{"id":"https://openalex.org/W7160547309","doi":"https://doi.org/10.48550/arxiv.2605.04617","title":"Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition","display_name":"Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160547309","doi":"https://doi.org/10.48550/arxiv.2605.04617"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04617","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04617","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135628936","display_name":"Zishu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Zishu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027931296","display_name":"Zaipeng Xie","orcid":"https://orcid.org/0000-0003-1637-1511"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Zaipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5107079476","display_name":"Xuanyao Jie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie, Xuanyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.6543999910354614,"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":0.6543999910354614,"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":0.2224999964237213,"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/T13553","display_name":"Age of Information Optimization","score":0.016100000590085983,"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/feature","display_name":"Feature (linguistics)","score":0.6154000163078308},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.5759000182151794},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5612999796867371},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.519599974155426},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.5181999802589417},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49639999866485596},{"id":"https://openalex.org/keywords/surprise","display_name":"Surprise","score":0.4611000120639801},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.4065999984741211},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38199999928474426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7549999952316284},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6154000163078308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6015999913215637},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.5759000182151794},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5612999796867371},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.519599974155426},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5048999786376953},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C2780343955","wikidata":"https://www.wikidata.org/wiki/Q333173","display_name":"Surprise","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.4065999984741211},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.3546999990940094},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.33640000224113464},{"id":"https://openalex.org/C1517167","wikidata":"https://www.wikidata.org/wiki/Q1134322","display_name":"Sight","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29429998993873596},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.2824999988079071},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27320000529289246},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04617","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04617","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Wearable":[0],"human":[1],"activity":[2],"recognition":[3],"(WHAR)":[4],"models":[5,24],"often":[6],"suffer":[7],"from":[8,36],"performance":[9],"degradation":[10,21],"under":[11],"real-world":[12,157],"cross-user":[13],"distribution":[14],"shifts.":[15],"Test-time":[16],"adaptation":[17],"(TTA)":[18],"mitigates":[19],"this":[20,50,103],"by":[22,124],"adapting":[23],"online":[25],"using":[26],"unlabeled":[27],"test":[28],"streams,":[29],"yet":[30],"existing":[31,163],"methods":[32],"largely":[33],"inherit":[34],"assumptions":[35],"vision":[37],"tasks":[38],"and":[39,76,92,110,134,150,169],"underexploit":[40],"the":[41,71,126,137],"inherent":[42],"inter-window":[43],"temporal":[44,55,74,90],"structure":[45,56],"in":[46],"WHAR":[47],"streams.":[48],"In":[49],"paper,":[51],"we":[52,105],"revisit":[53],"such":[54],"as":[57],"a":[58,108,130],"feature-conditioned":[59],"inference":[60],"signal":[61],"rather":[62],"than":[63],"merely":[64],"an":[65],"output-space":[66],"smoothing":[67],"prior.":[68],"We":[69],"derive":[70],"insight":[72],"that":[73,160],"continuity":[75],"observation-induced":[77],"feature":[78,128,139],"deviations":[79],"provide":[80],"complementary":[81],"cues":[82],"for":[83,114],"determining":[84],"when":[85],"to":[86,94,141],"preserve":[87],"or":[88],"release":[89],"inertia":[91],"where":[93],"route":[95],"prediction":[96],"refinement":[97],"during":[98],"likely":[99],"transitions.":[100],"Building":[101],"upon":[102],"insight,":[104],"propose":[106],"SIGHT,":[107],"lightweight":[109],"backpropagation-free":[111],"TTA":[112,164],"framework":[113],"WHAR,":[115],"enabling":[116],"real-time":[117],"edge":[118],"deployment.":[119],"SIGHT":[120,161],"estimates":[121],"predictive":[122],"surprise":[123],"comparing":[125],"current":[127],"with":[129],"prototype-based":[131],"expected":[132],"state,":[133],"then":[135],"uses":[136],"resulting":[138],"deviation":[140],"guide":[142],"geometry-aware":[143],"transition":[144],"routing":[145],"based":[146],"on":[147,156],"prototype":[148],"alignment":[149],"stream-level":[151],"marginal":[152],"habit":[153],"tracking.":[154],"Evaluations":[155],"datasets":[158],"confirm":[159],"outperforms":[162],"baselines":[165],"while":[166],"reducing":[167],"computational":[168],"memory":[170],"costs.":[171]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}
