{"id":"https://openalex.org/W4405270704","doi":"https://doi.org/10.1109/cvmi61877.2024.10782102","title":"Advancing Table Tennis Analytics: A CNN-LSTM Framework for Precision Serve Shot Analysis","display_name":"Advancing Table Tennis Analytics: A CNN-LSTM Framework for Precision Serve Shot Analysis","publication_year":2024,"publication_date":"2024-10-19","ids":{"openalex":"https://openalex.org/W4405270704","doi":"https://doi.org/10.1109/cvmi61877.2024.10782102"},"language":"en","primary_location":{"id":"doi:10.1109/cvmi61877.2024.10782102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi61877.2024.10782102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","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/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":true,"raw_author_name":"Shiva Mehta","raw_affiliation_strings":["Chitkara University Institute of Engineering and Technology Chitkara University,Centre for Research Impact &#x0026; Outcome,Rajpura,Punjab,India,140401"],"affiliations":[{"raw_affiliation_string":"Chitkara University Institute of Engineering and Technology Chitkara University,Centre for Research Impact &#x0026; Outcome,Rajpura,Punjab,India,140401","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028633502","display_name":"Aseem Aneja","orcid":null},"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":"Aseem Aneja","raw_affiliation_strings":["Chitkara University,Chitkara Centre for Research and Development,Himachal Pradesh,India,174103"],"affiliations":[{"raw_affiliation_string":"Chitkara University,Chitkara Centre for Research and Development,Himachal Pradesh,India,174103","institution_ids":["https://openalex.org/I74319210"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061558029"],"corresponding_institution_ids":["https://openalex.org/I74319210"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31092604,"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":0.995199978351593,"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":0.995199978351593,"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/T12677","display_name":"Sports Dynamics and Biomechanics","score":0.9854999780654907,"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/T11246","display_name":"Sports injuries and prevention","score":0.9736999869346619,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.7472628951072693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7461791038513184},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.7179169654846191},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.6564373970031738},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.5291462540626526},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5284214615821838},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4449910521507263},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.35017743706703186},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2827455401420593},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10976415872573853}],"concepts":[{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.7472628951072693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7461791038513184},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.7179169654846191},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6564373970031738},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.5291462540626526},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5284214615821838},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4449910521507263},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35017743706703186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2827455401420593},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10976415872573853},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvmi61877.2024.10782102","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi61877.2024.10782102","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2782116914","https://openalex.org/W2990118374","https://openalex.org/W3108725470","https://openalex.org/W3117221752","https://openalex.org/W3204161309","https://openalex.org/W3212433341","https://openalex.org/W4207001442","https://openalex.org/W4221013561","https://openalex.org/W4225316118","https://openalex.org/W4281750595","https://openalex.org/W4285815705","https://openalex.org/W4285987815","https://openalex.org/W4292296511","https://openalex.org/W4320039102","https://openalex.org/W4385871980","https://openalex.org/W4386919419","https://openalex.org/W4387489303","https://openalex.org/W4388950956"],"related_works":["https://openalex.org/W2497720472","https://openalex.org/W4292659306","https://openalex.org/W3044321615","https://openalex.org/W2806221744","https://openalex.org/W2326937258","https://openalex.org/W394267150","https://openalex.org/W2773965352","https://openalex.org/W4294892107","https://openalex.org/W2357748469","https://openalex.org/W2392917037"],"abstract_inverted_index":{"As":[0],"for":[1,12,244],"the":[2,16,28,38,48,60,94,101,114,117,120,141,151,159,171,183,199,202,225,242],"current":[3],"work,":[4],"this":[5,211,232],"research":[6],"proposes":[7],"a":[8,80],"CNN-LSTM":[9,203],"model":[10,103,149,204,233],"systematized":[11],"evaluating":[13],"and":[14,32,72,136,161,166,187,194,214,221,240],"predicting":[15],"serve":[17,77,238],"strokes":[18],"in":[19,251],"real-time":[20],"table":[21,124],"tennis.":[22],"It":[23,85,97,174],"is":[24,68,98],"supposed":[25],"to":[26,70,106,176,205,224,247],"address":[27],"deficiency":[29],"of":[30,42,53,62,76,83,93,116,123,140,154,163,201,237],"extensive":[31],"timely":[33],"sports":[34,207],"analysis":[35],"by":[36],"combining":[37],"spatial":[39],"FE":[40],"capability":[41],"Convolutional":[43],"Neural":[44],"Networks":[45],"(CNN)":[46],"with":[47,79,113,158],"temporal":[49],"dynamics":[50],"learning":[51],"function":[52],"Long":[54],"Short-Term":[55],"Memory":[56],"(LSTM)":[57],"networks.":[58],"In":[59],"case":[61],"our":[63,235],"signal,":[64],"high-frame-rate":[65],"video":[66,121,138],"data":[67],"used":[69,105],"study":[71,197],"identify":[73],"all":[74],"manner":[75],"shots":[78],"reasonable":[81],"degree":[82],"precision.":[84],"also":[86],"makes":[87],"predictions":[88],"that":[89,100],"give":[90],"essential":[91],"indications":[92],"strategic":[95],"games.":[96],"recommended":[99],"suggested":[102],"be":[104,177],"evaluate":[107],"its":[108,245],"performance,":[109,222],"which":[110,191],"was":[111,139],"conducted":[112],"help":[115],"dataset":[118],"containing":[119],"recordings":[122,128],"tennis":[125],"serves.":[126,173],"These":[127],"were":[129],"captured":[130],"at":[131],"120":[132],"frames":[133],"per":[134],"second,":[135],"each":[137],"dimension":[142],"$1920":[143],"\\times":[144],"1080$":[145],"pixels.":[146],"The":[147],"presented":[148],"showcased":[150],"top":[152],"performance":[153],"$\\mathbf{9":[155],"2}":[156],"\\%$":[157],"precision":[160],"recall":[162],"90":[164],"$\\%$":[165],"$\\mathbf{8":[167],"5":[168],"\\%}$":[169,180],"on":[170],"topspin":[172],"proved":[175],"$\\mathbf{2":[178],"0":[179],"better":[181],"than":[182],"standard":[184],"analytical":[185],"methods":[186],"other":[188,248],"independent":[189],"schemes,":[190],"include":[192],"CNNs":[193],"SVMs.":[195],"Our":[196],"underscores":[198],"potential":[200],"revolutionize":[206],"analysis.":[208],"By":[209],"integrating":[210],"model,":[212],"coaches,":[213],"athletes":[215],"can":[216],"significantly":[217],"enhance":[218],"their":[219],"strategies":[220],"thanks":[223],"comprehensive":[226],"vital":[227],"information":[228],"it":[229],"provides.":[230],"Moreover,":[231],"improves":[234],"understanding":[236],"mechanics":[239],"paves":[241],"way":[243],"application":[246],"complex":[249],"motions":[250],"sports.":[252]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
