{"id":"https://openalex.org/W4366967648","doi":"https://doi.org/10.1145/3544793.3560392","title":"Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks","display_name":"Hierarchical Feature Recovery for Robust Human Activity Recognition in Body Sensor Networks","publication_year":2022,"publication_date":"2022-09-11","ids":{"openalex":"https://openalex.org/W4366967648","doi":"https://doi.org/10.1145/3544793.3560392"},"language":"en","primary_location":{"id":"doi:10.1145/3544793.3560392","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3544793.3560392","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560392","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560392","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030123155","display_name":"Nobuyuki Oishi","orcid":"https://orcid.org/0000-0002-9809-4011"},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Nobuyuki Oishi","raw_affiliation_strings":["Wearable Technologies Laboratory, University of Sussex, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Wearable Technologies Laboratory, University of Sussex, United Kingdom","institution_ids":["https://openalex.org/I162608824"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070698030","display_name":"Paula Lago","orcid":"https://orcid.org/0000-0001-5290-6486"},"institutions":[{"id":"https://openalex.org/I60158472","display_name":"Concordia University","ror":"https://ror.org/0420zvk78","country_code":"CA","type":"education","lineage":["https://openalex.org/I60158472"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Paula Lago","raw_affiliation_strings":["Concordia University, Canada"],"affiliations":[{"raw_affiliation_string":"Concordia University, Canada","institution_ids":["https://openalex.org/I60158472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046762323","display_name":"Philip Birch","orcid":"https://orcid.org/0000-0002-7740-9379"},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Phil Birch","raw_affiliation_strings":["Engineering and Informatics, University of Sussex, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Engineering and Informatics, University of Sussex, United Kingdom","institution_ids":["https://openalex.org/I162608824"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051210293","display_name":"Daniel Roggen","orcid":"https://orcid.org/0000-0001-8033-6417"},"institutions":[{"id":"https://openalex.org/I162608824","display_name":"University of Sussex","ror":"https://ror.org/00ayhx656","country_code":"GB","type":"education","lineage":["https://openalex.org/I162608824"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Daniel Roggen","raw_affiliation_strings":["University of Sussex, United Kingdom"],"affiliations":[{"raw_affiliation_string":"University of Sussex, United Kingdom","institution_ids":["https://openalex.org/I162608824"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030123155"],"corresponding_institution_ids":["https://openalex.org/I162608824"],"apc_list":null,"apc_paid":null,"fwci":0.1021,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43524575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"364","last_page":"370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9994000196456909,"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":0.9994000196456909,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9571999907493591,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9417999982833862,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6850917339324951},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.6643138527870178},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.6473156809806824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6306660175323486},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6119818091392517},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5979208946228027},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4816966950893402},{"id":"https://openalex.org/keywords/soft-sensor","display_name":"Soft sensor","score":0.45659977197647095},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4504675269126892},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.4492255747318268},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36331549286842346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.26613426208496094},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.13813212513923645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6850917339324951},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.6643138527870178},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.6473156809806824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6306660175323486},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6119818091392517},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5979208946228027},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4816966950893402},{"id":"https://openalex.org/C115575686","wikidata":"https://www.wikidata.org/wiki/Q18822403","display_name":"Soft sensor","level":3,"score":0.45659977197647095},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4504675269126892},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.4492255747318268},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36331549286842346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26613426208496094},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.13813212513923645},{"id":"https://openalex.org/C127716648","wikidata":"https://www.wikidata.org/wiki/Q104053","display_name":"Phenotype","level":3,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C188082640","wikidata":"https://www.wikidata.org/wiki/Q1780899","display_name":"Complementation","level":4,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3544793.3560392","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3544793.3560392","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560392","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:sro.sussex.ac.uk:108153","is_oa":true,"landing_page_url":"http://sro.sussex.ac.uk/id/eprint/108153/1/ubicomp22f-sub1007-i4.pdf","pdf_url":"https://sussex.figshare.com/articles/conference_contribution/Hierarchical_feature_recovery_for_robust_human_activity_recognition_in_body_sensor_networks/23492021/2/files/41213034.pdf","source":{"id":"https://openalex.org/S4306400129","display_name":"Sussex Research Online (University of Sussex)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I162608824","host_organization_name":"University of Sussex","host_organization_lineage":["https://openalex.org/I162608824"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceedings"},{"id":"pmh:oai:figshare.com:article/23492021","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Hierarchical_feature_recovery_for_robust_human_activity_recognition_in_body_sensor_networks/23492021","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3544793.3560392","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3544793.3560392","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3544793.3560392","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5600000023841858,"display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G1829457474","display_name":null,"funder_award_id":"H2020-ICT-2019-3","funder_id":"https://openalex.org/F4320332999","funder_display_name":"Horizon 2020 Framework Programme"}],"funders":[{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4366967648.pdf","grobid_xml":"https://content.openalex.org/works/W4366967648.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1511085627","https://openalex.org/W1990172830","https://openalex.org/W2023302299","https://openalex.org/W2073401630","https://openalex.org/W2077982540","https://openalex.org/W2134525957","https://openalex.org/W2137100320","https://openalex.org/W2176018869","https://openalex.org/W2342865424","https://openalex.org/W2518937691","https://openalex.org/W2800295498","https://openalex.org/W2896518452","https://openalex.org/W2907123474","https://openalex.org/W2997591727","https://openalex.org/W3007181739"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W1977098485","https://openalex.org/W4285201053","https://openalex.org/W2378018100"],"abstract_inverted_index":{"With":[0],"the":[1,36,97,103,113],"advances":[2],"in":[3,29],"Body":[4],"Sensor":[5],"Networks":[6],"(BSNs)":[7],"and":[8,53,110],"textile-integrated":[9],"sensing,":[10],"more":[11],"sensor":[12,41,51,109],"data":[13],"becomes":[14],"available":[15],"from":[16],"which":[17],"human":[18],"activities":[19,120],"are":[20],"recognised.":[21],"However,":[22],"some":[23],"sensors":[24,61,64,74,79,100],"may":[25,80],"become":[26],"unavailable":[27],"unexpectedly":[28],"practice.":[30],"Previous":[31],"work":[32],"proposed":[33],"to":[34,49,141,145],"complement":[35],"features":[37],"of":[38,69,72,119,121],"a":[39,56,88,117,146],"missing":[40,52,73],"with":[42,124],"regression-based":[43],"methods":[44],"but":[45],"considered":[46],"only":[47],"up":[48,140],"one":[50],"thus":[54],"lacked":[55],"mechanism":[57],"for":[58],"selecting":[59],"relevant":[60],"when":[62,77],"multiple":[63,78],"were":[65],"missing.":[66,82],"The":[67],"number":[68],"unique":[70],"combinations":[71],"increases":[75],"exponentially":[76],"be":[81],"To":[83],"handle":[84],"this,":[85],"we":[86],"propose":[87],"Hierarchical":[89],"Feature":[90],"Recovery":[91],"(HFR)":[92],"approach.":[93],"We":[94],"first":[95],"assess":[96],"dependencies":[98],"between":[99,107],"by":[101,139],"comparing":[102],"feature":[104],"mapping":[105],"accuracy":[106],"each":[108],"then":[111],"evaluate":[112],"HFR":[114,132],"approach":[115],"on":[116],"dataset":[118],"daily":[122],"living":[123],"17":[125],"gestures":[126],"using":[127],"14":[128],"motion":[129],"sensors.":[130],"Our":[131],"method":[133],"can":[134],"alleviate":[135],"classification":[136],"performance":[137],"drop":[138],"8.3":[142],"pp":[143],"compared":[144],"baseline":[147],"method.":[148]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
