{"id":"https://openalex.org/W4313307166","doi":"https://doi.org/10.1109/ibcast54850.2022.9990303","title":"Fundamental Recognition of ADL Assessments Using Machine Learning Engineering","display_name":"Fundamental Recognition of ADL Assessments Using Machine Learning Engineering","publication_year":2022,"publication_date":"2022-08-16","ids":{"openalex":"https://openalex.org/W4313307166","doi":"https://doi.org/10.1109/ibcast54850.2022.9990303"},"language":"en","primary_location":{"id":"doi:10.1109/ibcast54850.2022.9990303","doi":"https://doi.org/10.1109/ibcast54850.2022.9990303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ibcast54850.2022.9990303","pdf_url":null,"source":{"id":"https://openalex.org/S4363608122","display_name":"International Bhurban Conference on Applied Sciences and Technology (IBCAST)","issn_l":null,"issn":null,"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":"2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5021881763","display_name":"Mouazma Batool","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I899713450","display_name":"Air University","ror":"https://ror.org/03yfe9v83","country_code":"PK","type":"education","lineage":["https://openalex.org/I899713450"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Mouazma Batool","raw_affiliation_strings":["Air University,Dept. of Computer Science,Islamabad,Pakistan","Dept. of Computer Science, Air University, Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Air University,Dept. of Computer Science,Islamabad,Pakistan","institution_ids":["https://openalex.org/I899713450"]},{"raw_affiliation_string":"Dept. of Computer Science, Air University, Islamabad, Pakistan","institution_ids":["https://openalex.org/I899713450"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089511340","display_name":"Madiha Javeed","orcid":"https://orcid.org/0000-0001-5093-7334","observed_orcids":["https://orcid.org/0000-0001-5093-7334"]},"institutions":[{"id":"https://openalex.org/I899713450","display_name":"Air University","ror":"https://ror.org/03yfe9v83","country_code":"PK","type":"education","lineage":["https://openalex.org/I899713450"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Madiha Javeed","raw_affiliation_strings":["Air University,Dept. of Computer Science,Islamabad,Pakistan","Dept. of Computer Science, Air University, Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Air University,Dept. of Computer Science,Islamabad,Pakistan","institution_ids":["https://openalex.org/I899713450"]},{"raw_affiliation_string":"Dept. of Computer Science, Air University, Islamabad, Pakistan","institution_ids":["https://openalex.org/I899713450"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I899713450"],"apc_list":null,"apc_paid":null,"fwci":0.7265,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.79913223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"195","last_page":"200"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.7821999788284302,"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":0.7821999788284302,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.20170000195503235,"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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.011300000362098217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/activities-of-daily-living","display_name":"activities of daily living","score":0.8820000290870667},{"id":"https://openalex.org/keywords/rgbd-images","display_name":"RGBD images","score":0.5799999833106995},{"id":"https://openalex.org/keywords/human-activity-recognition","display_name":"human activity recognition","score":0.8560000061988831},{"id":"https://openalex.org/keywords/lstm-rnn","display_name":"LSTM-RNN","score":0.6299999952316284},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.6990000009536743},{"id":"https://openalex.org/keywords/markerless-motion-capture","display_name":"markerless motion capture","score":0.5350000262260437}],"concepts":[{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.9672722816467285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7704225778579712},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6810727119445801},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6336500644683838},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5845463275909424},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5709820985794067},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5365199446678162},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.4689580500125885},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4494558870792389},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4175720512866974},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18625575304031372},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ibcast54850.2022.9990303","doi":"https://doi.org/10.1109/ibcast54850.2022.9990303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ibcast54850.2022.9990303","pdf_url":null,"source":{"id":"https://openalex.org/S4363608122","display_name":"International Bhurban Conference on Applied Sciences and Technology (IBCAST)","issn_l":null,"issn":null,"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":"2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":77,"referenced_works":["https://openalex.org/W92081101","https://openalex.org/W2022927695","https://openalex.org/W2079505600","https://openalex.org/W2133417935","https://openalex.org/W2614035153","https://openalex.org/W2753371832","https://openalex.org/W2803516967","https://openalex.org/W2901073115","https://openalex.org/W2909218472","https://openalex.org/W2910258641","https://openalex.org/W2921586637","https://openalex.org/W2921899828","https://openalex.org/W2922207171","https://openalex.org/W2925016180","https://openalex.org/W2932098188","https://openalex.org/W2944310648","https://openalex.org/W2964126558","https://openalex.org/W2966506379","https://openalex.org/W2974089216","https://openalex.org/W2977524124","https://openalex.org/W2977530153","https://openalex.org/W2977695604","https://openalex.org/W2995110559","https://openalex.org/W2995628968","https://openalex.org/W3005723746","https://openalex.org/W3013713530","https://openalex.org/W3013949455","https://openalex.org/W3015663082","https://openalex.org/W3015931671","https://openalex.org/W3016117922","https://openalex.org/W3023405914","https://openalex.org/W3027425326","https://openalex.org/W3042130258","https://openalex.org/W3044071265","https://openalex.org/W3091356402","https://openalex.org/W3096731295","https://openalex.org/W3108642393","https://openalex.org/W3110336329","https://openalex.org/W3110610882","https://openalex.org/W3113327043","https://openalex.org/W3121874414","https://openalex.org/W3127063527","https://openalex.org/W3128083678","https://openalex.org/W3130422393","https://openalex.org/W3132195234","https://openalex.org/W3134305571","https://openalex.org/W3136648828","https://openalex.org/W3139420163","https://openalex.org/W3153753207","https://openalex.org/W3154214969","https://openalex.org/W3155006330","https://openalex.org/W3156292869","https://openalex.org/W3156791706","https://openalex.org/W3161132553","https://openalex.org/W3163113004","https://openalex.org/W3167316949","https://openalex.org/W3183335166","https://openalex.org/W3193009145","https://openalex.org/W3215410177","https://openalex.org/W3217668285","https://openalex.org/W4213412603","https://openalex.org/W4213446433","https://openalex.org/W4214574268","https://openalex.org/W4214767594","https://openalex.org/W4220859858","https://openalex.org/W4221053107","https://openalex.org/W4226041359","https://openalex.org/W4280521791","https://openalex.org/W4282043382","https://openalex.org/W4285055452","https://openalex.org/W4285059378","https://openalex.org/W4285124013","https://openalex.org/W4285188219","https://openalex.org/W4285226278","https://openalex.org/W4288075601","https://openalex.org/W4288470889","https://openalex.org/W4319300449"],"related_works":["https://openalex.org/W4213412603","https://openalex.org/W1994792317","https://openalex.org/W2922225723","https://openalex.org/W2160131906","https://openalex.org/W7010298608","https://openalex.org/W2771696278","https://openalex.org/W2297328716","https://openalex.org/W2187234830","https://openalex.org/W2081529313","https://openalex.org/W2220115935"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"an":[3,174],"RGBD":[4,93],"(Red":[5],"Green":[6],"Blue":[7],"Depth)":[8],"image":[9],"daily":[10,46],"life":[11],"activity":[12,43],"identification":[13],"system":[14],"that":[15],"can":[16],"monitor":[17],"and":[18,72,95,107,118,124,130,146,156,176],"detect":[19],"human":[20,42,90],"activities":[21,61,98],"without":[22],"the":[23,45,85,89,111,128,136,141,159,182,191,221],"need":[24],"of":[25,59,88,110,127,135,166,179,220],"optical":[26],"markers":[27],"or":[28],"motion":[29],"sensors.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34,52,79],"have":[35,80],"developed":[36],"a":[37,54,197,204,212],"practical":[38],"methodology":[39],"for":[40,57,153],"detecting":[41],"in":[44,92,201,214],"environment.":[47],"For":[48],"feature":[49,154],"extraction":[50],"technique,":[51],"suggested":[53,192],"noble":[55],"method":[56],"recognition":[58],"ADL":[60,97],"based":[62],"on":[63],"symmetry":[64],"principle.":[65],"By":[66],"extracting":[67],"silhouette":[68],"from":[69,181],"depth":[70],"images":[71,94],"performing":[73],"mapping":[74],"operations":[75],"over":[76],"RGB":[77],"images,":[78],"been":[81],"able":[82],"to":[83,161,170,186,217],"extract":[84],"skeleton":[86],"information":[87],"body":[91,116],"identify":[96],"using":[99],"four":[100],"critical":[101],"parameters:":[102],"angle":[103,113,120,131],"formation":[104,121],"between":[105,114,122,132],"hand":[106,123],"upper":[108],"half":[109,126],"body,":[112,129],"center":[115],"point":[117],"hand,":[119],"lower":[125],"two":[133],"hands":[134],"single":[137],"silhouette.":[138],"We":[139],"employed":[140],"linearly":[142],"dependent":[143],"concept":[144],"(LDC)":[145],"long":[147],"short-term":[148],"memory-recurrent":[149],"neural":[150],"networks":[151],"(LSTM-RNN)":[152],"selection":[155],"classification,":[157],"compared":[158],"results":[160,195],"existing":[162],"approaches.":[163,189],"The":[164],"objective":[165],"our":[167],"research":[168],"is":[169],"not":[171],"only":[172],"find":[173],"effective":[175],"useful":[177],"collection":[178],"features":[180],"silhouette,":[183],"but":[184],"also":[185],"outperform":[187],"current":[188],"Finally,":[190],"method\u2019s":[193],"testing":[194],"showed":[196],"2.5":[198],"percent":[199,206,219],"increase":[200],"accuracy":[202],"with":[203],"92.83":[205],"success":[207],"rate,":[208],"as":[209,211],"well":[210],"reduction":[213],"relative":[215],"error":[216],"2.47":[218],"original":[222],"dataset.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-01-06T00:00:00"}