{"id":"https://openalex.org/W3006654525","doi":"https://doi.org/10.1109/tii.2020.2972628","title":"Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition","display_name":"Interpretable and Accurate Convolutional Neural Networks for Human Activity Recognition","publication_year":2020,"publication_date":"2020-02-10","ids":{"openalex":"https://openalex.org/W3006654525","doi":"https://doi.org/10.1109/tii.2020.2972628","mag":"3006654525"},"language":"en","primary_location":{"id":"doi:10.1109/tii.2020.2972628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.2972628","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","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/A5100319320","display_name":"Eunji Kim","orcid":"https://orcid.org/0000-0003-4857-521X"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Eunji Kim","raw_affiliation_strings":["Department of Industrial Engineering, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-4857-521X","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100319320"],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":4.4135,"has_fulltext":false,"cited_by_count":80,"citation_normalized_percentile":{"value":0.95624814,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"16","issue":"11","first_page":"7190","last_page":"7198"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9995999932289124,"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":0.9995999932289124,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9692999720573425,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9351000189781189,"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/interpretability","display_name":"Interpretability","score":0.9713152647018433},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7673384547233582},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7613943815231323},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7441526055335999},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6386255621910095},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6320450901985168},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5723140835762024},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5221034288406372},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5187814235687256},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.4860577881336212},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.48437485098838806},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4629453420639038},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42472514510154724},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09043213725090027}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9713152647018433},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7673384547233582},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7613943815231323},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7441526055335999},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6386255621910095},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6320450901985168},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5723140835762024},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5221034288406372},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5187814235687256},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.4860577881336212},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.48437485098838806},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4629453420639038},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42472514510154724},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09043213725090027},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","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},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tii.2020.2972628","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tii.2020.2972628","pdf_url":null,"source":{"id":"https://openalex.org/S184777250","display_name":"IEEE Transactions on Industrial Informatics","issn_l":"1551-3203","issn":["1551-3203","1941-0050"],"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 Industrial Informatics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W119403003","https://openalex.org/W1927170233","https://openalex.org/W1987371344","https://openalex.org/W2002261403","https://openalex.org/W2008311091","https://openalex.org/W2073401630","https://openalex.org/W2126511896","https://openalex.org/W2135046866","https://openalex.org/W2138019504","https://openalex.org/W2143517695","https://openalex.org/W2270470215","https://openalex.org/W2288074780","https://openalex.org/W2295647538","https://openalex.org/W2342792048","https://openalex.org/W2460144244","https://openalex.org/W2550797816","https://openalex.org/W2551239383","https://openalex.org/W2553427643","https://openalex.org/W2553915786","https://openalex.org/W2554931888","https://openalex.org/W2594332903","https://openalex.org/W2604630936","https://openalex.org/W2605049121","https://openalex.org/W2736191430","https://openalex.org/W2741485222","https://openalex.org/W2759690896","https://openalex.org/W2946530240","https://openalex.org/W2963000224","https://openalex.org/W2963855167","https://openalex.org/W4302613003","https://openalex.org/W6696429117","https://openalex.org/W6704286305","https://openalex.org/W6725543821","https://openalex.org/W6730382033","https://openalex.org/W6741783443"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W2888392564","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W2806259446","https://openalex.org/W3105278570","https://openalex.org/W2117913171","https://openalex.org/W2582769230"],"abstract_inverted_index":{"With":[0],"the":[1,33,40,53,67,128,135],"advances":[2],"of":[3,55,92],"sensing":[4],"technology":[5],"and":[6,36,86,120,143],"deep":[7,9,24],"learning,":[8],"learning":[10],"based":[11],"human":[12],"activity":[13,130],"recognition":[14,41,131,141],"from":[15,61],"sensor":[16,48,70,95,146],"signal":[17,71],"data":[18],"has":[19],"been":[20],"actively":[21],"studied.":[22],"While":[23,113],"neural":[25,89],"networks":[26],"can":[27,138],"automatically":[28],"extract":[29],"features":[30],"appropriate":[31],"for":[32,66,77],"target":[34,68],"task":[35],"focus":[37],"on":[38,127],"increasing":[39],"performance,":[42],"they":[43],"cannot":[44],"select":[45,93,144],"important":[46,65,94,145],"input":[47],"signals,":[49,147],"which":[50],"leads":[51],"to":[52],"lack":[54],"interpretability.":[56,149],"Since":[57],"not":[58],"all":[59],"signals":[60],"wearable":[62],"sensors":[63],"are":[64],"task,":[69],"importance":[72],"will":[73],"be":[74],"insightful":[75],"information":[76],"practitioners.":[78],"In":[79],"this":[80],"article,":[81],"we":[82],"propose":[83],"an":[84],"interpretable":[85],"accurate":[87],"convolutional":[88,103],"network":[90],"capable":[91],"signals.":[96],"This":[97],"is":[98,107,115],"enabled":[99],"by":[100,109],"spatially":[101],"sparse":[102],"filters":[104],"whose":[105],"sparsity":[106],"imposed":[108],"spatial":[110],"group":[111],"lasso.":[112],"there":[114],"a":[116,123],"tradeoff":[117],"between":[118],"accuracy":[119],"interpretability":[121],"in":[122],"model,":[124],"experimental":[125],"results":[126],"opportunity":[129],"dataset":[132],"show":[133],"that":[134],"proposed":[136],"model":[137],"help":[139],"improve":[140],"performance":[142],"providing":[148]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":18},{"year":2022,"cited_by_count":18},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5}],"updated_date":"2026-05-18T08:16:58.900851","created_date":"2025-10-10T00:00:00"}