{"id":"https://openalex.org/W2605049121","doi":"https://doi.org/10.1109/bsn.2017.7936037","title":"Unsupervised deep representation learning to remove motion artifacts in free-mode body sensor networks","display_name":"Unsupervised deep representation learning to remove motion artifacts in free-mode body sensor networks","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2605049121","doi":"https://doi.org/10.1109/bsn.2017.7936037","mag":"2605049121"},"language":"en","primary_location":{"id":"doi:10.1109/bsn.2017.7936037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bsn.2017.7936037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","raw_type":"proceedings-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/A5100535963","display_name":"Mohammed Shoaib","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shoaib Mohammed","raw_affiliation_strings":["Microsoft Al and Research, Redmond, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Al and Research, Redmond, WA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007425970","display_name":"Ivan Tashev","orcid":"https://orcid.org/0000-0002-2263-2047"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ivan Tashev","raw_affiliation_strings":["Microsoft Al and Research, Redmond, WA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Al and Research, Redmond, WA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3856,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.88680534,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"16","issue":null,"first_page":"183","last_page":"188"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9977999925613403,"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":0.9977999925613403,"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/T11932","display_name":"Wireless Body Area Networks","score":0.9970999956130981,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9948999881744385,"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/computer-science","display_name":"Computer science","score":0.636900007724762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6227515339851379},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.5844001770019531},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.5502979755401611},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5026938915252686},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4610210359096527},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4488827586174011},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42182207107543945},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4206651449203491},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4201757311820984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.636900007724762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6227515339851379},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.5844001770019531},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.5502979755401611},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5026938915252686},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4610210359096527},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4488827586174011},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42182207107543945},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4206651449203491},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4201757311820984},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bsn.2017.7936037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bsn.2017.7936037","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W652877236","https://openalex.org/W1914380097","https://openalex.org/W1959608418","https://openalex.org/W1973675857","https://openalex.org/W1978417428","https://openalex.org/W1981011635","https://openalex.org/W1982434105","https://openalex.org/W2025217905","https://openalex.org/W2027970644","https://openalex.org/W2052607251","https://openalex.org/W2072822711","https://openalex.org/W2073401630","https://openalex.org/W2105247495","https://openalex.org/W2110989568","https://openalex.org/W2120340025","https://openalex.org/W2137093189","https://openalex.org/W2141868140","https://openalex.org/W2149350027","https://openalex.org/W2151484683","https://openalex.org/W2270470215","https://openalex.org/W4293052541","https://openalex.org/W6640963894","https://openalex.org/W6682194319"],"related_works":["https://openalex.org/W2537963312","https://openalex.org/W2066003895","https://openalex.org/W2537762514","https://openalex.org/W2349788282","https://openalex.org/W577271088","https://openalex.org/W2120801881","https://openalex.org/W1982853263","https://openalex.org/W1974473538","https://openalex.org/W2020010749","https://openalex.org/W2796116650"],"abstract_inverted_index":{"In":[0,20,101],"body":[1,13,53],"sensor":[2,54],"networks,":[3],"the":[4,12,25,84,109,151],"need":[5],"to":[6,11,18,63,99,103,143],"brace":[7],"sensing":[8,28],"devices":[9,30],"firmly":[10],"raises":[14],"a":[15,91,148],"fundamental":[16],"barrier":[17],"usability.":[19],"this":[21,35],"paper,":[22],"we":[23,46,74,107],"examine":[24],"effects":[26],"of":[27,44,90,111,125,157],"from":[29,140],"that":[31,48,76,122],"do":[32],"not":[33],"face":[34],"mounting":[36],"limitation.":[37],"With":[38],"sensors":[39],"integrated":[40],"into":[41],"common":[42],"pieces":[43],"clothing,":[45],"demonstrate":[47],"signals":[49],"in":[50,136],"such":[51],"free-mode":[52,137],"networks":[55,132,139],"are":[56],"contaminated":[57],"heavily":[58],"with":[59,129],"motion":[60,77],"artifacts":[61,78],"leading":[62],"mean":[64],"signal-to-noise":[65],"ratios":[66],"(SNRs)":[67],"as":[68,70,147],"low":[69],"-12":[71,141],"dB.":[72],"Further,":[73],"show":[75,121],"at":[79],"these":[80,105],"SNR":[81],"levels":[82],"reduce":[83],"F":[85,152],"<sub":[86,153],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[87,154],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[88,155],"-score":[89,156],"state-of-the-art":[92],"algorithm":[93],"for":[94],"human-activity":[95],"recognition":[96],"by":[97,160,165],"up":[98],"77.1%.":[100],"order":[102],"mitigate":[104],"artifacts,":[106],"evaluate":[108],"use":[110],"statistical":[112],"(Kalman":[113],"Filters)":[114],"and":[115,162],"data-driven":[116],"(Neural":[117],"Networks)":[118],"techniques.":[119],"We":[120],"well-designed":[123],"methods":[124],"representing":[126],"IMU":[127],"data":[128],"deep":[130],"neural":[131],"can":[133],"increase":[134],"SNRs":[135],"body-sensor":[138],"dB":[142,145],"+18.2":[144],"and,":[146],"result,":[149],"improve":[150],"recognizing":[158],"gestures":[159],"14.4%":[161],"locomotion":[163],"activities":[164],"55.3%.":[166]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":9}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}