multi-scale":[104],"spatial":[105,110],"features,":[106],"CBAM":[107],"emphasizes":[108],"informative":[109],"channel":[112],"responses,":[113],"BiDLSTM":[114],"captures":[115],"long-range":[116],"dependencies,":[118],"MHA":[120],"selectively":[121],"focuses":[122],"on":[123,137],"relevant":[124],"patterns.":[126],"Experimental":[127],"evaluation":[128],"achieves":[129],"classification":[130],"accuracies":[131],"98.49%,":[133],"99.88%,":[134],"97.23%":[136],"the":[138,163,168],"WISDM,":[139],"MHEALTH,":[140],"UCI-HAR":[142],"datasets,":[143],"respectively.":[144],"Corresponding":[145],"Matthews":[146],"Correlation":[147],"Coefficient":[148],"(MCC)":[149],"Cohen\u2019s":[151],"Kappa":[152],"Score":[153],"(CKS)":[154],"values":[155],"96.98%,":[157],"99.76%,":[158],"94.46%":[160],"further":[161],"demonstrate":[162],"robustness":[164],"reliability":[166],"framework.":[170],"These":[171],"results":[172],"indicate":[173],"its":[174],"suitability":[175],"for":[176],"real-time,":[177],"context-aware":[178],"IoT-based":[182],"environments.":[184]},"counts_by_year":[],"updated_date":"2026-03-26T06:05:38.182114","created_date":"2026-01-21T00:00:00"}