{"id":"https://openalex.org/W4404030361","doi":"https://doi.org/10.1109/icccnt61001.2024.10723945","title":"Machine Learning in Motion: Enhancing Human Activity Recognition with Smartphone Sensor Data and Performance Metrics","display_name":"Machine Learning in Motion: Enhancing Human Activity Recognition with Smartphone Sensor Data and Performance Metrics","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4404030361","doi":"https://doi.org/10.1109/icccnt61001.2024.10723945"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt61001.2024.10723945","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10723945","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","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/A5111322794","display_name":"S.Sreenath Kashyap","orcid":null},"institutions":[{"id":"https://openalex.org/I101407740","display_name":"Chandigarh University","ror":"https://ror.org/05t4pvx35","country_code":"IN","type":"education","lineage":["https://openalex.org/I101407740"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shrish Kashyap","raw_affiliation_strings":["Chandigarh University,Department of Mathematics,Mohali"],"affiliations":[{"raw_affiliation_string":"Chandigarh University,Department of Mathematics,Mohali","institution_ids":["https://openalex.org/I101407740"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006526232","display_name":"Gagandeep Gagandeep","orcid":null},"institutions":[{"id":"https://openalex.org/I101407740","display_name":"Chandigarh University","ror":"https://ror.org/05t4pvx35","country_code":"IN","type":"education","lineage":["https://openalex.org/I101407740"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Gagandeep","raw_affiliation_strings":["Chandigarh University,Department of Mathematics,Mohali"],"affiliations":[{"raw_affiliation_string":"Chandigarh University,Department of Mathematics,Mohali","institution_ids":["https://openalex.org/I101407740"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111322794"],"corresponding_institution_ids":["https://openalex.org/I101407740"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19894871,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9545000195503235,"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":0.9545000195503235,"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/computer-science","display_name":"Computer science","score":0.7355371713638306},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.705718457698822},{"id":"https://openalex.org/keywords/motion-sensors","display_name":"Motion sensors","score":0.6190301179885864},{"id":"https://openalex.org/keywords/human-motion","display_name":"Human motion","score":0.5811879634857178},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5269578099250793},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.46137651801109314},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45780113339424133},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.431723415851593},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3851953446865082},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3744591474533081}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7355371713638306},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.705718457698822},{"id":"https://openalex.org/C2986565385","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion sensors","level":2,"score":0.6190301179885864},{"id":"https://openalex.org/C2986578859","wikidata":"https://www.wikidata.org/wiki/Q657632","display_name":"Human motion","level":3,"score":0.5811879634857178},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5269578099250793},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.46137651801109314},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45780113339424133},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.431723415851593},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3851953446865082},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3744591474533081}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt61001.2024.10723945","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10723945","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1650445574","https://openalex.org/W2953699171","https://openalex.org/W3214133564","https://openalex.org/W4293083375","https://openalex.org/W4306761173","https://openalex.org/W4320736771","https://openalex.org/W4360979678","https://openalex.org/W4382564633","https://openalex.org/W4389107826","https://openalex.org/W4390042982"],"related_works":["https://openalex.org/W1827696521","https://openalex.org/W2173450654","https://openalex.org/W2039848376","https://openalex.org/W2621720158","https://openalex.org/W2091722187","https://openalex.org/W2130272765","https://openalex.org/W4401486264","https://openalex.org/W2006196742","https://openalex.org/W2121113403","https://openalex.org/W2945983168"],"abstract_inverted_index":{"The":[0,45,62,98,246],"goal":[1,92,146],"of":[2,30,64,74,103,155,161,189,202,237],"this":[3,60,167],"undertaking":[4],"course,":[5],"is":[6,32,56,67,170,191],"to":[7,148,186,222,231],"enhance":[8],"the":[9,35,57,72,129,145,162,187,238,249],"efficiency":[10],"and":[11,23,41,51,77,119,126,142,153,204,215],"outcome":[12],"performance":[13],"in":[14,71,139,177,243],"Human":[15],"Activity":[16],"Recognition":[17],"(HAR)":[18],"using":[19,208],"smartphone":[20],"sensor":[21,244],"data":[22],"machine":[24,110],"learning":[25,225,254],"(ML)":[26],"approaches.":[27,226],"This":[28,86],"field":[29],"study":[31],"located":[33],"at":[34],"interface":[36],"between":[37],"pioneering":[38],"computational":[39],"techniques":[40],"practical":[42,175,205],"technical":[43],"implementations.":[44],"rising":[46],"demand":[47],"for":[48,93],"high-performance,":[49],"robust":[50],"user":[52],"friendly":[53],"HAR":[54,212],"systems":[55,66],"driver":[58],"behind":[59],"initiative.":[61],"importance":[63],"such":[65,199],"increasing":[68],"not":[69],"only":[70],"fields":[73],"medicine,":[75],"training,":[76],"cities":[78],"development":[79],"but":[80],"also":[81],"aided":[82],"by":[83],"these":[84],"areas.":[85],"investigation":[87],"will":[88],"have":[89,228],"a":[90,150],"primary":[91],"appraising":[94],"many":[95],"ML":[96,260],"models.":[97,261],"models":[99,130,190],"include":[100],"different":[101],"types":[102],"neural":[104,116],"network":[105],"architectures,":[106],"including":[107],"support":[108],"vector":[109],"(SVM),":[111],"decision":[112],"trees":[113],"(DT),":[114],"convolutional":[115],"networks":[117],"(CNN)":[118],"long":[120],"short-term":[121],"memory":[122],"(LSTM).":[123],"In":[124],"training":[125],"validation":[127],"procedures,":[128],"were":[131],"given":[132],"datasets":[133],"based":[134],"on":[135],"information":[136],"from":[137,248],"sensors":[138],"smartphones":[140],"accelerometers":[141],"gyroscopes.":[143],"Precisely,":[144],"was":[147],"provide":[149],"precise":[151],"identification":[152],"value":[154],"wide":[156],"range":[157],"human":[158],"behaviors.":[159],"One":[160],"key":[163],"components":[164],"that":[165,252],"make":[166],"course":[168],"meaningful":[169],"its":[171],"clear-cut":[172],"inclination":[173],"towards":[174],"application":[176],"real":[178],"world":[179],"situation.":[180],"To":[181],"do":[182],"this,":[183],"giving":[184],"priority":[185],"accuracy":[188],"paramount":[192],"while":[193],"simultaneously":[194],"taking":[195],"into":[196],"consideration":[197],"parameters":[198],"as":[200,220],"speed":[201],"processing":[203],"aspects":[206],"when":[207],"most":[209],"smartphones.":[210],"For":[211],"tasks,":[213],"CNN":[214],"LSTM":[216],"achieve":[217],"better":[218,257],"results":[219],"opposed":[221],"other":[223],"deep":[224,253],"They":[227],"amazing":[229],"ability":[230],"harness":[232],"more":[233],"or":[234],"less":[235],"characteristics":[236],"vibrations,":[239],"which":[240],"naturally":[241],"happen":[242],"data.":[245],"findings":[247],"studies":[250],"show":[251],"approaches":[255],"perform":[256],"than":[258],"traditional":[259]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
