{"id":"https://openalex.org/W3030996222","doi":"https://doi.org/10.1109/bigdata50022.2020.9378382","title":"Segmentation and Recognition of Eating Gestures from Wrist Motion using Deep Learning","display_name":"Segmentation and Recognition of Eating Gestures from Wrist Motion using Deep Learning","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3030996222","doi":"https://doi.org/10.1109/bigdata50022.2020.9378382","mag":"3030996222"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9378382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5080532032","display_name":"Yadnyesh Y. Luktuke","orcid":null},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yadnyesh Y. Luktuke","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Clemson University, Clemson, SC, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Clemson University, Clemson, SC, U.S.A","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009609077","display_name":"Adam Hoover","orcid":"https://orcid.org/0000-0003-2965-6524"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adam Hoover","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Clemson University, Clemson, SC, U.S.A"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Clemson University, Clemson, SC, U.S.A","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080532032"],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":1.2159,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.79057159,"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":"1368","last_page":"1373"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11398","display_name":"Hand Gesture Recognition Systems","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10866","display_name":"Nutritional Studies and Diet","score":0.9686999917030334,"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":0.9350000023841858,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8021762371063232},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.7819974422454834},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6664471626281738},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6419849991798401},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6244138479232788},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5897784233093262},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5366529822349548},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4832251965999603},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46793076395988464},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.4276845157146454},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4262520968914032},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.4215255677700043}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8021762371063232},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.7819974422454834},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6664471626281738},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6419849991798401},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6244138479232788},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5897784233093262},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5366529822349548},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4832251965999603},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46793076395988464},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.4276845157146454},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4262520968914032},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.4215255677700043},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9378382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9378382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:tigerprints.clemson.edu:all_theses-4307","is_oa":false,"landing_page_url":"https://tigerprints.clemson.edu/all_theses/3300","pdf_url":null,"source":{"id":"https://openalex.org/S4377196397","display_name":"TigerPrints (Clemson University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I8078737","host_organization_name":"Clemson University","host_organization_lineage":["https://openalex.org/I8078737"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"All Theses","raw_type":"text"},{"id":"pmh:oai:open.clemson.edu:all_theses-4307","is_oa":false,"landing_page_url":"https://open.clemson.edu/all_theses/3300","pdf_url":null,"source":{"id":"https://openalex.org/S4377196397","display_name":"TigerPrints (Clemson University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I8078737","host_organization_name":"Clemson University","host_organization_lineage":["https://openalex.org/I8078737"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"All Theses","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W326052202","https://openalex.org/W1901129140","https://openalex.org/W1990068111","https://openalex.org/W1995435322","https://openalex.org/W2016413609","https://openalex.org/W2046697072","https://openalex.org/W2089295051","https://openalex.org/W2095344888","https://openalex.org/W2116790911","https://openalex.org/W2127122874","https://openalex.org/W2128944537","https://openalex.org/W2142556291","https://openalex.org/W2162226056","https://openalex.org/W2167510172","https://openalex.org/W2293078015","https://openalex.org/W2510964718","https://openalex.org/W2516020480","https://openalex.org/W2523701324","https://openalex.org/W2577879957","https://openalex.org/W2579162453","https://openalex.org/W2609956602","https://openalex.org/W2763160469","https://openalex.org/W2909644522","https://openalex.org/W2947244567","https://openalex.org/W2949117887","https://openalex.org/W2963881378","https://openalex.org/W3030996222","https://openalex.org/W3104688303","https://openalex.org/W6639824700","https://openalex.org/W6763597339","https://openalex.org/W6779142789"],"related_works":["https://openalex.org/W4293211451","https://openalex.org/W3102253946","https://openalex.org/W4226289457","https://openalex.org/W4308191152","https://openalex.org/W3144574764","https://openalex.org/W1669643531","https://openalex.org/W2560232609","https://openalex.org/W2122581818","https://openalex.org/W2948658236","https://openalex.org/W2739874619"],"abstract_inverted_index":{"This":[0,218],"paper":[1],"describes":[2],"a":[3,16,61,71,161,179,221,234,242],"novel":[4],"approach":[5,26,67],"of":[6,27,34,65,130,151,168,194,196,200,208,238],"segmenting":[7],"and":[8,135,160,198],"classifying":[9],"eating":[10,81,231],"gestures":[11,43,171,202,210,232],"from":[12,111,233],"wrist":[13],"motion":[14],"using":[15,241],"deep":[17,222],"learning":[18,223],"neural":[19,29,72,188],"network.":[20],"It":[21,108],"is":[22,39,101],"inspired":[23],"by":[24,52,154,183],"the":[25,32,44,104,120,142],"fully-convolutional":[28],"networks":[30],"in":[31,69,145],"task":[33],"image":[35,48,94],"segmentation.":[36],"Our":[37,187],"idea":[38],"to":[40,74,89,229,245],"segment":[41,230],"1D":[42],"same":[45],"way":[46],"2D":[47],"regions":[49],"are":[50],"segmented,":[51],"treating":[53],"each":[54],"inertial":[55],"measurement":[56],"unit":[57],"(IMU)":[58],"datum":[59],"like":[60,84],"pixel.":[62],"The":[63,96],"novelty":[64],"our":[66],"lies":[68],"training":[70],"network":[73,189],"recognize":[75,90],"data":[76,97,240],"points":[77],"that":[78,114,220],"describe":[79],"an":[80,93,116,155,192,249],"gesture":[82],"just":[83],"it":[85],"would":[86],"be":[87,227],"trained":[88,185],"pixels":[91],"describing":[92],"region.":[95],"for":[98,141],"this":[99,146],"research":[100],"known":[102],"as":[103],"Clemson":[105,125],"Cafeteria":[106],"Dataset.":[107],"was":[109],"collected":[110],"276":[112],"participants":[113],"ate":[115],"unscripted":[117],"meal":[118,128,180],"at":[119,124],"Harcombe":[121],"Dining":[122],"Hall":[123],"University.":[126],"Each":[127],"consisted":[129],"1":[131],"-":[132],"4":[133],"courses,":[134],"488":[136],"such":[137],"recordings":[138],"were":[139,181,211],"used":[140,228],"experiments":[143],"described":[144],"paper.":[147],"Sensor":[148],"readings":[149],"consist":[150],"measurements":[152],"taken":[153],"accelerometer":[156],"(x,":[157],"y,":[158],"z)":[159],"gyroscope":[162],"(yaw,":[163],"pitch,":[164],"roll).":[165],"A":[166],"total":[167],"51,614":[169],"unique":[170],"associated":[172],"with":[173],"different":[174],"activities":[175],"commonly":[176],"seen":[177],"during":[178],"identified":[182],"18":[184],"raters.":[186],"classifier":[190],"recognized":[191,212],"average":[193,215],"79.7%":[195],"\u2018bite\u2019":[197],"84.7%":[199],"\u2018drink\u2019":[201],"correctly":[203,213],"per":[204,216],"meal.":[205,217],"Overall":[206],"77.7%":[207],"all":[209],"on":[214],"indicates":[219],"model":[224],"can":[225],"successfully":[226],"time":[235],"series":[236],"recording":[237],"IMU":[239],"technique":[243],"similar":[244],"pixel":[246],"segmentation":[247],"within":[248],"image.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
