Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial M...

Full description

Saved in:
Bibliographic Details
Published inFrontiers in sports and active living Vol. 6; p. 1326807
Main Authors Salim, Fahim A., Postma, Dees B. W., Haider, Fasih, Luz, Saturnino, Beijnum, Bert-Jan F. van, Reidsma, Dennis
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 16.04.2024
Subjects
action recognition
digital sports technologies
smart sports
Sports and Active Living
sports telematics
telematics applications
training methods
smart sports
digital sports technologies
training methods
telematics applications
sports telematics
action recognition
Online AccessGet full text

Cover

Abstract Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the “bump-set-spike” trainer , which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.
AbstractList Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the "bump-set-spike" trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the "bump-set-spike" trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.
Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the , which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.
Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the “bump-set-spike” trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.
Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the “bump-set-spike” trainer , which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.
Author Haider, Fasih
Postma, Dees B. W.
Reidsma, Dennis
Beijnum, Bert-Jan F. van
Salim, Fahim A.
Luz, Saturnino
AuthorAffiliation 2 Human Media Interaction, University of Twente , Enschede , Netherlands
4 Usher Institute, The University of Edinburgh , Edinburgh , United Kingdom
5 Biomedical Signals and Systems, University of Twente , Enschede , Netherlands
1 Digitalization Group, Irish Manufacturing Research , Mullingar , Ireland
3 School of Engineering, The University of Edinburgh , Edinburgh , United Kingdom
AuthorAffiliation_xml – name: 2 Human Media Interaction, University of Twente , Enschede , Netherlands
– name: 5 Biomedical Signals and Systems, University of Twente , Enschede , Netherlands
– name: 3 School of Engineering, The University of Edinburgh , Edinburgh , United Kingdom
– name: 1 Digitalization Group, Irish Manufacturing Research , Mullingar , Ireland
– name: 4 Usher Institute, The University of Edinburgh , Edinburgh , United Kingdom
Author_xml – sequence: 1
  givenname: Fahim A.
  surname: Salim
  fullname: Salim, Fahim A.
– sequence: 2
  givenname: Dees B. W.
  surname: Postma
  fullname: Postma, Dees B. W.
– sequence: 3
  givenname: Fasih
  surname: Haider
  fullname: Haider, Fasih
– sequence: 4
  givenname: Saturnino
  surname: Luz
  fullname: Luz, Saturnino
– sequence: 5
  givenname: Bert-Jan F. van
  surname: Beijnum
  fullname: Beijnum, Bert-Jan F. van
– sequence: 6
  givenname: Dennis
  surname: Reidsma
  fullname: Reidsma, Dennis
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38689871$$D View this record in MEDLINE/PubMed
BookMark eNp9kk1vFDEMhkeoiJbSP8ABzZHLLvmaJMMFoapApUpc4Bx5Es9OqmyyJLNF--_JflC1HDjFsl8_tpz3dXMWU8SmeUvJknPdfxjLJuUlI0wsKWdSE_WiuWCSiUXPpTp7Ep83V6XcE0JYRzpKu1fNOddS91rRi8bdxAmi9XHVPqQQcDdACO2cwcea-9jiepN-Y97XYZ4CzlhaiK61CexU43nKabuaWnAPFYOuLRjLQV1FECHsii9vmpcjhIJXp_ey-fnl5sf1t8Xd96-315_vFlbIfl5wtAIkl8ARGQMLwg4gRiHq5pR3nBGlHKGglZMKoOOjGKCTzg7cdXRU_LK5PXJdgnuzyX4NeWcSeHNIpLwykGdvAxqJcpCDVr3tpaAjDgKZk2CHTrnBcldZn46szXZYo7MY61HCM-jzSvSTWaUHQynptOR9Jbw_EXL6tcUym7UvFkOAiGlbDCeiV1Tznlbpu6fDHqf8_acqYEeBzamUjOOjhBKz94M5-MHs_WBOfqhN-p8m62eYfdov7MP_Wv8AwTS_Jw
CitedBy_id crossref_primary_10_1109_JSEN_2024_3492004
Cites_doi 10.1007/s10618-017-0495-0
10.1111/cgf.13447
10.1561/1100000041
10.1109/MSP.2006.1621448
10.1080/02640414.2018.1521769
10.2352/ISSN.2470-1173.2016.1.VDA-510
10.1016/j.patcog.2016.08.003
10.4236/ce.2015.67070
10.1145/3191770
10.1109/ACCESS.2018.2839766
10.1016/j.cviu.2017.04.011
10.3389/fcomp.2020.00001
10.1123/jmpb.2020-0012
10.1080/19346182.2008.9648487
10.1080/07303084.2017.1390504
10.1080/19346182.2008.9648479
10.1109/TVCG.2014.2346445
10.1080/19346182.2009.9648507
10.1007/s11042-015-3188-y
10.3758/s13423-012-0333-8
10.1145/2724584
10.1109/ACCESS.2018.2792220
10.1080/1750984X.2012.665076
10.1109/TSMC.2015.2391258
10.3390/s100302359
10.1080/19346182.2012.708974
10.1080/19346182.2009.9648510
10.1109/JSEN.2010.2048205
10.1519/JSC.0000000000003732
10.1016/j.robot.2012.09.015
10.1111/cgf.13131
10.1007/978-3-319-14112-1
10.1080/17461391.2018.1463397
10.5370/JEET.2016.11.6.1857
10.1371/journal.pone.0203348
10.1145/2724582
10.1016/j.cviu.2017.01.002
10.26773/mjssm.2017.09.005
10.3390/mti4020033
10.1080/02701367.1998.10607712
10.3390/s140917235
10.3389/fphys.2018.00115
10.3390/s20154232
10.1016/j.neucom.2012.04.038
10.3390/data2010002
10.1123/jsep.34.3.305
10.1049/iet-cvi.2015.0189
10.3390/s22134786
10.1109/JSTSP.2019.2955022
10.1088/1742-6596/1529/2/022015
10.3390/s130405317
10.1080/24748668.2016.11868917
10.13140/RG.2.2.23576.49929
10.1080/14763141.2017.1314544
10.1007/978-3-642-56927-2
10.1016/j.jbiomech.2017.04.028
10.1109/JIOT.2014.2377238
10.1080/19346182.2014.905949
10.1145/2731182
10.3758/BRM.41.3.841
ContentType Journal Article
Copyright 2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma.
2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma. 2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma
Copyright_xml – notice: 2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma.
– notice: 2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma. 2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma
DBID AAYXX
CITATION
NPM
7X8
5PM
DOA
DOI 10.3389/fspor.2024.1326807
DatabaseName CrossRef
PubMed
MEDLINE - Academic
PubMed Central (Full Participant titles)
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
PubMed
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic
PubMed


CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Recreation & Sports
EISSN 2624-9367
ExternalDocumentID oai_doaj_org_article_6e6b6b879c9641feb4e2d6acb57dbc3d
PMC11058639
38689871
10_3389_fspor_2024_1326807
Genre Journal Article
GrantInformation_xml – fundername: Dutch ZonMw organisation
  grantid: 546001004
GroupedDBID 53G
9T4
AAFWJ
AAYXX
ACXDI
AFPKN
ALMA_UNASSIGNED_HOLDINGS
CITATION
GROUPED_DOAJ
M~E
OK1
PGMZT
RPM
NPM
7X8
5PM
ID FETCH-LOGICAL-c469t-3ec4a636a3ee22aca4cba4f4402513532077d01a87d67aa53f4ba56dcb3d51f73
IEDL.DBID DOA
ISSN 2624-9367
IngestDate Wed Aug 27 01:32:05 EDT 2025
Thu Aug 21 18:34:36 EDT 2025
Fri Jul 11 12:22:28 EDT 2025
Thu Apr 03 06:56:05 EDT 2025
Tue Jul 01 02:50:38 EDT 2025
Thu Apr 24 23:03:36 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Keywords smart sports
digital sports technologies
training methods
telematics applications
sports telematics
action recognition
Language English
License 2024 Salim, Postma, Haider, Luz, Beijnum and Reidsma.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c469t-3ec4a636a3ee22aca4cba4f4402513532077d01a87d67aa53f4ba56dcb3d51f73
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Edited by: Ekaterina Glebova, Université Paris-Saclay CIAMS, France
Ahmad Jalal, Air University, Pakistan
Reviewed by: Wojciech Lewicki, West Pomeranian University of Technology, Poland
Ciara Feely, University College Dublin, Ireland
OpenAccessLink https://doaj.org/article/6e6b6b879c9641feb4e2d6acb57dbc3d
PMID 38689871
PQID 3049718391
PQPubID 23479
ParticipantIDs doaj_primary_oai_doaj_org_article_6e6b6b879c9641feb4e2d6acb57dbc3d
pubmedcentral_primary_oai_pubmedcentral_nih_gov_11058639
proquest_miscellaneous_3049718391
pubmed_primary_38689871
crossref_primary_10_3389_fspor_2024_1326807
crossref_citationtrail_10_3389_fspor_2024_1326807
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2024-04-16
PublicationDateYYYYMMDD 2024-04-16
PublicationDate_xml – month: 04
  year: 2024
  text: 2024-04-16
  day: 16
PublicationDecade 2020
PublicationPlace Switzerland
PublicationPlace_xml – name: Switzerland
PublicationTitle Frontiers in sports and active living
PublicationTitleAlternate Front Sports Act Living
PublicationYear 2024
Publisher Frontiers Media S.A
Publisher_xml – name: Frontiers Media S.A
References Jensen (B6)
Kesicki (B85)
Ghasemzadeh (B75)
Koekoek (B93) 2018; 89
Boyd (B103) 2010
Shao (B14) 2016; 28
Marquardt (B119)
Puwein (B21)
Trajkova (B120) 2018; 2
van Delden (B131)
Postma (B33)
Thomas (B4) 2017; 159
Van Delden (B30)
Pei (B47)
Jensen (B126)
Lu (B83) 2017; 76
team (B121)
Newell (B124)
Moreno Celleri (B112)
Kautz (B132) 2017; 31
Kamal (B54) 2016; 6
Newell (B125) 2007
Fuss (B43) 2008; 1
Lamas (B90)
Chi (B79)
Hsu (B1) 2018; 6
Stein (B91) 2017; 2
Dadashi (B65)
Gireesh (B24)
Postma (B32)
Hämäläinen (B118)
Jensen (B95) 2015; 22
Perin (B12) 2018; 37
Holsti (B100)
Taylor (B29) 2021; 68
Altimira (B5)
López-Matencio (B19)
Haider (B134)
Jalal (B23)
Cust (B45) 2019; 37
Schuldhaus (B64)
Haider (B136) 2020; 2
Kos (B68)
O’Reilly (B70) 2017; 58
Velasco (B130) 2016
Hileno (B37) 2012; 12
Vales-Alonso (B10) 2015; 45
Chattopadhyay (B55) 2015; 10
Kosmalla (B97)
Beenhakker (B38)
Polk (B13) 2014; 20
Loureiro (B86) 2017; 6
Ahmadi (B69) 2009; 2
Ma (B77)
Van der Kruk (B42) 2018; 18
Ryoo (B26)
von Marcard (B51) 2017; 36
Fuss (B44) 2009; 2
Siegel (B101)
Vales-Alonso (B20) 2010; 10
Feltz (B107) 2020; 34
Drikos (B87)
Haider (B35) 2020; 4
Sigrist (B114) 2013; 20
Kosmalla (B98)
Edwards (B113) 2010
Cuspinera (B61)
Renò (B18) 2017; 159
Wang (B62) 2018; 6
Kohonen (B137) 2001
Kosmalla (B108)
Kajastila (B127)
Ste-Marie (B115) 2012; 5
Harvey (B92) 2014; 16
Blank (B67)
Haider (B34)
Jensen (B102) 2014; 13
van Delden (B7) 2014; 1
Ghasemzadeh (B76) 2011; 11
Kajastila (B9) 2015; 22
Brunauer (B15)
Farooq (B56) 2018
Zivkovic (B80)
Nguyen Ngu Nguyen (B78)
Sadi (B58) 2011; 4
Hunicke (B110)
Salim (B36)
Weiss (B116) 1998; 69
Bagautdinov (B8)
Ruffaldi (B105) 2013; 61
Bellusci (B46) 2018; 1
Stach (B111)
Venek (B40) 2022; 22
Hurst (B88) 2016; 16
B122
Anderson (B117)
Ismail (B50)
Groh (B72)
O’Reilly (B71) 2017; 16
Luz (B16)
Mueller (B31) 2016; 10
Slimani (B28)
Berlin (B53)
Jalal (B27)
Laporta (B89) 2018; 13
Wang (B48) 2018; 6
Koekoek (B123) 2015; 6
Lausberg (B133) 2009; 41
Salim (B3) 2020; 3
Kokaram (B11) 2006; 23
Salim (B2)
Jalal (B25) 2016; 61
Shahar (B74) 2020; 1529
Kautz (B73)
Gandy (B63) 2014; 7
Haider (B135) 2019; 14
Cesarini (B104)
Bucanek (B129) 2009
Kajastila (B96)
Fuss (B41) 2008; 1
Shapiee (B81)
Luteberget (B84) 2018; 9
Woźniak (B109) 2015; 22
Jalal (B57) 2018
Ludvigsen (B99)
Stienstra (B106)
Zhao (B22) 2013; 105
Mitchell (B82) 2013; 13
Andres (B128)
Neuwirth (B59) 2020; 20
Cancela (B49) 2014; 14
Correia (B94) 2012; 34
Wirth (B39)
Dhamsania (B52)
Matejka (B17)
Technologies (B138)
Ahmadi (B66) 2014; 2
Harding (B60)
References_xml – ident: B79
– ident: B106
– volume: 31
  start-page: 1678
  year: 2017
  ident: B132
  article-title: Activity recognition in beach volleyball using a deep convolutional neural network: leveraging the potential of deep learning in sports
  publication-title: Data Min Knowl Discov
  doi: 10.1007/s10618-017-0495-0
– volume: 37
  start-page: 663
  year: 2018
  ident: B12
  article-title: State of the art of sports data visualization
  publication-title: Comput Graph Forum
  doi: 10.1111/cgf.13447
– ident: B121
– volume: 10
  start-page: 1
  year: 2016
  ident: B31
  article-title: Exertion games
  publication-title: Found Trends® Hum Comput Interact
  doi: 10.1561/1100000041
– ident: B101
– ident: B126
– volume: 23
  start-page: 47
  year: 2006
  ident: B11
  article-title: Browsing sports video: trends in sports-related indexing and retrieval work
  publication-title: IEEE Signal Process Mag
  doi: 10.1109/MSP.2006.1621448
– ident: B21
– volume: 37
  start-page: 568
  year: 2019
  ident: B45
  article-title: Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance
  publication-title: J Sports Sci
  doi: 10.1080/02640414.2018.1521769
– ident: B111
– ident: B30
– ident: B2
– volume: 28
  start-page: 1
  year: 2016
  ident: B14
  article-title: Visual-interactive search for soccer trajectories to identify interesting game situations
  publication-title: Electron Imaging
  doi: 10.2352/ISSN.2470-1173.2016.1.VDA-510
– volume: 61
  start-page: 295
  year: 2016
  ident: B25
  article-title: Robust human activity recognition from depth video using spatiotemporal multi-fused features
  publication-title: Pattern Recognit
  doi: 10.1016/j.patcog.2016.08.003
– ident: B61
– ident: B24
– volume: 16
  start-page: 271
  year: 2014
  ident: B92
  article-title: Effects of integrating video-based feedback into a teaching games for understanding soccer unit
  publication-title: Agora para la educación física y el deporte
  doi: 10.4236/ce.2015.67070
– volume: 2
  start-page: 1
  year: 2018
  ident: B120
  article-title: Takes tutu to ballet: designing visual and verbal feedback for augmented mirrors
  publication-title: Proc ACM Interact Mob Wear Ubiquitous Technol
  doi: 10.1145/3191770
– volume: 6
  start-page: 31715
  year: 2018
  ident: B1
  article-title: Human daily and sport activity recognition using a wearable inertial sensor network
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2839766
– volume: 159
  start-page: 3
  year: 2017
  ident: B4
  article-title: Computer vision for sports: current applications and research topics
  publication-title: Comput Vis Image Underst
  doi: 10.1016/j.cviu.2017.04.011
– ident: B39
– volume: 2
  start-page: 1
  year: 2020
  ident: B136
  article-title: An active data representation of videos for automatic scoring of oral presentation delivery skills and feedback generation
  publication-title: Front Comput Sci
  doi: 10.3389/fcomp.2020.00001
– volume-title: Human Activity Recognition Using the Labeled Depth Body Parts Information of Depth Silhouettes
  year: 2018
  ident: B57
– ident: B87
– ident: B90
– ident: B64
– volume: 3
  start-page: 323
  year: 2020
  ident: B3
  article-title: Towards automatic modeling of volleyball players’ behavior for analysis, feedback, and hybrid training
  publication-title: J Meas Phys Behav
  doi: 10.1123/jmpb.2020-0012
– volume: 1
  start-page: 301
  year: 2008
  ident: B43
  article-title: Instrumented climbing holds and performance analysis in sport climbing
  publication-title: Sports Technol
  doi: 10.1080/19346182.2008.9648487
– ident: B68
– ident: B131
– volume: 89
  start-page: 12
  year: 2018
  ident: B93
  article-title: Aligning digital video technology with game pedagogy in physical education
  publication-title: J Phys Educ Recreation Dance
  doi: 10.1080/07303084.2017.1390504
– volume: 1
  start-page: 235
  year: 2008
  ident: B41
  article-title: Editorial, special issue on instrumentation of athletes and equipment during competitions
  publication-title: Sports Technol
  doi: 10.1080/19346182.2008.9648479
– ident: B124
– volume: 20
  start-page: 2339
  year: 2014
  ident: B13
  article-title: Tennivis: visualization for tennis match analysis
  publication-title: IEEE Trans Vis Comput Graph
  doi: 10.1109/TVCG.2014.2346445
– ident: B8
– ident: B98
– volume-title: Task Constraints and Movement Organization: A Common Language
  year: 2007
  ident: B125
– ident: B81
– ident: B65
– volume: 2
  start-page: 97
  year: 2009
  ident: B44
  article-title: Design of an instrumented bowling ball and its application to performance analysis in tenpin bowling
  publication-title: Sports Technol
  doi: 10.1080/19346182.2009.9648507
– volume: 76
  start-page: 10701
  year: 2017
  ident: B83
  article-title: Towards unsupervised physical activity recognition using smartphone accelerometers
  publication-title: Multimed Tools Appl
  doi: 10.1007/s11042-015-3188-y
– volume: 20
  start-page: 21
  year: 2013
  ident: B114
  article-title: Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review
  publication-title: Psychon Bull Rev
  doi: 10.3758/s13423-012-0333-8
– ident: B108
– ident: B47
– ident: B73
– ident: B96
– ident: B112
– volume: 22
  start-page: 40
  year: 2015
  ident: B109
  article-title: Untangling running: designing for real-life runner experiences
  publication-title: Interactions
  doi: 10.1145/2724584
– ident: B85
– volume: 6
  start-page: 13758
  year: 2018
  ident: B62
  article-title: Volleyball skill assessment using a single wearable micro inertial measurement unit at wrist
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2792220
– ident: B16
– volume: 5
  start-page: 145
  year: 2012
  ident: B115
  article-title: Observation interventions for motor skill learning and performance: an applied model for the use of observation
  publication-title: Int Rev Sport Exerc Psychol
  doi: 10.1080/1750984X.2012.665076
– volume: 45
  start-page: 1138
  year: 2015
  ident: B10
  article-title: SAETA: a smart coaching assistant for professional volleyball training
  publication-title: IEEE Trans Syst Man Cybern Syst
  doi: 10.1109/TSMC.2015.2391258
– volume: 10
  start-page: 2359
  year: 2010
  ident: B20
  article-title: Ambient intelligence systems for personalized sport training
  publication-title: Sensors
  doi: 10.3390/s100302359
– volume: 4
  start-page: 88
  year: 2011
  ident: B58
  article-title: Reliable jump detection for snow sports with low-cost MEMS inertial sensors
  publication-title: Sports Technol
  doi: 10.1080/19346182.2012.708974
– ident: B118
– volume: 2
  start-page: 129
  year: 2009
  ident: B69
  article-title: Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve
  publication-title: Sports Technol
  doi: 10.1080/19346182.2009.9648510
– ident: B80
– ident: B72
– volume: 11
  start-page: 603
  year: 2011
  ident: B76
  article-title: Coordination analysis of human movements with body sensor networks: a signal processing model to evaluate baseball swings
  publication-title: Sens J
  doi: 10.1109/JSEN.2010.2048205
– ident: B33
– ident: B53
– ident: B60
– ident: B77
– volume: 34
  start-page: 2434
  year: 2020
  ident: B107
  article-title: Can simulated partners boost workout effort in long-term exercise?
  publication-title: J Strength Cond Res
  doi: 10.1519/JSC.0000000000003732
– ident: B127
– ident: B78
– volume: 61
  start-page: 390
  year: 2013
  ident: B105
  article-title: Structuring a virtual environment for sport training: A case study on rowing technique
  publication-title: Rob Auton Syst
  doi: 10.1016/j.robot.2012.09.015
– volume: 36
  start-page: 349
  year: 2017
  ident: B51
  article-title: Sparse inertial poser: automatic 3D human pose estimation from sparse IMUs
  publication-title: Comput Graph Forum
  doi: 10.1111/cgf.13131
– ident: B104
– volume: 1
  start-page: 145
  year: 2014
  ident: B7
  article-title: Steering gameplay behavior in the interactive tag playground
  publication-title: Ambient Intell
  doi: 10.1007/978-3-319-14112-1
– volume: 6
  start-page: 13758
  year: 2018
  ident: B48
  article-title: Volleyball skill assessment using a single wearable micro inertial measurement unit at wrist
  publication-title: IEEE Access
  doi: 10.1109/ACCESS.2018.2792220
– ident: B110
– volume: 12
  start-page: 557
  year: 2012
  ident: B37
  article-title: Observational tool for analyzing attack coverage in volleyball
  publication-title: Revista Internacional de Medicina y Ciencias de la Actividad Fisica y del Deporte
– ident: B119
– ident: B75
– ident: B128
– volume: 18
  start-page: 1
  year: 2018
  ident: B42
  article-title: Accuracy of human motion capture systems for sport applications; state-of-the-art review
  publication-title: Eur J Sport Sci
  doi: 10.1080/17461391.2018.1463397
– volume: 6
  start-page: 1857
  year: 2016
  ident: B54
  article-title: Depth images-based human detection, tracking and activity recognition using spatiotemporal features and modified HMM
  publication-title: J Electr Eng Technol
  doi: 10.5370/JEET.2016.11.6.1857
– ident: B67
– ident: B5
– ident: B50
– volume-title: Apache Solr: For Starters
  year: 2016
  ident: B130
– ident: B6
– volume: 13
  start-page: 1
  year: 2018
  ident: B89
  article-title: Interaction network analysis of the six game complexes in high-level volleyball through the use of eigenvector centrality
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0203348
– ident: B34
– volume: 22
  start-page: 36
  year: 2015
  ident: B95
  article-title: Designing training games for soccer
  publication-title: Interactions
  doi: 10.1145/2724582
– ident: B26
– volume: 159
  start-page: 164
  year: 2017
  ident: B18
  article-title: A technology platform for automatic high-level tennis game analysis
  publication-title: Comput Vis Image Underst
  doi: 10.1016/j.cviu.2017.01.002
– volume: 6
  start-page: 35
  year: 2017
  ident: B86
  article-title: A comprehensive mapping of high-level men’s volleyball gameplay through social network analysis: analysing serve, side-out, side-out transition and transition
  publication-title: Montenegrin J Sports Sci Med
  doi: 10.26773/mjssm.2017.09.005
– ident: B122
– ident: B32
– ident: B17
– volume: 4
  start-page: 1
  year: 2020
  ident: B35
  article-title: A super-bagging method for volleyball action recognition using wearable sensors
  publication-title: Multimodal Technol Interact
  doi: 10.3390/mti4020033
– volume: 69
  start-page: 380
  year: 1998
  ident: B116
  article-title: Observational learning and the fearful child: Influence of peer models on swimming skill performance and psychological responses
  publication-title: Res Q Exerc Sport
  doi: 10.1080/02701367.1998.10607712
– volume: 6
  start-page: 16
  year: 2015
  ident: B123
  article-title: Spelanalyse met video-catch voor de ipad. een combinatie van digitale techniek, vakinhoud en didactiek in het spelonderwijs
  publication-title: Lichamelijke Opvoeding
– volume: 14
  start-page: 17235
  year: 2014
  ident: B49
  article-title: Wearability assessment of a wearable system for Parkinson’s disease remote monitoring based on a body area network of sensors
  publication-title: Sensors (Switzerland)
  doi: 10.3390/s140917235
– volume: 9
  start-page: 1
  year: 2018
  ident: B84
  article-title: Validity of the Catapult ClearSky T6 local positioning system for team sports specific drills, in indoor conditions
  publication-title: Front Physiol
  doi: 10.3389/fphys.2018.00115
– ident: B23
– volume: 20
  start-page: 4232
  year: 2020
  ident: B59
  article-title: Classification of alpine skiing styles using GNSS and inertial measurement units
  publication-title: Sensors
  doi: 10.3390/s20154232
– volume: 105
  start-page: 45
  year: 2013
  ident: B22
  article-title: Human action recognition based on semi-supervised discriminant analysis with global constraint
  publication-title: Neurocomputing
  doi: 10.1016/j.neucom.2012.04.038
– ident: B100
– ident: B99
– ident: B134
– ident: B52
– volume: 13
  start-page: 57
  year: 2014
  ident: B102
  article-title: Interactive football-training based on rebounders with hit position sensing and audio-visual feedback
  publication-title: Int J Comput Sci Sport
– ident: B36
– volume-title: Corrective Sonic Feedback for Speed Skating: A Case Study
  year: 2010
  ident: B103
– volume: 2
  start-page: 2
  year: 2017
  ident: B91
  article-title: How to make sense of team sport data: From acquisition to data modeling and research aspects
  publication-title: Data
  doi: 10.3390/data2010002
– volume: 34
  start-page: 305
  year: 2012
  ident: B94
  article-title: Perceiving and acting upon spaces in a VR rugby task: expertise effects in affordance detection and task achievement
  publication-title: J Sport Exerc Psychol
  doi: 10.1123/jsep.34.3.305
– volume: 10
  start-page: 220
  year: 2015
  ident: B55
  article-title: Supervised framework for automatic recognition and retrieval of interaction: a framework for classification and retrieving videos with similar human interactions
  publication-title: IET Comput Vis
  doi: 10.1049/iet-cvi.2015.0189
– volume: 22
  start-page: 1
  year: 2022
  ident: B40
  article-title: Human movement quality assessment using sensor technologies in recreational and professional sports: a scoping review
  publication-title: Sensors
  doi: 10.3390/s22134786
– volume: 68
  start-page: 8
  year: 2021
  ident: B29
  article-title: Paced to perfection: exploring the potential impact of wavelight technology in athletics
  publication-title: Sport Exerc Sci
– volume: 14
  start-page: 272
  year: 2019
  ident: B135
  article-title: An assessment of paralinguistic acoustic features for detection of Alzheimer’s dementia in spontaneous speech
  publication-title: IEEE J Sel Top Signal Process
  doi: 10.1109/JSTSP.2019.2955022
– volume: 1529
  start-page: 022015
  year: 2020
  ident: B74
  article-title: Wearable inertial sensor for human activity recognition in field hockey: influence of sensor combination and sensor location
  publication-title: J Phys Conf Ser
  doi: 10.1088/1742-6596/1529/2/022015
– ident: B138
– volume: 13
  start-page: 5317
  year: 2013
  ident: B82
  article-title: Classification of sporting activities using smartphone accelerometers
  publication-title: Sensors (Switzerland)
  doi: 10.3390/s130405317
– volume: 16
  start-page: 695
  year: 2016
  ident: B88
  article-title: Systemic mapping of high-level women’s volleyball using social network analysis: The case of serve (K0), side-out (KI), side-out transition (KII) and transition (KIII)
  publication-title: Int J Perform Anal Sport
  doi: 10.1080/24748668.2016.11868917
– ident: B38
– volume: 1
  start-page: 1
  year: 2018
  ident: B46
  article-title: Xsens MTw: miniature wireless inertial motion tracker for highly accurate 3D kinematic applications
  publication-title: Xsens Technol
  doi: 10.13140/RG.2.2.23576.49929
– start-page: 353
  volume-title: Model-View-Controller Pattern
  year: 2009
  ident: B129
– volume: 16
  start-page: 342
  year: 2017
  ident: B71
  article-title: Classification of lunge biomechanics with multiple and individual inertial measurement units
  publication-title: Sports Biomech
  doi: 10.1080/14763141.2017.1314544
– volume-title: Self-Organizing Maps
  year: 2001
  ident: B137
  doi: 10.1007/978-3-642-56927-2
– ident: B27
– volume: 58
  start-page: 155
  year: 2017
  ident: B70
  article-title: Classification of deadlift biomechanics with wearable inertial measurement units
  publication-title: J Biomech
  doi: 10.1016/j.jbiomech.2017.04.028
– volume-title: Motor Learning and Control: From Theory to Practice
  year: 2010
  ident: B113
– volume: 2
  start-page: 23
  year: 2014
  ident: B66
  article-title: Toward automatic activity classification and movement assessment during a sports training session
  publication-title: IEEE Internet Things J
  doi: 10.1109/JIOT.2014.2377238
– volume: 7
  start-page: 79
  year: 2014
  ident: B63
  article-title: A preliminary investigation of the use of inertial sensing technology for the measurement of hip rotation asymmetry in horse riders
  publication-title: Sports Technol
  doi: 10.1080/19346182.2014.905949
– volume-title: Dense RGB-D Map-Based Human Tracking and Activity Recognition Using Skin Joints Features and Self-Organizing Map
  year: 2018
  ident: B56
– ident: B15
– ident: B97
– ident: B117
– ident: B28
– ident: B19
– volume: 22
  start-page: 44
  year: 2015
  ident: B9
  article-title: Motion games in real sports environments
  publication-title: Interactions
  doi: 10.1145/2731182
– volume: 41
  start-page: 841
  year: 2009
  ident: B133
  article-title: Coding gestural behavior with the NEUROGES-ELAN system
  publication-title: Behav Res Methods
  doi: 10.3758/BRM.41.3.841
SSID ssj0002505115
Score 2.2810857
Snippet Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive...
SourceID doaj
pubmedcentral
proquest
pubmed
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
StartPage 1326807
SubjectTerms action recognition
digital sports technologies
smart sports
Sports and Active Living
sports telematics
telematics applications
training methods
Title Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis
URI https://www.ncbi.nlm.nih.gov/pubmed/38689871
https://www.proquest.com/docview/3049718391
https://pubmed.ncbi.nlm.nih.gov/PMC11058639
https://doaj.org/article/6e6b6b879c9641feb4e2d6acb57dbc3d
Volume 6
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELYqTlwqHi0EKHKligsKkPUr4QYVCFUqJ1bamzV-AdIqIHZpxb_vjJPd7iJULr1FiaM4Htvzffb4G8a-NcknrYwvPaLzUkZJidwp1pX2oNDDnbhIRPHntb4ayh8jNVpI9UUxYZ08cNdwxzpqp11tGt9oWaXoZBwEDd4pE5wXgWZf9HkLZIrmYHLsiHW6UzLIwprjRCwR-eBAHiEB0zXlj13wRFmw_y2U-TpYcsH7XK6xjz1s5GddddfZh9husI2_mI8f8JyvfLLJwkV7RyIa7S3_RasCLw7GYz5LBXHKSYvqdxYg5DmSArEmhzZwT0er8LrP3MNn0QF8QjHuVBoLQa9h8okNLy9uvl-VfS6F0iMBnpYieglaaBAxDgbgQXoHMklJHENQdghjwkkFtQnaACiRpAOlg3ciqCoZ8ZmttA9t3GZcgUkyhtpJFaXXHmIikfwUZYBKu1Cwatau1vdC4_STY4uEg2xhsy0s2cL2tijY4fydx05m45-lz8lc85IkkZ1vYMexfcex73Wcgn2dGdvikKJ9Emjjw_PE0s6jIeRYFWyrM_78U6LWdYMks2D1UrdYqsvyk_b-Lst2I9BSNQLCnf9R-122Si1C-1qV3mMr06fn-AXh0dTt55Gwn9et_gDUahQQ
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Enhancing+volleyball+training%3A+empowering+athletes+and+coaches+through+advanced+sensing+and+analysis&rft.jtitle=Frontiers+in+sports+and+active+living&rft.au=Salim%2C+Fahim+A&rft.au=Postma%2C+Dees+B+W&rft.au=Haider%2C+Fasih&rft.au=Luz%2C+Saturnino&rft.date=2024-04-16&rft.eissn=2624-9367&rft.volume=6&rft.spage=1326807&rft_id=info:doi/10.3389%2Ffspor.2024.1326807&rft_id=info%3Apmid%2F38689871&rft.externalDocID=38689871
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2624-9367&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2624-9367&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2624-9367&client=summon