{"id":"https://openalex.org/W4404031576","doi":"https://doi.org/10.1109/icccnt61001.2024.10725414","title":"Enhanced Sports Performance Analysis: CNNLSTM Based Detection of Tennis Serve Variations","display_name":"Enhanced Sports Performance Analysis: CNNLSTM Based Detection of Tennis Serve Variations","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4404031576","doi":"https://doi.org/10.1109/icccnt61001.2024.10725414"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt61001.2024.10725414","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10725414","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/A5047312965","display_name":"Vikrant Sharma","orcid":"https://orcid.org/0000-0003-3178-8657"},"institutions":[{"id":"https://openalex.org/I60054993","display_name":"Graphic Era University","ror":"https://ror.org/03wqgqd89","country_code":"IN","type":"education","lineage":["https://openalex.org/I60054993"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vikrant Sharma","raw_affiliation_strings":["Graphic Era Hill University Graphic Era Deemed to be University,Computer Science and Engineering,Dehradun,India,248002"],"affiliations":[{"raw_affiliation_string":"Graphic Era Hill University Graphic Era Deemed to be University,Computer Science and Engineering,Dehradun,India,248002","institution_ids":["https://openalex.org/I60054993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061558029","display_name":"Shiva Mehta","orcid":"https://orcid.org/0009-0002-5537-7027"},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shiva Mehta","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology Chitkara University Punjab,India"],"affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology Chitkara University Punjab,India","institution_ids":["https://openalex.org/I74319210"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047312965"],"corresponding_institution_ids":["https://openalex.org/I60054993"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26899628,"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/T10157","display_name":"Sports Performance and Training","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10157","display_name":"Sports Performance and Training","score":0.9843999743461609,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9803000092506409,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12677","display_name":"Sports Dynamics and Biomechanics","score":0.965399980545044,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6087271571159363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6087271571159363}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt61001.2024.10725414","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icccnt61001.2024.10725414","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":18,"referenced_works":["https://openalex.org/W3117221752","https://openalex.org/W3212433341","https://openalex.org/W3214473442","https://openalex.org/W4377700161","https://openalex.org/W4378805884","https://openalex.org/W4383745162","https://openalex.org/W4383745473","https://openalex.org/W4383747221","https://openalex.org/W4385625287","https://openalex.org/W4385625564","https://openalex.org/W4385871980","https://openalex.org/W4386427013","https://openalex.org/W4386919701","https://openalex.org/W4386919702","https://openalex.org/W4386920845","https://openalex.org/W4386920900","https://openalex.org/W4387642931","https://openalex.org/W4399557791"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"This":[0,60],"study":[1],"looks":[2],"at":[3,165,180],"the":[4,27,30,43,94,100,106,115,118,134,141,145,148,178,187,201,204,236,244,254,263],"applicability":[5],"of":[6,29,45,50,56,72,82,112,117,123,127,177,200,203,224,230,243,253,271],"a":[7,67,79,121,125,219,227],"special":[8],"network":[9],"structure":[10],"referred":[11],"to":[12],"as":[13,42,216],"Convolutional":[14],"Neural":[15],"Network-Long":[16],"Short-Term":[17],"Memory":[18],"(CNN-LSTM)":[19],"in":[20,24,26,38,93,160],"identifying":[21],"minor":[22],"changes":[23,85],"motion":[25,84,255],"handling":[28],"tennis":[31,76],"racket":[32],"during":[33],"serves.":[34],"The":[35,96,175,198,241],"desired":[36],"performance":[37,179],"rare":[39],"movements,":[40],"such":[41],"incorporation":[44],"spatial":[46],"feature":[47],"extraction":[48],"capabilities":[49],"CNNs":[51],"and":[52,64,74,88,147,155,189,235,262],"temporal":[53],"sequence":[54],"learning":[55],"LSTMs,":[57],"attains":[58],"serve.":[59],"model":[61,108],"was":[62],"trained":[63],"tested":[65],"using":[66],"video-based":[68],"dataset":[69],"containing":[70],"videos":[71],"professional/semi-professional":[73],"amateur":[75],"players.":[77],"Next,":[78],"considerable":[80],"number":[81],"small":[83],"were":[86],"extracted":[87],"labelled":[89],"for":[90,251,273],"many":[91,172],"samples":[92],"dataset.":[95],"observation":[97],"made":[98],"from":[99],"current":[101],"research":[102],"analysis":[103,176,266],"shows":[104,183,247],"that":[105,133,138,208],"CNN-LSTM":[107],"achieves":[109],"an":[110,209,268],"accuracy":[111,194,222],"94%.5%":[113],"on":[114,238],"detection":[116],"disease":[119],"with":[120,218,226,258],"precision":[122,153],"92.3%,":[124],"recall":[126,146],"93":[128],"-":[129],"it":[130,150,163],"is":[131,137,168],"noticed":[132],"above":[135],"result":[136,149],"which":[139,167],"consolidates":[140],"fundamental":[142],"constancy":[143],"between":[144],"portrays.":[151],"8%,":[152],"93%,":[154],"F1-score":[156],"93%.":[157],"But":[158],"even":[159,169],"this":[161],"regard,":[162],"stands":[164],"0%,":[166],"higher":[170],"than":[171],"state-of-the-art":[173],"models.":[174],"each":[181],"epoch":[182],"gradual":[184],"improvements;":[185],"consequently,":[186],"training":[188],"validation":[190],"sets":[191],"attain":[192],"high":[193,248],"after":[195],"50":[196],"epochs.":[197],"optimization":[199],"form":[202],"batch":[205,211],"size":[206,212],"implies":[207],"ideal":[210],"has":[213],"been":[214],"estimated":[215],"32,":[217],"corresponding":[220],"classification":[221,249],"estimate":[223],"94":[225],"standard":[228],"error":[229],"0.":[231,239],"Per":[232],"cent:":[233],"5%":[234],"decrease":[237],"25.":[240],"heatmap":[242],"confusion":[245],"matrix":[246],"rates":[250],"several":[252],"variation":[256],"classes":[257],"very":[259],"little":[260],"confusion,":[261],"ROC":[264],"curve":[265],"yields":[267],"average":[269],"AUC":[270],"0.93":[272],"all":[274],"classes.":[275]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}