{"id":"https://openalex.org/W7130338390","doi":"https://doi.org/10.1109/tencon66050.2025.11375066","title":"DGE2I-Net: Exploring Depth Gait Energy and Entropy-Based Image Features for Human Gait Classification Using Deep Neural Networks","display_name":"DGE2I-Net: Exploring Depth Gait Energy and Entropy-Based Image Features for Human Gait Classification Using Deep Neural Networks","publication_year":2025,"publication_date":"2025-10-27","ids":{"openalex":"https://openalex.org/W7130338390","doi":"https://doi.org/10.1109/tencon66050.2025.11375066"},"language":null,"primary_location":{"id":"doi:10.1109/tencon66050.2025.11375066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","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/A5101922191","display_name":"Mainak Ghosh","orcid":"https://orcid.org/0000-0002-9910-3047"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Mainak Ghosh","raw_affiliation_strings":["National Institute of Technology Rourkela,Department of Computer Science and Engineering,Rourkela,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Rourkela,Department of Computer Science and Engineering,Rourkela,India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126299385","display_name":"Sourav Biswas","orcid":null},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sourav Biswas","raw_affiliation_strings":["National Institute of Technology Rourkela,Department of Computer Science and Engineering,Rourkela,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Rourkela,Department of Computer Science and Engineering,Rourkela,India","institution_ids":["https://openalex.org/I16292982"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082301428","display_name":"Anup Nandy","orcid":"https://orcid.org/0000-0003-4865-5420"},"institutions":[{"id":"https://openalex.org/I16292982","display_name":"National Institute of Technology Rourkela","ror":"https://ror.org/011gmn932","country_code":"IN","type":"education","lineage":["https://openalex.org/I16292982"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Anup Nandy","raw_affiliation_strings":["National Institute of Technology Rourkela,Department of Computer Science and Engineering,Rourkela,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Institute of Technology Rourkela,Department of Computer Science and Engineering,Rourkela,India","institution_ids":["https://openalex.org/I16292982"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16292982"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75564764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"346","last_page":"350"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9937999844551086,"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"}},"topics":[{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9937999844551086,"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/T10114","display_name":"Balance, Gait, and Falls Prevention","score":0.00279999990016222,"subfield":{"id":"https://openalex.org/subfields/3612","display_name":"Physical Therapy, Sports Therapy and Rehabilitation"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.0003000000142492354,"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/gait","display_name":"Gait","score":0.7371000051498413},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7148000001907349},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6098999977111816},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5458999872207642},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4781999886035919},{"id":"https://openalex.org/keywords/energy","display_name":"Energy (signal processing)","score":0.4487000107765198},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43849998712539673},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4244999885559082}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7710999846458435},{"id":"https://openalex.org/C151800584","wikidata":"https://www.wikidata.org/wiki/Q2370000","display_name":"Gait","level":2,"score":0.7371000051498413},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7148000001907349},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.64410001039505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6098999977111816},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5936999917030334},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5458999872207642},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4781999886035919},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.4487000107765198},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C173906292","wikidata":"https://www.wikidata.org/wiki/Q1493441","display_name":"Gait analysis","level":3,"score":0.4009000062942505},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3368000090122223},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2971000075340271}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tencon66050.2025.11375066","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tencon66050.2025.11375066","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"TENCON 2025 - 2025 IEEE Region 10 Conference (TENCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.795213520526886,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2080718748","https://openalex.org/W2126680226","https://openalex.org/W2344595463","https://openalex.org/W2542803194","https://openalex.org/W3184090563","https://openalex.org/W4311091141","https://openalex.org/W4313327926","https://openalex.org/W4386817633","https://openalex.org/W4388591042","https://openalex.org/W4388658412","https://openalex.org/W4400527891","https://openalex.org/W4402644259","https://openalex.org/W4404075327","https://openalex.org/W4406981124","https://openalex.org/W4408323707","https://openalex.org/W4408521250"],"related_works":[],"abstract_inverted_index":{"Image-based":[0,20],"gait":[1,16,85,119,145],"analysis":[2,27],"has":[3],"become":[4],"an":[5],"important":[6],"research":[7],"area":[8],"for":[9,13,141],"extracting":[10],"comprehensive":[11],"features":[12,86,127],"classification":[14,38,142],"of":[15,36,84,103,121,143],"abnormality.":[17],"However,":[18],"traditional":[19,155],"models":[21],"mostly":[22],"focus":[23],"on":[24],"the":[25,29,34,37,104,112],"pattern":[26],"from":[28],"image":[30],"sequences,":[31],"which":[32,151],"affects":[33],"efficiency":[35],"model.":[39],"To":[40,74],"overcome":[41],"this":[42],"problem,":[43],"Gait":[44,48,89,95],"Energy":[45,90],"Image":[46,50,91,97],"and":[47,71,93,138,148],"Entropy":[49,96],"are":[51,108],"used":[52],"as":[53],"silhouette-based":[54,60],"feature":[55],"extraction":[56],"methods.":[57,156],"But":[58],"these":[59,76,107,126],"methods":[61],"frequently":[62],"face":[63],"challenges":[64],"due":[65],"to":[66,82,100,124],"variations":[67],"in":[68],"lighting,":[69],"background,":[70],"clothing":[72],"conditions.":[73],"mitigate":[75],"challenges,":[77],"depth":[78,105,118],"information":[79],"is":[80],"captured":[81],"represent":[83],"using":[87],"Depth":[88,94],"(DGEI)":[92],"(DGEnI).":[98],"Due":[99],"3D":[101],"nature":[102],"images,":[106],"not":[109],"affected":[110],"by":[111],"environmental":[113],"obstacles.":[114],"We":[115,135],"create":[116],"a":[117],"dataset":[120],"10":[122],"subjects":[123],"evaluate":[125],"with":[128,146],"two":[129],"Convolutional":[130],"Neural":[131],"Network":[132],"based":[133],"models.":[134],"achieve":[136],"96.30%":[137],"98.62%":[139],"accuracy":[140],"human":[144],"DGEI":[147],"DGEnI-features":[149],"respectively,":[150],"significantly":[152],"outperforms":[153],"many":[154]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-19T00:00:00"}