{"id":"https://openalex.org/W7154506036","doi":"https://doi.org/10.1109/upinlbs68186.2025.11468397","title":"MoE-HARNet: A Multimodal Mixture-of-Experts Network for Robust Smartphone Behavior Recognition in Complex Indoor Scenarios","display_name":"MoE-HARNet: A Multimodal Mixture-of-Experts Network for Robust Smartphone Behavior Recognition in Complex Indoor Scenarios","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7154506036","doi":"https://doi.org/10.1109/upinlbs68186.2025.11468397"},"language":null,"primary_location":{"id":"doi:10.1109/upinlbs68186.2025.11468397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/upinlbs68186.2025.11468397","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Ubiquitous Positioning, Indoor Navigation and Location-Based Services Conference (UPINLBS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5133682974","display_name":"Bing Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Yu","raw_affiliation_strings":["School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture,Beijing,China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123733751","display_name":"Qianhao Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianhao Zhang","raw_affiliation_strings":["School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture,Beijing,China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112401230","display_name":"Jianhua Liu","orcid":"https://orcid.org/0009-0001-3153-9960"},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Liu","raw_affiliation_strings":["School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture,Beijing,China","institution_ids":["https://openalex.org/I62853816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I62853816"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79968203,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.20499999821186066,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.20499999821186066,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.13699999451637268,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.06120000034570694,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/artificial-neural-network","display_name":"Artificial neural network","score":0.33959999680519104},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.2957000136375427},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2849000096321106},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2840000092983246},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.2840000092983246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6022999882698059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44510000944137573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/upinlbs68186.2025.11468397","is_oa":false,"landing_page_url":"https://doi.org/10.1109/upinlbs68186.2025.11468397","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Ubiquitous Positioning, Indoor Navigation and Location-Based Services Conference (UPINLBS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Pedestrian":[0],"Dead":[1],"Reckoning":[2],"(PDR)":[3],"systems":[4],"relying":[5],"on":[6],"smartphone":[7],"Inertial":[8],"Measurement":[9],"Units":[10],"(IMU)":[11],"are":[12],"fundamentally":[13],"constrained":[14],"by":[15],"accumulated":[16],"positioning":[17],"errors,":[18],"primarily":[19],"due":[20],"to":[21,105],"the":[22,29,56,108,115,123,140,155],"lack":[23],"of":[24,59,159],"robust":[25],"context-aware":[26,150],"capabilities.":[27],"Specifically,":[28],"coupled":[30],"variations":[31],"in":[32,50,65,139],"user":[33],"motion":[34],"states":[35],"and":[36,148,157],"phone":[37],"holding":[38],"postures":[39],"generate":[40],"various":[41],"highly":[42],"heterogeneous":[43],"joint":[44,82,141],"behavior":[45,83,142],"categories.":[46],"This":[47,98,145],"phenomenon":[48],"results":[49],"severe":[51],"domain":[52],"shift,":[53],"significantly":[54],"weakening":[55],"generalization":[57],"performance":[58,132],"conventional":[60],"dense":[61,137],"deep":[62],"learning":[63],"models":[64,138],"cross-user":[66],"scenarios.":[67],"To":[68],"overcome":[69],"this":[70],"challenge,":[71],"we":[72],"propose":[73],"a":[74,87,93],"novel":[75],"Multimodal":[76],"Mixture-of-Experts":[77,95],"Network":[78],"(MoE-HARNet)":[79],"for":[80,153],"high-precision":[81],"recognition.":[84],"MoE-HARNet":[85,129],"utilizes":[86],"Conv1D-Transformer":[88],"encoder":[89],"architecture":[90],"integrated":[91],"with":[92],"Sparse":[94],"(MoE)":[96],"layer.":[97],"conditional":[99],"computation":[100],"mechanism":[101],"leverages":[102],"expert":[103],"specialization":[104],"efficiently":[106],"decompose":[107],"complex":[109],"feature":[110],"space,":[111],"thereby":[112],"substantially":[113],"enhancing":[114,154],"model's":[116],"robustness":[117],"against":[118],"cross-subject":[119],"variations.":[120],"Evaluated":[121],"under":[122],"Leave-One-Subject-Out":[124],"Cross-Validation":[125],"(LOSO":[126],"CV)":[127],"protocol,":[128],"achieves":[130],"significant":[131],"improvements":[133],"over":[134],"existing":[135],"advanced":[136],"recognition":[143],"task.":[144],"offers":[146],"critical":[147],"stable":[149],"support":[151],"essential":[152],"stability":[156],"precision":[158],"next-generation":[160],"adaptive":[161],"PDR":[162],"systems.":[163]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-04-16T00:00:00"}
