{"id":"https://openalex.org/W4292230836","doi":"https://doi.org/10.1109/icc45855.2022.9839267","title":"Improving Human Activity Recognition using ML and Wearable Sensors","display_name":"Improving Human Activity Recognition using ML and Wearable Sensors","publication_year":2022,"publication_date":"2022-05-16","ids":{"openalex":"https://openalex.org/W4292230836","doi":"https://doi.org/10.1109/icc45855.2022.9839267"},"language":"en","primary_location":{"id":"doi:10.1109/icc45855.2022.9839267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9839267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2022 - IEEE International Conference on Communications","raw_type":"proceedings-article"},"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/A5076580590","display_name":"Gael S. Mubibya","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Gael S. Mubibya","raw_affiliation_strings":["Université de Moncton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Université de Moncton","institution_ids":["https://openalex.org/I154799132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111912045","display_name":"Jalal Almhana","orcid":null},"institutions":[{"id":"https://openalex.org/I154799132","display_name":"Universit\u00e9 de Moncton","ror":"https://ror.org/029tnqt29","country_code":"CA","type":"education","lineage":["https://openalex.org/I154799132"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jalal Almhana","raw_affiliation_strings":["Université de Moncton"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Université de Moncton","institution_ids":["https://openalex.org/I154799132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I154799132"],"apc_list":null,"apc_paid":null,"fwci":1.202,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.86790434,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"165","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9998999834060669,"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":0.9998999834060669,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.7505372166633606},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.6632106900215149},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6622597575187683},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.6552086472511292},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6515034437179565},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.6217140555381775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6213436722755432},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6162222623825073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6103010177612305},{"id":"https://openalex.org/keywords/wearable-technology","display_name":"Wearable technology","score":0.604211688041687},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.572314977645874},{"id":"https://openalex.org/keywords/gyroscope","display_name":"Gyroscope","score":0.5318386554718018},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.526997983455658},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5038437247276306},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.49520984292030334},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.42615532875061035},{"id":"https://openalex.org/keywords/statistical-classification","display_name":"Statistical classification","score":0.41156670451164246},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37516510486602783},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.1173669695854187},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11180505156517029},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11071935296058655}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7505372166633606},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.6632106900215149},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6622597575187683},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6552086472511292},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6515034437179565},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.6217140555381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6213436722755432},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6162222623825073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6103010177612305},{"id":"https://openalex.org/C54290928","wikidata":"https://www.wikidata.org/wiki/Q4845080","display_name":"Wearable technology","level":3,"score":0.604211688041687},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.572314977645874},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.5318386554718018},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.526997983455658},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5038437247276306},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.49520984292030334},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.42615532875061035},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.41156670451164246},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37516510486602783},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.1173669695854187},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11180505156517029},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11071935296058655},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc45855.2022.9839267","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9839267","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2022 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5400000214576721,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1564237868","https://openalex.org/W2026297770","https://openalex.org/W2044628302","https://openalex.org/W2054242744","https://openalex.org/W2063598276","https://openalex.org/W2766054595","https://openalex.org/W2770587725","https://openalex.org/W2805637662","https://openalex.org/W2924509255","https://openalex.org/W2963373106","https://openalex.org/W2979737317","https://openalex.org/W2984877669","https://openalex.org/W2996833046","https://openalex.org/W3030475315","https://openalex.org/W3097300016","https://openalex.org/W3113797302","https://openalex.org/W3125167453","https://openalex.org/W4289363592","https://openalex.org/W6755346545","https://openalex.org/W6769147564"],"related_works":["https://openalex.org/W1889129279","https://openalex.org/W4387451989","https://openalex.org/W2532412374","https://openalex.org/W2063137106","https://openalex.org/W2358715846","https://openalex.org/W2020920196","https://openalex.org/W4229455305","https://openalex.org/W2582769230","https://openalex.org/W4207072607","https://openalex.org/W2067125787"],"abstract_inverted_index":{"The":[0],"Internet":[1],"of":[2,8,13,22,48,67,118,133,136,150,178,192,222],"Things":[3],"(IoT)":[4],"generates":[5],"massive":[6],"amounts":[7],"data":[9,25],"everywhere":[10],"through":[11],"sensors":[12,119],"every":[14],"kind":[15],"which":[16,38,233],"are":[17,91],"disseminated":[18],"in":[19,37,52,164,176,238],"a":[20,74,169,181,239],"variety":[21],"objects.":[23],"This":[24],"contains":[26],"incredibly":[27],"valuable":[28],"information":[29],"useful":[30],"for":[31,152],"multiple":[32],"applications.":[33],"Knowing":[34],"the":[35,49,54,116,131,148,165,173,190,219,235,244],"context":[36,110],"it":[39,56],"was":[40],"generated":[41],"is":[42,73,225],"extremely":[43],"important":[44,65],"and":[45,76,95,141,144,188,211,230],"constitutes":[46],"one":[47],"first":[50],"steps":[51],"extracting":[53],"knowledge":[55],"contains.":[57],"Thereby,":[58],"Context-Aware":[59],"Learning":[60],"(CAL)":[61],"has":[62],"become":[63],"an":[64],"area":[66],"research":[68],"as":[69],"machine":[70],"learning":[71],"(ML)":[72],"fast":[75],"ever-evolving":[77],"technology.":[78],"Wearable":[79],"devices,":[80],"ranging":[81],"from":[82],"accelerometers":[83],"(ACC),":[84],"frequently":[85,162],"used,":[86],"to":[87,93],"magnetic":[88,158],"field":[89,159],"sensors,":[90,160],"used":[92,120,163],"monitor":[94],"recognize":[96],"human":[97],"activities":[98],"(HA).":[99],"Beyond":[100],"ML":[101],"Algorithms":[102],"(MLA),":[103],"accurate":[104],"Human":[105],"Activities":[106],"Recognition":[107],"(HAR)":[108],"or":[109],"identification,":[111],"depends":[112],"not":[113],"only":[114],"on":[115,123,147,243],"kinds":[117],"but":[121],"also":[122],"their":[124,145],"location.":[125],"In":[126],"this":[127],"paper,":[128],"we":[129,186],"study":[130],"impact":[132],"three":[134],"types":[135],"sensors:":[137],"ACC,":[138],"gyroscope":[139],"(GYR),":[140],"magnetometer":[142],"(MAG);":[143],"locations":[146],"performance":[149,175,191],"MLA":[151],"HAR.":[153,179],"Our":[154,215],"results":[155,216,236],"show":[156,217],"that":[157,218],"less":[161],"literature,":[166],"placed":[167],"at":[168],"specific":[170],"location,":[171],"provide":[172],"best":[174],"terms":[177],"Using":[180],"publicly":[182],"available":[183],"dataset,":[184],"PAMAP2,":[185],"implement":[187],"evaluate":[189],"HAR":[193],"using":[194],"five":[195],"MLA:":[196],"Linear":[197],"Discriminant":[198,202],"Analysis":[199,203],"(LDA),":[200],"Quadratic":[201],"(QLA),":[204],"K-Nearest":[205],"Neighbors":[206],"(KNN),":[207],"Decision":[208],"Tree":[209],"(DT),":[210],"Random":[212],"Forest":[213],"(RF).":[214],"success":[220],"rate":[221],"these":[223],"algorithms":[224],"98.3%,":[226],"90.4%,":[227],"97.6%,":[228],"99.9%,":[229],"100%":[231],"respectively,":[232],"exceeds":[234],"obtained":[237],"previous":[240],"work":[241],"based":[242],"same":[245],"dataset.":[246]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}