{"id":"https://openalex.org/W3094515045","doi":"https://doi.org/10.1109/jiot.2020.3033173","title":"DeepSeg: Deep-Learning-Based Activity Segmentation Framework for Activity Recognition Using WiFi","display_name":"DeepSeg: Deep-Learning-Based Activity Segmentation Framework for Activity Recognition Using WiFi","publication_year":2020,"publication_date":"2020-10-22","ids":{"openalex":"https://openalex.org/W3094515045","doi":"https://doi.org/10.1109/jiot.2020.3033173","mag":"3094515045"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2020.3033173","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2020.3033173","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-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/A5108053370","display_name":"Chunjing Xiao","orcid":"https://orcid.org/0000-0001-8339-1278"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunjing Xiao","raw_affiliation_strings":["Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-8339-1278","affiliations":[{"raw_affiliation_string":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]},{"raw_affiliation_string":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111321760","display_name":"Lei Yue","orcid":"https://orcid.org/0009-0005-7788-1920"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Lei","raw_affiliation_strings":["School of Computer and Information Engineering, Henan University, Kaifeng, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Engineering, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035178314","display_name":"Yongsen Ma","orcid":"https://orcid.org/0000-0002-8321-3735"},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]},{"id":"https://openalex.org/I267592682","display_name":"Williams (United States)","ror":"https://ror.org/007zhvp17","country_code":"US","type":"company","lineage":["https://openalex.org/I267592682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongsen Ma","raw_affiliation_strings":["College of William and Mary, Williamsburg, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-8321-3735","affiliations":[{"raw_affiliation_string":"College of William and Mary, Williamsburg, VA, USA","institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403505","display_name":"Fan Zhou","orcid":"https://orcid.org/0000-0002-8038-8150"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhou","raw_affiliation_strings":["School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-8038-8150","affiliations":[{"raw_affiliation_string":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045032131","display_name":"Zhiguang Qin","orcid":"https://orcid.org/0000-0001-6745-6377"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiguang Qin","raw_affiliation_strings":["School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-6745-6377","affiliations":[{"raw_affiliation_string":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7661,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.91292636,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"8","issue":"7","first_page":"5669","last_page":"5681"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9962999820709229,"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/T11158","display_name":"Wireless Networks and Protocols","score":0.9955999851226807,"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/segmentation","display_name":"Segmentation","score":0.8719329833984375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8183035850524902},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6386356353759766},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5962814092636108},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5671281218528748},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5341208577156067},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49687865376472473},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4377847909927368},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4331511855125427},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41314831376075745},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3969321548938751},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09052088856697083},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08999809622764587}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8719329833984375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8183035850524902},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6386356353759766},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5962814092636108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5671281218528748},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5341208577156067},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49687865376472473},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4377847909927368},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4331511855125427},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41314831376075745},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3969321548938751},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09052088856697083},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08999809622764587},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2020.3033173","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2020.3033173","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1714217298","display_name":null,"funder_award_id":"61402151","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5983462902","display_name":"\u56fe\u5b66\u4e60\u4e2d\u7684\u53ef\u89e3\u91ca\u6027\u7814\u7a76","funder_award_id":"62072077","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6903982096","display_name":null,"funder_award_id":"61806074","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1628685102","https://openalex.org/W1975684011","https://openalex.org/W2002475595","https://openalex.org/W2010882865","https://openalex.org/W2036266947","https://openalex.org/W2089695767","https://openalex.org/W2095396347","https://openalex.org/W2134295053","https://openalex.org/W2149527117","https://openalex.org/W2172292165","https://openalex.org/W2340862004","https://openalex.org/W2344757691","https://openalex.org/W2404247879","https://openalex.org/W2460512640","https://openalex.org/W2466337456","https://openalex.org/W2506886870","https://openalex.org/W2515462165","https://openalex.org/W2515822248","https://openalex.org/W2517331439","https://openalex.org/W2524771588","https://openalex.org/W2534482106","https://openalex.org/W2626807326","https://openalex.org/W2739996966","https://openalex.org/W2762414408","https://openalex.org/W2763219399","https://openalex.org/W2770265759","https://openalex.org/W2776652360","https://openalex.org/W2781944640","https://openalex.org/W2783857023","https://openalex.org/W2794588549","https://openalex.org/W2794843057","https://openalex.org/W2803428049","https://openalex.org/W2808572700","https://openalex.org/W2809191403","https://openalex.org/W2811266412","https://openalex.org/W2886490782","https://openalex.org/W2890776497","https://openalex.org/W2891267845","https://openalex.org/W2899430105","https://openalex.org/W2900264681","https://openalex.org/W2909693411","https://openalex.org/W2910408172","https://openalex.org/W2919295266","https://openalex.org/W2944661854","https://openalex.org/W2951274974","https://openalex.org/W2952065976","https://openalex.org/W2954709787","https://openalex.org/W2960582000","https://openalex.org/W2964116971","https://openalex.org/W2964121744","https://openalex.org/W2964192401","https://openalex.org/W2969645632","https://openalex.org/W2970344707","https://openalex.org/W2974216175","https://openalex.org/W2989059099","https://openalex.org/W3002717429","https://openalex.org/W3006071891","https://openalex.org/W3006408325","https://openalex.org/W3047637857","https://openalex.org/W3106126084","https://openalex.org/W6631190155","https://openalex.org/W6746856007"],"related_works":["https://openalex.org/W3195649134","https://openalex.org/W2281498195","https://openalex.org/W4375867731","https://openalex.org/W2506504620","https://openalex.org/W2017526120","https://openalex.org/W2610664080","https://openalex.org/W2188304107","https://openalex.org/W2611989081","https://openalex.org/W4315434538","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Due":[0],"to":[1,47,68],"its":[2],"nonintrusive":[3],"character,":[4],"WiFi":[5,94],"channel":[6],"state":[7],"information":[8],"(CSI)-based":[9],"activity":[10,20,26,32,87,91,110,149],"recognition":[11,21,92,150],"has":[12],"attracted":[13],"tremendous":[14],"attention":[15],"in":[16],"recent":[17],"years.":[18],"Since":[19],"performance":[22,64,123],"heavily":[23],"relies":[24],"on":[25,43,57,118,144],"segmentation":[27,33,88,101,111,139],"results,":[28],"a":[29,84,108,134],"number":[30],"of":[31,40],"methods":[34,53],"have":[35],"been":[36],"designed,":[37],"and":[38,60,74,106,120],"most":[39],"them":[41],"focus":[42],"seeking":[44],"optimal":[45],"thresholds":[46],"segment":[48],"activities.":[49,76],"However,":[50],"these":[51,79],"threshold-based":[52],"are":[54],"strongly":[55],"dependent":[56],"designers'":[58],"experience":[59,119],"might":[61],"suffer":[62],"from":[63],"decline":[65],"when":[66],"applying":[67],"the":[69,116,122,129,138,145],"scenario,":[70],"including":[71],"both":[72],"fine-grained":[73],"coarse-grained":[75],"To":[77,126],"address":[78,121],"challenges,":[80],"we":[81,99,132],"present":[82],"DeepSeg,":[83],"deep":[85],"learning-based":[86],"framework":[89],"for":[90],"using":[93,148],"signals.":[95],"In":[96],"this":[97],"framework,":[98],"transform":[100],"tasks":[102],"into":[103],"classification":[104],"problems":[105],"propose":[107],"CNN-based":[109],"algorithm,":[112],"which":[113],"can":[114],"reduce":[115],"dependence":[117],"degradation":[124],"problem.":[125],"further":[127],"enhance":[128],"overall":[130],"performance,":[131],"design":[133],"feedback":[135,146],"mechanism,":[136],"where":[137],"algorithm":[140],"is":[141],"refined":[142],"based":[143],"computed":[147],"results.":[151],"The":[152],"experiments":[153],"demonstrate":[154],"that":[155],"DeepSeg":[156],"acquires":[157],"remarkable":[158],"gains":[159],"compared":[160],"with":[161],"state-of-the-art":[162],"approaches.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
