{"id":"https://openalex.org/W4382935247","doi":"https://doi.org/10.1109/iwasi58316.2023.10164436","title":"Enhanced Exploration of Neural Network Models for Indoor Human Monitoring","display_name":"Enhanced Exploration of Neural Network Models for Indoor Human Monitoring","publication_year":2023,"publication_date":"2023-06-08","ids":{"openalex":"https://openalex.org/W4382935247","doi":"https://doi.org/10.1109/iwasi58316.2023.10164436"},"language":"en","primary_location":{"id":"doi:10.1109/iwasi58316.2023.10164436","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iwasi58316.2023.10164436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","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/A5052703210","display_name":"Giorgia Subbicini","orcid":"https://orcid.org/0000-0003-0201-4222"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Giorgia Subbicini","raw_affiliation_strings":["Politecnico di Torino,Electronics and Telecommunications,Torino,Italy","Electronics and Telecommunications, Politecnico di Torino, Torino, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino,Electronics and Telecommunications,Torino,Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"Electronics and Telecommunications, Politecnico di Torino, Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050365912","display_name":"Luciano Lavagno","orcid":"https://orcid.org/0000-0002-9762-6522"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luciano Lavagno","raw_affiliation_strings":["Politecnico di Torino,Electronics and Telecommunications,Torino,Italy","Electronics and Telecommunications, Politecnico di Torino, Torino, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino,Electronics and Telecommunications,Torino,Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"Electronics and Telecommunications, Politecnico di Torino, Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030646218","display_name":"Mihai T. Lazarescu","orcid":"https://orcid.org/0000-0003-0884-5158"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mihai T. Lazarescu","raw_affiliation_strings":["Politecnico di Torino,Electronics and Telecommunications,Torino,Italy","Electronics and Telecommunications, Politecnico di Torino, Torino, Italy"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino,Electronics and Telecommunications,Torino,Italy","institution_ids":["https://openalex.org/I177477856"]},{"raw_affiliation_string":"Electronics and Telecommunications, Politecnico di Torino, Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5052703210"],"corresponding_institution_ids":["https://openalex.org/I177477856"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08103743,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":null,"first_page":"109","last_page":"114"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9979000091552734,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9908000230789185,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9883000254631042,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8251882791519165},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7172991037368774},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5350123047828674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5046323537826538},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4880315363407135},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.48152977228164673},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.47437649965286255},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4424847364425659},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4308207333087921},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.430508553981781},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3665674030780792},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.3372790217399597},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.1438179612159729}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8251882791519165},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7172991037368774},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5350123047828674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5046323537826538},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4880315363407135},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.48152977228164673},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.47437649965286255},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4424847364425659},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4308207333087921},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.430508553981781},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3665674030780792},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.3372790217399597},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.1438179612159729},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iwasi58316.2023.10164436","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iwasi58316.2023.10164436","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1821462560","https://openalex.org/W1968016787","https://openalex.org/W1980875585","https://openalex.org/W2003390621","https://openalex.org/W2034821754","https://openalex.org/W2044620160","https://openalex.org/W2055590061","https://openalex.org/W2058335156","https://openalex.org/W2089381528","https://openalex.org/W2104066809","https://openalex.org/W2145881665","https://openalex.org/W2187376963","https://openalex.org/W2492865903","https://openalex.org/W2550143307","https://openalex.org/W2553303224","https://openalex.org/W2592368662","https://openalex.org/W2625887608","https://openalex.org/W2792764867","https://openalex.org/W2811154926","https://openalex.org/W2884604806","https://openalex.org/W2952436057","https://openalex.org/W2965100203","https://openalex.org/W2965847928","https://openalex.org/W2967031846","https://openalex.org/W3009685637","https://openalex.org/W3039427918","https://openalex.org/W3045848426","https://openalex.org/W3140854437","https://openalex.org/W3191609922","https://openalex.org/W4239850762","https://openalex.org/W4283161030","https://openalex.org/W4284898748","https://openalex.org/W4285160149","https://openalex.org/W4293527381","https://openalex.org/W4307077174","https://openalex.org/W6628877408","https://openalex.org/W6638523607","https://openalex.org/W6664465318","https://openalex.org/W6729956949","https://openalex.org/W6766097413","https://openalex.org/W6846365645"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W2810679507","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Indoor":[0],"human":[1,185],"monitoring":[2,26],"can":[3,49],"enable":[4],"or":[5,18],"enhance":[6],"a":[7],"wide":[8],"range":[9],"of":[10,90],"applications,":[11],"from":[12],"medical":[13],"to":[14,31,52,117],"security":[15],"and":[16,33,39,56,74,107,123,139,150,187],"home":[17],"building":[19],"automation.":[20],"For":[21],"effective":[22],"ubiquitous":[23],"deployment,":[24],"the":[25,88,100,108,157,161,174],"system":[27],"should":[28],"be":[29,50],"easy":[30],"install":[32],"unobtrusive,":[34],"reliable,":[35],"low":[36],"cost,":[37],"tagless,":[38],"privacy-aware.":[40],"Long-range":[41],"capacitive":[42,180],"sensors":[43],"are":[44,133],"good":[45],"candidates,":[46],"but":[47,77],"they":[48,78],"susceptible":[51],"environmental":[53],"electromagnetic":[54],"noise":[55],"require":[57],"special":[58],"signal":[59],"processing.":[60],"Neural":[61],"networks":[62,68,102,111,132],"(NNs),":[63],"especially":[64],"1D":[65],"convolutional":[66,110],"neural":[67,147],"(1D-CNNs),":[69],"excel":[70],"at":[71],"extracting":[72],"information":[73],"rejecting":[75],"noise,":[76],"lose":[79],"important":[80],"relationships":[81],"in":[82],"max/average":[83],"pooling":[84],"operations.":[85],"We":[86],"investigate":[87],"performance":[89],"NN":[91],"architectures":[92],"for":[93,135,183],"time":[94],"series":[95],"analysis":[96],"without":[97],"this":[98],"shortcoming,":[99],"capsule":[101],"that":[103,113,156],"use":[104,114],"dynamic":[105],"routing,":[106],"temporal":[109],"(TCNs)":[112],"dilated":[115],"convolutions":[116],"preserve":[118],"input":[119],"resolution":[120],"across":[121],"layers":[122],"extend":[124],"their":[125],"receptive":[126],"field":[127],"with":[128,168],"fewer":[129],"layers.":[130],"The":[131],"optimized":[134],"both":[136],"inference":[137,166],"accuracy":[138],"resource":[140,171],"consumption":[141,172],"using":[142],"two":[143],"independent":[144],"state-of-the-art":[145],"methods,":[146],"architecture":[148,159],"search":[149],"knowledge":[151],"distillation.":[152],"Experimental":[153],"results":[154],"show":[155],"TCN":[158],"performs":[160],"best,":[162],"achieving":[163],"12.7%":[164],"lower":[165],"loss":[167],"73.3%":[169],"less":[170],"than":[173],"best":[175],"1D-CNN":[176],"when":[177],"processing":[178],"noisy":[179],"sensor":[181],"data":[182],"indoor":[184],"localization":[186],"tracking.":[188]},"counts_by_year":[],"updated_date":"2025-12-21T01:58:51.020947","created_date":"2025-10-10T00:00:00"}
