{"id":"https://openalex.org/W4223644476","doi":"https://doi.org/10.1109/iceic54506.2022.9748312","title":"Smartphone Based Human Activity Recognition Using 1D Lightweight Convolutional Neural Network","display_name":"Smartphone Based Human Activity Recognition Using 1D Lightweight Convolutional Neural Network","publication_year":2022,"publication_date":"2022-02-06","ids":{"openalex":"https://openalex.org/W4223644476","doi":"https://doi.org/10.1109/iceic54506.2022.9748312"},"language":"en","primary_location":{"id":"doi:10.1109/iceic54506.2022.9748312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic54506.2022.9748312","pdf_url":null,"source":{"id":"https://openalex.org/S4363608213","display_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","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/A5008993851","display_name":"Myung-Kyu Yi","orcid":"https://orcid.org/0000-0002-2360-4523"},"institutions":[{"id":"https://openalex.org/I12832649","display_name":"Gachon University","ror":"https://ror.org/03ryywt80","country_code":"KR","type":"education","lineage":["https://openalex.org/I12832649"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Myung-Kyu Yi","raw_affiliation_strings":["Gachon University,Department of Computer Engineering,Republic of Korea","Department of Computer Engineering, Gachon University, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Gachon University,Department of Computer Engineering,Republic of Korea","institution_ids":["https://openalex.org/I12832649"]},{"raw_affiliation_string":"Department of Computer Engineering, Gachon University, Republic of Korea","institution_ids":["https://openalex.org/I12832649"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086844518","display_name":"Seong Oun Hwang","orcid":"https://orcid.org/0000-0003-4240-6255"},"institutions":[{"id":"https://openalex.org/I12832649","display_name":"Gachon University","ror":"https://ror.org/03ryywt80","country_code":"KR","type":"education","lineage":["https://openalex.org/I12832649"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seong Oun Hwang","raw_affiliation_strings":["Gachon University,Department of Computer Engineering,Republic of Korea","Department of Computer Engineering, Gachon University, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Gachon University,Department of Computer Engineering,Republic of Korea","institution_ids":["https://openalex.org/I12832649"]},{"raw_affiliation_string":"Department of Computer Engineering, Gachon University, Republic of Korea","institution_ids":["https://openalex.org/I12832649"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008993851"],"corresponding_institution_ids":["https://openalex.org/I12832649"],"apc_list":null,"apc_paid":null,"fwci":0.3598,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.65884785,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998000264167786,"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":0.9998000264167786,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9930999875068665,"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"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9656999707221985,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.875648021697998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8268091678619385},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.7423562407493591},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.7133454084396362},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6158958673477173},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5380221605300903},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5037383437156677},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4186720848083496},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3736637830734253},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3405929207801819},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.24548861384391785}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.875648021697998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8268091678619385},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7423562407493591},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7133454084396362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6158958673477173},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5380221605300903},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5037383437156677},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4186720848083496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3736637830734253},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3405929207801819},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.24548861384391785},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceic54506.2022.9748312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic54506.2022.9748312","pdf_url":null,"source":{"id":"https://openalex.org/S4363608213","display_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.7699999809265137,"display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G1650114425","display_name":null,"funder_award_id":"2021-0-01532","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G6300930233","display_name":null,"funder_award_id":"2020R1A2B5B01002145","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2794833149","https://openalex.org/W2980092400","https://openalex.org/W3161770512","https://openalex.org/W3177026772"],"related_works":["https://openalex.org/W3195649134","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4399254932"],"abstract_inverted_index":{"Smartphones":[0],"are":[1,16,72],"an":[2],"obvious":[3],"platform":[4],"for":[5],"the":[6,9,63,88],"de-ployment":[7],"of":[8,20],"Human":[10],"Activity":[11],"Recognition(HAR)":[12],"system.":[13],"But,":[14],"they":[15],"limited":[17],"in":[18],"terms":[19],"processing":[21],"power,":[22],"energy":[23],"and":[24,70,105],"storage":[25],"space.":[26],"Therefore,":[27],"there":[28],"is":[29],"a":[30,50],"need":[31],"to":[32,74,94],"make":[33],"lightweight":[34,52,66],"deep":[35],"learning":[36],"models":[37],"that":[38,56,87],"can":[39,57],"be":[40,58,95],"run":[41],"within":[42],"these":[43],"constraints.":[44],"In":[45,62],"this":[46],"paper,":[47],"we":[48],"propose":[49],"one-dimensional":[51,65],"Convolutional":[53],"Neural":[54],"Network(CNN)":[55],"operated":[59],"on":[60],"smartphones.":[61],"proposed":[64,89],"CNN":[67,76,90],"model,":[68],"pruning":[69],"quantization":[71,104],"used":[73],"compress":[75],"model":[77,91],"size":[78],"without":[79],"significant":[80],"accuracy":[81,97],"losses.":[82],"The":[83],"experimental":[84],"result":[85],"shows":[86],"was":[92],"proven":[93],"successful":[96],"while":[98],"maintaining":[99],"their":[100],"performance":[101],"even":[102],"after":[103],"pruning.":[106]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
