{"id":"https://openalex.org/W4387421157","doi":"https://doi.org/10.1145/3594739.3610799","title":"iEat: Human-food interaction with bio-impedance sensing","display_name":"iEat: Human-food interaction with bio-impedance sensing","publication_year":2023,"publication_date":"2023-10-07","ids":{"openalex":"https://openalex.org/W4387421157","doi":"https://doi.org/10.1145/3594739.3610799"},"language":"en","primary_location":{"id":"doi:10.1145/3594739.3610799","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3610799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","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/A5101623985","display_name":"Mengxi Liu","orcid":"https://orcid.org/0000-0003-0527-1208"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Mengxi Liu","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Germany"],"raw_orcid":"https://orcid.org/0000-0003-0527-1208","affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101405337","display_name":"Yu Zhang","orcid":"https://orcid.org/0009-0009-2625-538X"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Germany"],"raw_orcid":"https://orcid.org/0009-0009-2625-538X","affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059956294","display_name":"Bo Zhou","orcid":"https://orcid.org/0000-0002-8976-5960"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bo Zhou","raw_affiliation_strings":["Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Germany"],"raw_orcid":"https://orcid.org/0000-0002-8976-5960","affiliations":[{"raw_affiliation_string":"Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066958860","display_name":"Sizhen Bian","orcid":"https://orcid.org/0000-0001-6760-5539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sizhen Bian","raw_affiliation_strings":["ETH Z\u00fcrih, Switzerland"],"raw_orcid":"https://orcid.org/0000-0001-6760-5539","affiliations":[{"raw_affiliation_string":"ETH Z\u00fcrih, Switzerland","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108869002","display_name":"Agnes Gr\u00fcnerbl","orcid":null},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Agnes Gr\u00fcnerbl","raw_affiliation_strings":["Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Germany"],"raw_orcid":"https://orcid.org/0000-0002-4156-7121","affiliations":[{"raw_affiliation_string":"Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063453660","display_name":"Paul Lukowicz","orcid":"https://orcid.org/0000-0003-0320-6656"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Paul Lukowicz","raw_affiliation_strings":["Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Germany"],"raw_orcid":"https://orcid.org/0000-0003-0320-6656","affiliations":[{"raw_affiliation_string":"Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI), Germany","institution_ids":["https://openalex.org/I33256026"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101623985"],"corresponding_institution_ids":["https://openalex.org/I33256026"],"apc_list":null,"apc_paid":null,"fwci":1.4102,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.81957709,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"207","last_page":"207"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.881600022315979,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10866","display_name":"Nutritional Studies and Diet","score":0.881600022315979,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.7871999740600586,"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/electrical-impedance","display_name":"Electrical impedance","score":0.6211538314819336},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.5753310322761536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5553956627845764},{"id":"https://openalex.org/keywords/food-intake","display_name":"Food intake","score":0.5002169609069824},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46864956617355347},{"id":"https://openalex.org/keywords/idle","display_name":"Idle","score":0.4443163573741913},{"id":"https://openalex.org/keywords/focused-impedance-measurement","display_name":"Focused Impedance Measurement","score":0.42500966787338257},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3760722875595093},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.37007462978363037},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.24248984456062317},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2078692615032196},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.20084130764007568},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.18414902687072754}],"concepts":[{"id":"https://openalex.org/C17829176","wikidata":"https://www.wikidata.org/wiki/Q179043","display_name":"Electrical impedance","level":2,"score":0.6211538314819336},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.5753310322761536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5553956627845764},{"id":"https://openalex.org/C3018685816","wikidata":"https://www.wikidata.org/wiki/Q213449","display_name":"Food intake","level":2,"score":0.5002169609069824},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46864956617355347},{"id":"https://openalex.org/C16320812","wikidata":"https://www.wikidata.org/wiki/Q1812200","display_name":"Idle","level":2,"score":0.4443163573741913},{"id":"https://openalex.org/C172066009","wikidata":"https://www.wikidata.org/wiki/Q5463955","display_name":"Focused Impedance Measurement","level":3,"score":0.42500966787338257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3760722875595093},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.37007462978363037},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.24248984456062317},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2078692615032196},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.20084130764007568},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.18414902687072754},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3594739.3610799","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3594739.3610799","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing &amp; the 2023 ACM International Symposium on Wearable Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.4099999964237213,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2545105407","https://openalex.org/W1530957495","https://openalex.org/W2562155397","https://openalex.org/W2752941547","https://openalex.org/W1974813547","https://openalex.org/W1990323938","https://openalex.org/W2072189119","https://openalex.org/W2740538285","https://openalex.org/W2518027987","https://openalex.org/W1982482510"],"abstract_inverted_index":{"We":[0],"explore":[1],"an":[2],"atypical":[3],"use":[4],"of":[5,23,177],"bio-impedance":[6],"by":[7,15,141],"leveraging":[8],"the":[9,16,27,82,101,106,117,120],"unique":[10],"temporal":[11],"impedance":[12,64,104],"patterns":[13],"caused":[14],"dynamic":[17],"circuit":[18],"changes":[19],"between":[20,105,116],"a":[21,44,94,131,145,150],"pair":[22],"electrodes":[24],"due":[25],"to":[26],"body":[28,103],"motions,":[29],"and":[30,35,90,122,155,173],"interactions":[31],"with":[32,66,84,168,179],"metal":[33],"utensils":[34,121],"food":[36,50,74,80,91,132,165],"during":[37],"dining":[38],"activities.":[39],"Specifically,":[40],"we":[41],"present":[42],"iEat,":[43],"wearable":[45],"impedance-sensing":[46],"device":[47],"for":[48],"automatic":[49],"intake":[51,75,133],"monitoring":[52],"without":[53,86],"using":[54],"external":[55],"devices":[56],"such":[57],"as":[58],"smart":[59],"utensils.":[60],"Using":[61],"only":[62],"one":[63,67,157],"channel":[65],"electrode":[68],"on":[69],"each":[70],"wrist,":[71],"iEat":[72,99,127,161],"detects":[73],"activities":[76,167],"(e.g.":[77],"cutting,":[78],"putting":[79],"in":[81,128,144],"mouth":[83],"or":[85],"utensils,":[87],"drinking,":[88],"etc.)":[89],"types":[92,176],"from":[93],"defined":[95],"category.":[96],"At":[97],"idle,":[98],"measures":[100],"normal":[102],"wrists;":[107],"while":[108],"eating,":[109],"new":[110],"parallel":[111],"circuits":[112],"will":[113],"be":[114],"formed":[115],"hands":[118],"through":[119],"food.":[123],"To":[124],"quantitatively":[125],"evaluate":[126],"real-world":[129],"settings,":[130],"experiment":[134],"was":[135],"conducted":[136],"including":[137],"40":[138],"meals":[139],"performed":[140],"ten":[142],"volunteers":[143],"realistic":[146],"table-dining":[147],"environment.":[148],"With":[149],"light-weight":[151],"convolutional":[152],"neural":[153],"network":[154],"leaving":[156],"subject":[158],"out":[159],"cross-validation,":[160],"could":[162],"detect":[163],"five":[164],"intake-related":[166],"86.27":[169],"%":[170,181],"average":[171,182],"accuracy,":[172],"classify":[174],"eight":[175],"foods":[178],"77.73":[180],"accuracy.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
