{"id":"https://openalex.org/W7108216098","doi":"https://doi.org/10.48550/arxiv.2511.23173","title":"Robust In-the-Wild Exercise Recognition from a Single Wearable: Data-Side Fusion, Sensor Rotation, and Feature Engineering","display_name":"Robust In-the-Wild Exercise Recognition from a Single Wearable: Data-Side Fusion, Sensor Rotation, and Feature Engineering","publication_year":2025,"publication_date":"2025-11-28","ids":{"openalex":"https://openalex.org/W7108216098","doi":"https://doi.org/10.48550/arxiv.2511.23173"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2511.23173","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.23173","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2511.23173","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Phan, Hoang Khang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Phan, Hoang Khang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Le, Khang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Khang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Nguyen, Tu Nhat Khang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Tu Nhat Khang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Van Dao, Anh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Van Dao, Anh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Le, Nhat Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Nhat Tan","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":0.7434999942779541,"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":0.7434999942779541,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.05169999971985817,"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"}},{"id":"https://openalex.org/T10352","display_name":"Physical Activity and Health","score":0.024800000712275505,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5968000292778015},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5824000239372253},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.49480000138282776},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4357999861240387},{"id":"https://openalex.org/keywords/voting","display_name":"Voting","score":0.39890000224113464},{"id":"https://openalex.org/keywords/feature-matching","display_name":"Feature matching","score":0.3952000141143799},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.38929998874664307},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.3513999879360199}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6197999715805054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6186000108718872},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5968000292778015},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5824000239372253},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4357999861240387},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.39890000224113464},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.3952000141143799},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39329999685287476},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3538999855518341},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3513999879360199},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C132778050","wikidata":"https://www.wikidata.org/wiki/Q2065430","display_name":"Weighted voting","level":4,"score":0.3343999981880188},{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2549000084400177},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2511.23173","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.23173","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2511.23173","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.23173","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Monitoring":[0],"physical":[1],"exercises":[2],"is":[3,140],"vital":[4],"for":[5,127,132],"health":[6,15],"promotion,":[7],"with":[8,96],"automated":[9],"systems":[10],"becoming":[11],"standard":[12],"in":[13,44],"personal":[14],"surveillance.":[16],"However,":[17],"sensor":[18,81],"placement":[19],"variability":[20],"and":[21,41,52,65,78,83,99],"unconstrained":[22],"movements":[23],"limit":[24],"their":[25],"effectiveness.":[26],"This":[27],"study":[28],"proposes":[29],"the":[30,108,138],"team":[31],"\"3KA\"'s":[32],"one-sensor":[33],"workout":[34],"activity":[35],"recognition":[36,129],"method":[37],"using":[38],"feature":[39],"extraction":[40,61],"data":[42,75],"augmentation":[43],"2ndWEAR":[45],"Dataset":[46],"Challenge.":[47],"From":[48],"raw":[49],"acceleration,":[50],"angle":[51],"signal":[53],"magnitude":[54],"vector":[55],"features":[56,123],"were":[57,124],"derived,":[58],"followed":[59],"by":[60],"of":[62],"statistical,":[63],"fractal/spectral,":[64],"higher-order":[66],"differential":[67],"features.":[68],"A":[69],"fused":[70],"dataset":[71],"combining":[72,92],"left/right":[73],"limb":[74],"was":[76],"created,":[77],"augmented":[79],"via":[80],"rotation":[82],"axis":[84],"inversion.":[85],"We":[86],"utilized":[87],"a":[88],"soft":[89],"voting":[90],"model":[91,109],"Hist":[93],"Gradient":[94,101],"Boosting":[95,102],"balanced":[97],"weights":[98],"Extreme":[100],"without.":[103],"Under":[104],"group":[105],"5-fold":[106],"evaluation,":[107],"achieved":[110],"58.83\\%":[111],"macro":[112],"F1":[113],"overall":[114],"(61.72%":[115],"arm,":[116],"55.95%":[117],"leg).":[118],"ANOVA":[119],"F-score":[120],"showed":[121],"fractal/spectral":[122],"most":[125],"important":[126],"arm-based":[128],"but":[130],"least":[131],"leg-based.":[133],"The":[134],"code":[135],"to":[136],"reproduce":[137],"experiments":[139],"publicly":[141],"available":[142],"via:":[143],"https://github.com/Khanghcmut/WEAR\\_3K":[144]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-12-03T00:00:00"}