{"id":"https://openalex.org/W4400488299","doi":"https://doi.org/10.1109/access.2024.3426075","title":"GNet-FHO: A Light Weight Deep Neural Network for Monitoring Human Health and Activities","display_name":"GNet-FHO: A Light Weight Deep Neural Network for Monitoring Human Health and Activities","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400488299","doi":"https://doi.org/10.1109/access.2024.3426075"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3426075","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3426075","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3426075","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039167433","display_name":"Ravi Kumar Athota","orcid":"https://orcid.org/0000-0002-6838-2397"},"institutions":[{"id":"https://openalex.org/I4210131147","display_name":"SRM University","ror":"https://ror.org/037skf023","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018","https://openalex.org/I4210131147"]},{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ravi Kumar Athota","raw_affiliation_strings":["School of Computer Science Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-6838-2397","affiliations":[{"raw_affiliation_string":"School of Computer Science Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4210131147","https://openalex.org/I4401726783"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110921209","display_name":"D. Sumathi","orcid":"https://orcid.org/0000-0003-2920-4640"},"institutions":[{"id":"https://openalex.org/I4210131147","display_name":"SRM University","ror":"https://ror.org/037skf023","country_code":"IN","type":"education","lineage":["https://openalex.org/I145286018","https://openalex.org/I4210131147"]},{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"D. Sumathi","raw_affiliation_strings":["School of Computer Science Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0003-2920-4640","affiliations":[{"raw_affiliation_string":"School of Computer Science Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4210131147","https://openalex.org/I4401726783"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08816866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"108484","last_page":"108503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9995999932289124,"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":0.9995999932289124,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9732000231742859,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.9105975031852722},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8001285791397095},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6892051100730896},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6347396969795227},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"Short-time Fourier transform","score":0.5954546332359314},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.5662084817886353},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5563993453979492},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5463240742683411},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5327638983726501},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4817247688770294},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4772093892097473},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.42553389072418213},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4253504276275635},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3627324104309082},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35406702756881714},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.17768877744674683},{"id":"https://openalex.org/keywords/fourier-analysis","display_name":"Fourier analysis","score":0.13384094834327698},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09771493077278137}],"concepts":[{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.9105975031852722},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8001285791397095},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6892051100730896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6347396969795227},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.5954546332359314},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.5662084817886353},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5563993453979492},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5463240742683411},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5327638983726501},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4817247688770294},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4772093892097473},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.42553389072418213},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4253504276275635},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3627324104309082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35406702756881714},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.17768877744674683},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.13384094834327698},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09771493077278137},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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":2,"locations":[{"id":"doi:10.1109/access.2024.3426075","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3426075","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e6f49a33e2cc4589854eaa047b5ccffe","is_oa":true,"landing_page_url":"https://doaj.org/article/e6f49a33e2cc4589854eaa047b5ccffe","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 108484-108503 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3426075","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3426075","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2093945453","https://openalex.org/W2342792048","https://openalex.org/W2476501661","https://openalex.org/W2495920728","https://openalex.org/W3009186400","https://openalex.org/W3018521436","https://openalex.org/W3018775395","https://openalex.org/W3127543252","https://openalex.org/W3162602085","https://openalex.org/W3164845984","https://openalex.org/W3195795483","https://openalex.org/W3207356324","https://openalex.org/W3210766530","https://openalex.org/W3211522713","https://openalex.org/W4220939620","https://openalex.org/W4226073445","https://openalex.org/W4282575297","https://openalex.org/W4283461979","https://openalex.org/W4285306850","https://openalex.org/W4286681770","https://openalex.org/W4298121354","https://openalex.org/W4315648739","https://openalex.org/W4317209102","https://openalex.org/W4317624908","https://openalex.org/W4327714852","https://openalex.org/W4367016241","https://openalex.org/W4372197859","https://openalex.org/W4376108630","https://openalex.org/W4378842286","https://openalex.org/W4378895615","https://openalex.org/W4380607148","https://openalex.org/W4382583402","https://openalex.org/W4383899954","https://openalex.org/W4384945978","https://openalex.org/W4386097534","https://openalex.org/W4386634462","https://openalex.org/W4387817111","https://openalex.org/W4390349622","https://openalex.org/W4392523452","https://openalex.org/W6605479355","https://openalex.org/W6697274609","https://openalex.org/W6775758467"],"related_works":["https://openalex.org/W2120540196","https://openalex.org/W3095343173","https://openalex.org/W2381036744","https://openalex.org/W2288135719","https://openalex.org/W2323749021","https://openalex.org/W2533590149","https://openalex.org/W2901989338","https://openalex.org/W82005754","https://openalex.org/W2334448276","https://openalex.org/W3210733254"],"abstract_inverted_index":{"Regular":[0],"monitoring":[1],"of":[2,26,42,62,123,164,177,183,225,243],"physical":[3,33],"activities":[4,43,63],"is":[5,140,159,169,189],"essential":[6],"in":[7,31,37,58,144,153,175,262],"mitigating":[8],"the":[9,24,53,59,94,117,145,151,162,165,172,187,192,219,232,246,253,267],"risks":[10],"associated":[11],"with":[12,107,161],"diseases":[13],"like":[14],"heart":[15],"problems,":[16],"obesity,":[17],"and":[18,51,56,88,91,119,148,167,227,235,250],"diabetes.":[19],"Recent":[20],"studies":[21],"have":[22],"emphasized":[23],"significance":[25],"Human":[27],"Activity":[28],"Recognition":[29],"(HAR)":[30],"tracking":[32],"movements,":[34],"which":[35],"aids":[36],"enhancing":[38],"healthcare.":[39],"The":[40],"detection":[41],"could":[44],"be":[45],"done":[46,160],"by":[47],"analyzing":[48],"different":[49],"patterns":[50],"interpreting":[52],"signal":[54],"trends,":[55],"variations":[57],"individual":[60],"performance":[61,188],"can":[64],"lead":[65],"to":[66,111,216,231],"inconsistent":[67],"sensor":[68],"signals.":[69],"To":[70],"address":[71],"these":[72,257],"challenges,":[73],"this":[74,137],"work":[75],"utilizes":[76],"Inertial":[77],"Measurement":[78],"Unit":[79],"(IMU)":[80],"data,":[81],"converts":[82],"it":[83,168],"into":[84],"spectrograms":[85,118],"through":[86],"time":[87],"frequency":[89],"analysis,":[90],"primarily":[92],"employs":[93],"short-time":[95],"Fourier":[96],"transform":[97],"(STFT)":[98],"technique.":[99],"This":[100],"strategy":[101],"specifically":[102],"implements":[103],"Ghost":[104],"Neural":[105],"Network":[106],"Fire-Hawk":[108],"Optimizer":[109],"(GNet-FHO)":[110],"analyze":[112],"both":[113],"time-related":[114],"characteristics":[115],"from":[116],"distinct":[120],"spatial":[121,130],"attributes":[122],"each":[124],"spectrogram.":[125],"It":[126],"also":[127,186],"effectively":[128],"identifies":[129],"correlations":[131],"among":[132],"various":[133],"spectrogram":[134],"types.":[135],"Through":[136],"method,":[138],"there":[139],"a":[141,208,222,240],"remarkable":[142],"improvement":[143],"feature":[146],"extraction":[147],"thereby":[149],"enhances":[150],"accuracy":[152],"identifying":[154],"human":[155,212],"activities.":[156],"Feature":[157],"selection":[158],"help":[163],"FHO,":[166],"better":[170],"than":[171],"Adam":[173],"optimizer":[174],"context":[176],"robust":[178],"global":[179],"optimization,":[180],"effective":[181],"handling":[182],"complex":[184],"landscapes":[185],"analyzed":[190],"using":[191],"evaluation":[193],"metrics.":[194],"Experimental":[195],"results":[196],"exhibit":[197],"that":[198,265],"GNet-FHO":[199],"outperforms":[200],"other":[201],"existing":[202],"algorithms,":[203],"establishing":[204],"its":[205],"efficacy":[206],"as":[207],"lightweight":[209],"model":[210],"for":[211],"activity":[213],"recognition.":[214],"According":[215],"our":[217],"findings,":[218],"algorithms":[220],"demonstrated":[221],"success":[223,241],"rate":[224,242],"99.01%":[226],"98.97%":[228],"when":[229],"applied":[230],"WISDM":[233],"smartwatch":[234],"smartphone":[236],"dataset.":[237,255],"Additionally,":[238],"achieved":[239],"97.6%":[244],"on":[245,252],"MOTION":[247],"SENSE":[248],"dataset":[249],"95.21%":[251],"UCI-HAR":[254],"Notably,":[256],"findings":[258],"outperformed":[259],"those":[260],"published":[261],"previous":[263],"research":[264],"used":[266],"identical":[268],"datasets.":[269]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
