{"id":"https://openalex.org/W2991267567","doi":"https://doi.org/10.1109/chase48038.2019.00011","title":"Poster Abstract: Investigating Fusion-Based Deep Learning Architectures for Smoking Puff Detection","display_name":"Poster Abstract: Investigating Fusion-Based Deep Learning Architectures for Smoking Puff Detection","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W2991267567","doi":"https://doi.org/10.1109/chase48038.2019.00011","mag":"2991267567"},"language":"en","primary_location":{"id":"doi:10.1109/chase48038.2019.00011","doi":"https://doi.org/10.1109/chase48038.2019.00011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chase48038.2019.00011","pdf_url":null,"source":{"id":"https://openalex.org/S4393919729","display_name":"IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies","issn_l":"2832-2967","issn":["2832-2967"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5023142437","display_name":"Benjamin M. Marlin","orcid":"https://orcid.org/0000-0002-2626-3410","observed_orcids":["https://orcid.org/0000-0002-2626-3410"]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Benjamin Marlin","raw_affiliation_strings":["University of Massachusetts Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070743314","display_name":"Meet P. Vadera","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Meet P. Vadera","raw_affiliation_strings":["University of Massachusetts Amherst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst","institution_ids":["https://openalex.org/I24603500"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24603500"],"apc_list":null,"apc_paid":null,"fwci":0.0806,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.38703972,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"10","issue":null,"first_page":"11","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.5,"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":0.5,"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/T10060","display_name":"Smoking Behavior and Cessation","score":0.09600000083446503,"subfield":{"id":"https://openalex.org/subfields/2737","display_name":"Physiology (medical)"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.031099999323487282,"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/wearable-sensors","display_name":"wearable sensors","score":0.6209999918937683},{"id":"https://openalex.org/keywords/small-labeled-datasets","display_name":"small labeled datasets","score":0.8349999785423279},{"id":"https://openalex.org/keywords/mobile-health","display_name":"mobile health","score":0.718999981880188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7725279331207275},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7515404224395752},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.6941813826560974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6410761475563049},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5958632230758667},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.5900331139564514},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5481526851654053},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5098854303359985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5090669989585876},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.49934864044189453},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.42162787914276123},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34082961082458496},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.15885987877845764},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/chase48038.2019.00011","doi":"https://doi.org/10.1109/chase48038.2019.00011","is_oa":false,"landing_page_url":"https://doi.org/10.1109/chase48038.2019.00011","pdf_url":null,"source":{"id":"https://openalex.org/S4393919729","display_name":"IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies","issn_l":"2832-2967","issn":["2832-2967"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W179875071","https://openalex.org/W1575873342","https://openalex.org/W1854498722","https://openalex.org/W2088366166","https://openalex.org/W2135874502","https://openalex.org/W2139705460","https://openalex.org/W2159903337","https://openalex.org/W2163605009","https://openalex.org/W2170612786","https://openalex.org/W2962972936"],"related_works":["https://openalex.org/W4380928218","https://openalex.org/W4380926941","https://openalex.org/W4324070659","https://openalex.org/W2559948037","https://openalex.org/W4381747112","https://openalex.org/W4389542492","https://openalex.org/W3202210378","https://openalex.org/W4415284564","https://openalex.org/W3003924791","https://openalex.org/W4230516725"],"abstract_inverted_index":{"Supervised":[0],"deep":[1,70],"learning":[2,51],"methods":[3],"have":[4],"the":[5,34],"ability":[6],"to":[7,77],"extract":[8],"useful":[9],"features":[10,56,68],"from":[11,74,91],"raw":[12,75],"data":[13,20,38,45,76,84,94],"when":[14],"a":[15,48],"sufficient":[16],"volume":[17],"of":[18,37],"labeled":[19,44,83],"is":[21],"available":[22],"for":[23,65],"training.":[24],"However,":[25],"in":[26],"emerging":[27],"application":[28,98],"areas":[29],"such":[30],"as":[31,95],"mobile":[32],"health,":[33],"high":[35],"cost":[36],"collection":[39],"often":[40],"precludes":[41],"collecting":[42],"large-scale":[43],"sets.":[46,85],"As":[47],"result,":[49],"machine":[50],"pipelines":[52],"based":[53],"on":[54,81],"hand-engineered":[55,67],"remain":[57],"common.":[58],"In":[59],"this":[60],"paper,":[61],"we":[62],"investigate":[63],"architectures":[64],"combining":[66],"with":[69],"learning-based":[71],"feature":[72],"extraction":[73],"enhance":[78],"prediction":[79],"performance":[80],"small":[82],"We":[86],"use":[87],"smoking":[88],"puff":[89],"detection":[90],"wearable":[92],"sensor":[93],"an":[96],"example":[97],"domain.":[99]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-12-05T00:00:00"}
