research-article

Psychomotor Learning in Martial Arts: an Opportunity for User Modeling, Adaptation and Personalization

Author:

Olga C. SantosAuthors Info & Claims

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 89 - 92

https://doi.org/10.1145/3099023.3099107

Published: 09 July 2017 Publication History

Abstract

Psychomotor learning is crucial for many kind of tasks that involve the acquisition of motor skills, such as practicing martial arts. In the past two decades, diverse technological solutions have been developed to support the learning of the corresponding motor skills. However, the UMAP (User Modeling, Adaptation and Personalization) community has not taken part in that research efforts and thus, resulting systems do not adapt and personalize the system response to their users' needs. This paper discusses the main features of existing systems to learn martial arts and identifies research opportunities that are to be included in the future agenda for UMAP research.

References

[1]

R.W. Byrne, A.E. Russon. 1998. Learning by imitation: a hierarchical approach. Behav Brain Sci. 21(5), 667--721.

[2]

O.C. Santos. 2016. Training the Body: The Potential of AIED to support Personalized Motor Skills Learning. International Journal of Artificial Intelligence in Education, 26 (2), 730--755.

[3]

J.K. Aggarwal, L. Xia. 2014. Human activity recognition from 3D data: A review, Pattern Recognition Letters, 48 (15),70--80.

[4]

A.I. Cuesta-Vargas, A. Galán-Mercant, J.M. Williams. 2010. The use of inertial sensors system for human motion analysis. Physical Therapy Reviews, 15 (6), 462--473.

[5]

J. Van der Linden, E. Schoonderwaldt, J. Bird, R. Johnson. 2011. MusicJacket-Combining Motion Capture and Vibrotactile Feedback to Teach Violin Bowing. IEEE Trans. Instrum. Meas. 60, 104--113.

[6]

R. Sigrist, G. Rauter, R. Riener, P. Wolf. 2013. Augmented visual, auditory, haptic, and multi-modal feedback in motor learning: A review. Psychonomic Bulletin & Review, 20, 21--53.

[7]

E. Polak, J. Kulasa, A.V. Brito, M.A. Castro, O. Fernandes. 2016. Motion analysis systems as optimization training tools in combat sports and martial arts. Revista de Artes Marciales Asiáticas, León, 10 (2), 105--123.

[8]

P. Thiparpakul, W. Limprasert. 2017. New Design System to Learning Martial Art Via Kinect 2.0. In Proceedings of the 5th International Conference on Information and Education Technology (ICIET '17). ACM, New York, NY, USA, 89--93.

Digital Library

[9]

D.A. Becker. 1997. Sensei: A Real-Time Recognition, Feedback and Training System for T'ai Chi Gestures. M.I.T. Media Lab Perceptual Computing Group Technical Report No 426. Master of Science in Media Technology.

[10]

X. Sun, C.-W. Chen, B.S. Manjunath, 2002. Probabilistic motion parameter models for human activity recognition. In Proceedings of the 16th International Conference on Pattern Recognition, 443--446.

Digital Library

[11]

P.T. Chua, R. Crivella, B. Daly. 2003. Training for physical tasks in virtual environments: Tai Chi. Proceedings of IEEE Virtual Reality, 87--94.

Digital Library

[12]

S. Wolf, R. Sattin, M. Kutner. 2003. Intense t'ai chi exercise training and fall occurrences in older, transitionally frail adults: a randomized, controlled trial. J Am Geriatr Soc., 1:188--189.

[13]

C. Wang, R. Bannuru, J. Ramel, B. Kupelnick, T. Scott, C.H. Schmid. 2010. Tai Chi on psychological well-being: systematic review and meta-analysis. BMC Complementary and Alternative Medicine. 10:23.

[14]

P.-H. Han, Y.-S. Chen, Y. Zhong, H.-L. Wang, Y.-P. Hung. 2017. My Tai-Chi coaches: an augmented-learning tool for practicing Tai-Chi Chuan. In Proceedings of the 8th Augmented Human International Conference (AH '17). ACM, New York, NY, USA, Article 25, 4 pages.

Digital Library

[15]

T. Iwaanaguchi, M. Shinya, S. Nakajima, M. Shiraishi. 2015. Cyber Tai Chi - CG-based Video Materials for Tai Chi Chuan Self-Study. 2015 International Conference on Cyberworlds (CW), Visby, 365--368.

Digital Library

[16]

O. Portillo-Rodriguez, O. Sandoval-Gonzalez, E. Ruffaldi, R. Leonardi, C. Avizzano, M. Bergamasco. 2008. Real-time gesture recognition, evaluation and feed-forward correction of a multimodal Tai-Chi platform. International Workshop on Haptic and Audio Interaction Design. HAID 2008: Haptic and Audio Interaction Design. Lect. Notes Comput. Sci. 2008, 5270, 30--39.

Digital Library

[17]

K. Kunze, M. Barry, E.A. Heinz, P. Lukowicz, D. Majoe, J. Gutknecht. 2006. Towards Recognizing Tai Chi - An Initial Experiment Using Wearable Sensors. 3rd International Forum on Applied Wearable Computing 2006, Bremen, Germany, 2006, 1--6.

[18]

P. Hämäläinen, T. Ilmonen, J. Höysniemi, M. Lindholm, A. Nykänen. 2005. Martial arts in artificial reality. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '05). ACM, NY, USA, 781--790.

Digital Library

[19]

F. Goodman, A. Popovic, P. Brady. 2010. The Practical Step-by-step Guide To Martial arts: T'ai Chi & Aikido. London: Hermes House, 512 pages.

[20]

D. James, W. Uroda, T. Gibson. 2005. Dynamics of swing: A study of classical japanese swordsmanship using accelerometers. In A. S. Ujihashi S. (Ed.). Presented at the Asia-Pacific Congress on Sports Technology, Tokyo (2005).

[21]

A. Timmi. 2012. Biomechanical analysis of Karate techniques based on the evaluation of the body kinetic energy from 3D mocap data. 1st IMACSSS (International Martial Arts and Combat Sports Scientific Society). Genova, 8--10 June 2012,

[22]

C. Chye, M. Sakamoto, T. Nakajima. 2014. An Exergame for Encouraging Martial Arts. Human-Computer Interaction, Part III, HCII 2014, LNCS 8512, 221--232.

[23]

A. Bloomfield, N. Badler. 2008. Virtual Training via vibrotactile arrays. Teleoper. Virtual Environ, 17, 103--120.

Digital Library

[24]

M. Takahata, K. Shiraki, Y. Sakane, Y. Takebayashi. 2004. Sound feedback for powerful karate training. In Proceedings of the 2004 conference on New interfaces for musical expression (NIME '04), Michael J. Lyons (Ed.). National University of Singapore, Singapore, Singapore, 13--18.

Digital Library

[25]

E. A. Heinz, K. S. Kunze, M. Gruber, D. Bannach and P. Lukowicz. 2006. Using Wearable Sensors for Real-Time Recognition Tasks in Games of Martial Arts - An Initial Experiment. 2006 IEEE Symposium on Computational Intelligence and Games, Reno, NV, 2006, 98--102.

[26]

D.Y. Kwon, M. Gross. 2005. Combining Body Sensors and Visual Sensors for Motion Training. In Proceedings of Advances in Computer Entertainment Technology, Valencia, Spain, 15--17 June 2005, 94--101.

Digital Library

[27]

S. Phunsa, N. Potisarn and S. Tirakoat, 2009. Edutainment -- Thai Art of Self-Defense and Boxing by Motion Capture Technique. 2009 International Conference on Computer Modeling and Simulation, Macau, 152--155.

Digital Library

[28]

G. Mustapha, M.F.A. Razak, M.S.M. Hamzah, N.H.M. Yahya, J. Mahmud. 2016. The Development of a Low Cost Motion Analysis System: Cekak Visual 3D V1.0. International Journal of GEOMATE, 11 (24), 2248--2252.

[29]

Y. Shinagawa, J. Nakajima, T. L. Kunii, K. Hara. 1997., Capturing and analyzing stability of human body motions using video cameras. Computer Animation '97, Geneva, 1997, 48--57.

Digital Library

[30]

E. H. Chi. 2005. Introducing wearable force sensors in martial arts. IEEE Pervasive Computing, 4 (3), 47--53.

Digital Library

[31]

Aikido3D: http://www.aikido3d.com/.

[32]

K. Kiili. 2005. Digital game-based learning: Towards an experiential gaming model. The Internet and Higher Education 8(1), 13--24.

[33]

A. Canessa, M. Chessa, A. Gibaldi, S. P. Sabatini, F. Solari. 2014. Calibrated depth and color cameras for accurate 3D interaction in a stereoscopic augmented reality environment. Vis. Commun. Image Represent, 25 (1), 227--237.

Digital Library

[34]

L. Emering, R. Boulic and D. Thalmann. 1998. Interacting with virtual humans through body actions. IEEE Computer Graphics and Applications, 18 (1), 8--11.

Digital Library

[35]

C.J. Winstein, R.A. Schmidt, 1990. Reduced frequency of knowledge of results enhances motor skill learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(4), 677--691.

[36]

G.C. Burdea. 1996. Force and Touch Feedback for Virtual Reality. New York: Wiley.

Digital Library

[37]

O.C. Santos, M. Eddy. 2017. Modeling Psychomotor Activity: Current Approaches and Open Issues. In UMAP 2017 Adjunct proceedings (in press).

Digital Library

[38]

M. Hausen, P.P. Soares, M.P., Araújo, F. Porto, E. Franchini, C.A. Bridge, J. Gurgel. 2017. Physiological responses and external validity of a new setting for taekwondo combat simulation. PLoS ONE 12(2).

[39]

O.C. Santos. 2016. Emotions and personality in e-learning systems: an affective computing perspective. In Emotions and Personality in Personalized Services (ed. Tkalčič, DeCarolis, de Gemmis, Odić, Košir). Springer, 278--279.

[40]

J. Harasymowicz, R.M. Kalina. 2005. Training of psychomotor adaptation -- a key factor in teaching self-defence. Archives of Budo, 1: 19--26.

[41]

O.C. Santos, J.G. Boticario. 2015. Practical guidelines for designing and evaluating educationally oriented recommendations. Comput. Educ. 2015, 81, 354--374.

Digital Library

[42]

O.C. Santos. 2015. Education still needs Artificial Intelligence to support Motor skill Learning. A Case Study with Aikido. In: Les Contes du Marriage: Should AI stay married to ED? A workshop examining the current and future identity of the AIED field. CEUR Workshop Proceedings, AIED 2015 workshops, 1432 (4), 72--81.

Cited By

Portaz MGarcia PMajumdar RSantos OBoratto LGena CMarras MGermanakos PPopescus E(2024)Mastering Mind and Movement. ACM UMAP 2024 Tutorial on Modeling Intelligent Psychomotor Systems (M3@ACM UMAP 2024)Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3658534(9-12)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3658534
Casas-Ortiz AEcheverria JJimenez-Tellez NSantos O(2024)Exploring the Impact of Partial Occlusion on Emotion Classification From Facial Expressions: A Comparative Study of XR Headsets and Face MasksIEEE Access10.1109/ACCESS.2024.338043912(44613-44627)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3380439
Portaz MCorbi ACasas-Ortiz ASantos O(2024)Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skillsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09393-234:4(1283-1325)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11257-024-09393-2
Show More Cited By

Index Terms

Psychomotor Learning in Martial Arts: an Opportunity for User Modeling, Adaptation and Personalization
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User models
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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Published In

cover image ACM Conferences

UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

456 pages

ISBN:9781450350679

DOI:10.1145/3099023

Editors:
Marko Tkalcic
Free University of Bolzano
,
Dhaval Thakker
University of Leeds
,
Panagiotis Germanakos
SAP SE & University of Cyprus
,
Kalina Yacef
University of Sydney
,
Cecile Paris
CSIRO ICT Centre
,
Olga Santos
Spanish National University for Distance Education
,
General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Michel Desmarais
Polytechnique University, Montreal, Canada
,
Federica Cena
University of Torino, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 09 July 2017

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Portaz MGarcia PMajumdar RSantos OBoratto LGena CMarras MGermanakos PPopescus E(2024)Mastering Mind and Movement. ACM UMAP 2024 Tutorial on Modeling Intelligent Psychomotor Systems (M3@ACM UMAP 2024)Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3658534(9-12)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3658534
Casas-Ortiz AEcheverria JJimenez-Tellez NSantos O(2024)Exploring the Impact of Partial Occlusion on Emotion Classification From Facial Expressions: A Comparative Study of XR Headsets and Face MasksIEEE Access10.1109/ACCESS.2024.338043912(44613-44627)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3380439
Portaz MCorbi ACasas-Ortiz ASantos O(2024)Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skillsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09393-234:4(1283-1325)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11257-024-09393-2
Echeverria JSantos O(2021)Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose EstimationSensors10.3390/s2124837821:24(8378)Online publication date: 15-Dec-2021
https://doi.org/10.3390/s21248378
Echeverria JSantos O(2021)Punch Anticipation in a Karate Combat with Computer VisionAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3461688(61-67)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3461688
Echeverria JC. Santos OVerbert KParra DHammond TKnijnenburg BO'Donovan JTaele P(2021)KUMITRON: Artificial Intelligence System to Monitor Karate Fights that Synchronize Aerial Images with Physiological and Inertial SignalsCompanion Proceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397482.3450730(37-39)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397482.3450730
Corbí ASantos OMitrovic TZhang JChen LChin D(2018)MyShikkoAdjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization10.1145/3213586.3225225(217-218)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3213586.3225225
Santos OEddy MBielikova MHerder EDesmarais MCena F(2017)Modeling Psychomotor ActivityAdjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization10.1145/3099023.3099083(305-310)Online publication date: 9-Jul-2017
https://dl.acm.org/doi/10.1145/3099023.3099083
Santos O(2017)Towards Personalized Vibrotactile Support for Learning AikidoData Driven Approaches in Digital Education10.1007/978-3-319-66610-5_70(593-597)Online publication date: 5-Sep-2017
https://doi.org/10.1007/978-3-319-66610-5_70

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