{"id":"https://openalex.org/W4401113721","doi":"https://doi.org/10.1109/tsp63128.2024.10605952","title":"Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition","display_name":"Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition","publication_year":2024,"publication_date":"2024-07-10","ids":{"openalex":"https://openalex.org/W4401113721","doi":"https://doi.org/10.1109/tsp63128.2024.10605952"},"language":"en","primary_location":{"id":"doi:10.1109/tsp63128.2024.10605952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp63128.2024.10605952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 47th International Conference on Telecommunications and Signal Processing (TSP)","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/A5068798343","display_name":"Sakorn Mekruksavanich","orcid":"https://orcid.org/0000-0002-3735-4262"},"institutions":[{"id":"https://openalex.org/I4210090662","display_name":"University of Phayao","ror":"https://ror.org/00a5mh069","country_code":"TH","type":"education","lineage":["https://openalex.org/I4210090662"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Sakorn Mekruksavanich","raw_affiliation_strings":["School of Information and Communication Technology, University of Phayao,Department of Computer Engineering,Phayao,Thailand"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, University of Phayao,Department of Computer Engineering,Phayao,Thailand","institution_ids":["https://openalex.org/I4210090662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038701071","display_name":"Wikanda Phaphan","orcid":"https://orcid.org/0000-0002-6082-4779"},"institutions":[{"id":"https://openalex.org/I82828225","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97","country_code":"TH","type":"education","lineage":["https://openalex.org/I82828225"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Wikanda Phaphan","raw_affiliation_strings":["Research Group in Statistical Learning and Inference, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Applied Statistics Faculty of Applied Science,Bangkok,Thailand"],"affiliations":[{"raw_affiliation_string":"Research Group in Statistical Learning and Inference, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Applied Statistics Faculty of Applied Science,Bangkok,Thailand","institution_ids":["https://openalex.org/I82828225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085644461","display_name":"Anuchit Jitpattanakul","orcid":"https://orcid.org/0000-0002-5249-2786"},"institutions":[{"id":"https://openalex.org/I82828225","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97","country_code":"TH","type":"education","lineage":["https://openalex.org/I82828225"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Anuchit Jitpattanakul","raw_affiliation_strings":["Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand"],"affiliations":[{"raw_affiliation_string":"Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut&#x0027;s University of Technology North Bangkok,Department of Mathematics Faculty of Applied Science,Bangkok,Thailand","institution_ids":["https://openalex.org/I82828225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068798343"],"corresponding_institution_ids":["https://openalex.org/I4210090662"],"apc_list":null,"apc_paid":null,"fwci":0.2632,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.51529608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"156","last_page":"159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.897599995136261,"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":0.897599995136261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7068732380867004},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6197549104690552},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5677334666252136},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5198745727539062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49524012207984924},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42668667435646057},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07550439238548279}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7068732380867004},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6197549104690552},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5677334666252136},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5198745727539062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49524012207984924},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42668667435646057},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07550439238548279}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp63128.2024.10605952","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp63128.2024.10605952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 47th International Conference on Telecommunications and Signal Processing (TSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1946890873","display_name":null,"funder_award_id":"FF67-UoE-214","funder_id":"https://openalex.org/F4320326818","funder_display_name":"University of Phayao"},{"id":"https://openalex.org/G8056027561","display_name":null,"funder_award_id":"KMUTNB-FF-67-B-10","funder_id":"https://openalex.org/F4320324344","funder_display_name":"King Mongkut's University of Technology North Bangkok"}],"funders":[{"id":"https://openalex.org/F4320324344","display_name":"King Mongkut's University of Technology North Bangkok","ror":"https://ror.org/04fy6jb97"},{"id":"https://openalex.org/F4320326818","display_name":"University of Phayao","ror":"https://ror.org/00a5mh069"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2057907879","https://openalex.org/W2805240935","https://openalex.org/W2919115771","https://openalex.org/W3088411822","https://openalex.org/W3163690265","https://openalex.org/W3182319652","https://openalex.org/W3194178254","https://openalex.org/W3203762492","https://openalex.org/W3210766530","https://openalex.org/W3217615879","https://openalex.org/W4210718128","https://openalex.org/W4283013741","https://openalex.org/W4296400853","https://openalex.org/W4304142225","https://openalex.org/W4308078775","https://openalex.org/W4313201252","https://openalex.org/W4313585941","https://openalex.org/W4318587306","https://openalex.org/W4353069334","https://openalex.org/W4384557825","https://openalex.org/W4385277267","https://openalex.org/W4385325179","https://openalex.org/W4385498088","https://openalex.org/W4387609072","https://openalex.org/W4390403684","https://openalex.org/W4390738727","https://openalex.org/W4393170655","https://openalex.org/W4399058647"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"This":[0],"study":[1],"introduces":[2],"a":[3],"novel":[4],"approach":[5,60],"to":[6,75],"identifying":[7],"human":[8,86],"activities":[9],"using":[10],"wearable":[11],"sensors,":[12],"particularly":[13,94],"smart-phones":[14],"and":[15,23,32,56,64,80,114],"smartwatches.":[16],"By":[17],"leveraging":[18],"deep":[19,82],"learning":[20,78,83],"neural":[21],"networks":[22],"data":[24,34],"from":[25,35],"the":[26,45,109,116],"HHAR":[27],"dataset,":[28],"which":[29],"includes":[30],"accelerometer":[31],"gyroscope":[33],"individuals":[36],"engaged":[37],"in":[38,67,95],"various":[39],"activities,":[40],"our":[41,59],"method,":[42],"centered":[43],"around":[44],"HAR-Res":[46],"NeXt":[47],"model,":[48],"accurately":[49],"detects":[50],"six":[51],"activities.":[52],"Utilizing":[53],"residual":[54],"connections":[55],"multi-kernel":[57],"blocks,":[58],"effectively":[61],"captures":[62],"temporal":[63],"spatial":[65],"relationships":[66],"sensor":[68,98,120],"data.":[69],"Experimental":[70],"results":[71],"demonstrate":[72],"superior":[73],"performance":[74],"standard":[76],"machine":[77],"algorithms":[79],"other":[81],"approaches":[84],"for":[85,122],"activity":[87,124],"recognition.":[88],"HAR-ResNeXt":[89],"achieves":[90],"high":[91],"accuracy":[92],"rates,":[93],"classifying":[96],"smartphone":[97,112],"data,":[99],"underscoring":[100],"its":[101],"adaptability":[102],"across":[103],"diverse":[104],"scenarios.":[105],"Comparative":[106],"analysis":[107],"reveals":[108],"effectiveness":[110],"of":[111,118],"sensors":[113],"emphasizes":[115],"importance":[117],"multi-modal":[119],"fusion":[121],"accurate":[123],"detection.":[125]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
