{"id":"https://openalex.org/W3036546791","doi":"https://doi.org/10.1109/tim.2020.3003395","title":"An Automated Daily Sports Activities and Gender Recognition Method Based on Novel Multikernel Local Diamond Pattern Using Sensor Signals","display_name":"An Automated Daily Sports Activities and Gender Recognition Method Based on Novel Multikernel Local Diamond Pattern Using Sensor Signals","publication_year":2020,"publication_date":"2020-06-18","ids":{"openalex":"https://openalex.org/W3036546791","doi":"https://doi.org/10.1109/tim.2020.3003395","mag":"3036546791"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2020.3003395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3003395","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Transactions on Instrumentation and Measurement","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/A5029287310","display_name":"T\u00fcrker Tuncer","orcid":null},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Turker Tuncer","raw_affiliation_strings":["Digital Forensics Engineering, Technology Faculty, Firat University, El\u00e2z\u0131\u011f, Turkey"],"raw_orcid":"https://orcid.org/0000-0002-1425-4664","affiliations":[{"raw_affiliation_string":"Digital Forensics Engineering, Technology Faculty, Firat University, El\u00e2z\u0131\u011f, Turkey","institution_ids":["https://openalex.org/I143396566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047672864","display_name":"Fatih Ertam","orcid":"https://orcid.org/0000-0002-9736-8068"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Fatih Ertam","raw_affiliation_strings":["Digital Forensics Engineering, Technology Faculty, Firat University, El\u00e2z\u0131\u011f, Turkey"],"raw_orcid":"https://orcid.org/0000-0002-9736-8068","affiliations":[{"raw_affiliation_string":"Digital Forensics Engineering, Technology Faculty, Firat University, El\u00e2z\u0131\u011f, Turkey","institution_ids":["https://openalex.org/I143396566"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040772000","display_name":"\u015eeng\u00fcl Do\u011fan","orcid":"https://orcid.org/0000-0001-9677-5684"},"institutions":[{"id":"https://openalex.org/I143396566","display_name":"F\u0131rat University","ror":"https://ror.org/05teb7b63","country_code":"TR","type":"education","lineage":["https://openalex.org/I143396566"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sengul Dogan","raw_affiliation_strings":["Digital Forensics Engineering, Technology Faculty, Firat University, El\u00e2z\u0131\u011f, Turkey"],"raw_orcid":"https://orcid.org/0000-0001-9677-5684","affiliations":[{"raw_affiliation_string":"Digital Forensics Engineering, Technology Faculty, Firat University, El\u00e2z\u0131\u011f, Turkey","institution_ids":["https://openalex.org/I143396566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076009908","display_name":"Abd\u00fclhamit Suba\u015f\u0131","orcid":"https://orcid.org/0000-0001-7630-4084"},"institutions":[{"id":"https://openalex.org/I125656591","display_name":"Effat University","ror":"https://ror.org/02cnwgt19","country_code":"SA","type":"education","lineage":["https://openalex.org/I125656591"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Abdulhamit Subasi","raw_affiliation_strings":["College of Engineering, Effat University, Jeddah, Saudi Arabia"],"raw_orcid":"https://orcid.org/0000-0001-7630-4084","affiliations":[{"raw_affiliation_string":"College of Engineering, Effat University, Jeddah, Saudi Arabia","institution_ids":["https://openalex.org/I125656591"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1114,"has_fulltext":false,"cited_by_count":54,"citation_normalized_percentile":{"value":0.95207678,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"69","issue":"12","first_page":"9441","last_page":"9448"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9957000017166138,"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":0.9957000017166138,"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":0.9902999997138977,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7570472359657288},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7518717050552368},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7179926633834839},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.710252046585083},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6213861107826233},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4955967664718628},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.4523443281650543},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3565278649330139},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1326064169406891}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7570472359657288},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7518717050552368},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7179926633834839},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.710252046585083},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6213861107826233},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4955967664718628},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.4523443281650543},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3565278649330139},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1326064169406891},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2020.3003395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2020.3003395","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1430732283","https://openalex.org/W1507216665","https://openalex.org/W1518320710","https://openalex.org/W1686810756","https://openalex.org/W1964793210","https://openalex.org/W1969069964","https://openalex.org/W1975043475","https://openalex.org/W2000931321","https://openalex.org/W2028538780","https://openalex.org/W2054780155","https://openalex.org/W2092493898","https://openalex.org/W2097117768","https://openalex.org/W2123504417","https://openalex.org/W2194775991","https://openalex.org/W2317216178","https://openalex.org/W2323192341","https://openalex.org/W2482927090","https://openalex.org/W2520949346","https://openalex.org/W2550476060","https://openalex.org/W2593796416","https://openalex.org/W2605666222","https://openalex.org/W2613648063","https://openalex.org/W2617726104","https://openalex.org/W2617995709","https://openalex.org/W2736191430","https://openalex.org/W2736707111","https://openalex.org/W2757995887","https://openalex.org/W2770265759","https://openalex.org/W2794045421","https://openalex.org/W2795342689","https://openalex.org/W2880592144","https://openalex.org/W2892176301","https://openalex.org/W2911792760","https://openalex.org/W2929827605","https://openalex.org/W2937800999","https://openalex.org/W2938260150","https://openalex.org/W2944383286","https://openalex.org/W2945979798","https://openalex.org/W2947481730","https://openalex.org/W2965907538","https://openalex.org/W2966886210","https://openalex.org/W2981850489","https://openalex.org/W4288375845","https://openalex.org/W6628269818","https://openalex.org/W6637373629","https://openalex.org/W6761116321"],"related_works":["https://openalex.org/W1976280679","https://openalex.org/W1978011637","https://openalex.org/W3211626993","https://openalex.org/W2122718025","https://openalex.org/W2169521742","https://openalex.org/W2900460335","https://openalex.org/W771773272","https://openalex.org/W4288057579","https://openalex.org/W2508787685","https://openalex.org/W3128113522"],"abstract_inverted_index":{"Sensor":[0],"signals":[1],"have":[2],"been":[3],"frequently":[4],"used":[5,140],"for":[6,33,74,146,185],"activity":[7,76,166,172],"recognition":[8,77],"and":[9,52,56,64,99,104,133,157,168,171,183,190,193,209,221],"gender":[10,164,170,192],"classification":[11,105],"in":[12],"the":[13,118,129,135,144,154,207,218],"literature.":[14],"In":[15],"this":[16],"study,":[17],"a":[18,28,46,70],"new":[19],"multikernel":[20,57],"local":[21,30],"diamond":[22],"pattern":[23],"(MK-LDP)":[24],"is":[25,39,67,203,224],"proposed":[26,37,80,112,155,200,219],"as":[27,69,141],"novel":[29],"descriptor":[31],"method":[32,82,202],"feature":[34,71,89],"extraction.":[35],"The":[36,79,111,175,199,213],"MK-LDP":[38,66,113],"aimed":[40],"to":[41,152,206],"generate":[42],"distinctive":[43],"features":[44,94,116,138],"from":[45,117,128],"signal":[47],"or":[48],"image":[49],"using":[50,96,106,227],"vertical":[51],"horizontal":[53],"diamond-like":[54],"patterns":[55],"functions.":[58],"These":[59,161],"kernels":[60],"are":[61,139,150,163,180],"signum,":[62],"ternary,":[63],"quaternary.":[65],"utilized":[68],"extraction":[72],"technique":[73],"human":[75],"(HAR).":[78],"HAR":[81,159,226],"has":[83],"four":[84],"fundamental":[85],"phases,":[86],"namely,":[87],"preprocessing,":[88],"generation":[90],"with":[91],"MK-LDP,":[92],"informative":[93],"selection":[95],"hybrid":[97],"ReliefF":[98],"neighborhood":[100],"component":[101],"analysis":[102],"(RFNCA),":[103],"support":[107],"vector":[108],"machine":[109],"(SVM).":[110],"extracts":[114],"2560":[115,131],"raw":[119],"sensor":[120,228],"signals,":[121],"RFINCA":[122],"selects":[123],"512":[124,137],"most":[125],"meaningful":[126],"ones":[127],"extracted":[130],"features,":[132],"then":[134],"selected":[136],"input":[142],"of":[143],"SVM":[145],"HAR.":[147],"Three":[148],"cases":[149,162],"defined":[151],"test":[153],"MK-LDP-":[156,220],"RFNCA-based":[158,222],"method.":[160],"classification,":[165,167,173],"both":[169,191],"respectively.":[174,198],"achieved":[176],"best":[177],"accuracy":[178],"rates":[179],"99.47%,":[181],"99.71%,":[182],"99.36%":[184],"gender,":[186],"daily":[187,194],"sports":[188,195],"activities,":[189],"activities":[196],"recognition,":[197],"MK-LDP-based":[201],"also":[204],"compared":[205],"state-of-the-art":[208],"deep":[210],"learning":[211],"techniques.":[212],"obtained":[214],"results":[215],"revealed":[216],"that":[217],"framework":[223],"successful":[225],"signals.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}