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Learning Effects and Retention of Electrical Muscle Stimulation in Piano Playing

Published: 08 October 2023 Publication History

Abstract

Electrical muscle stimulation (EMS)-based systems have been proposed to assist in the learning of motor skills for piano playing. However, learning effects and retention have not been thoroughly evaluated. To address this, we conducted two user studies to investigate the learning effects and retention of EMS for piano playing. Twenty-four novice participants practiced the technique of tremolo, a rapid change between two notes, with both hands under three conditions: without EMS, with EMS on one hand, and with EMS on both hands. The results showed that practicing with EMS on both hands significantly improved tempo accuracy compared to practicing without EMS. A follow-up study of 15 participants confirmed that the improved performance achieved with EMS on both hands was maintained after one week and was not significantly different from practicing without EMS.

References

[1]
Rumen Donchev, Erik Pescara, and Michael Beigl. 2021. Investigating Retention in Passive Haptic Learning of Piano Songs. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2 (2021), 1–14. https://doi.org/10.1145/3463513
[2]
Felipe Augusto dos Santos Mendes, José Eduardo Pompeu, Alexandra Modenesi Lobo, Keyte Guedes da Silva, Tatiana de Paula Oliveira, Andrea Peterson Zomignani, and Maria Elisa Pimentel Piemonte. 2012. Motor learning, retention and transfer after virtual-reality-based training in Parkinson’s disease–effect of motor and cognitive demands of games: a longitudinal, controlled clinical study. Physiotherapy 98, 3 (2012), 217–223. https://doi.org/10.1016/j.physio.2012.06.001
[3]
Ayaka Ebisu, Satoshi Hashizume, and Yoichi Ochiai. 2018. Building a feedback loop between electrical stimulation and percussion learning. In ACM SIGGRAPH 2018 Studio. 1–2. https://doi.org/10.1145/3214822.3214824
[4]
Ayaka Ebisu, Satoshi Hashizume, Kenta Suzuki, Akira Ishii, Mose Sakashita, and Yoichi Ochiai. 2017. Stimulated percussions: method to control human for learning music by using electrical muscle stimulation. In Proceedings of the 8th Augmented Human International Conference. 1–5. https://doi.org/10.1145/3041164.3041202
[5]
Shinichi Furuya, Tatsushi Goda, Haruhiro Katayose, Hiroyoshi Miwa, and Noriko Nagata. 2011. Distinct inter-joint coordination during fast alternate keystrokes in pianists with superior skill. Frontiers in human neuroscience 5 (2011), 50. https://doi.org/10.3389/fnhum.2011.00050
[6]
Mahmoud Hassan, Florian Daiber, Frederik Wiehr, Felix Kosmalla, and Antonio Krüger. 2017. Footstriker: an EMS-based foot strike assistant for running. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 1 (2017), 1–18. https://doi.org/10.1145/3053332
[7]
Kevin Huang, Ellen Yi-Luen Do, and Thad Starner. 2008. PianoTouch: a wearable haptic piano instruction system for passive learning of piano skills. In 2008 12th IEEE international symposium on wearable computers. IEEE, 41–44. https://doi.org/10.1109/ISWC.2008.4911582
[8]
Gillian Murphy, John A Groeger, and Ciara M Greene. 2016. Twenty years of load theory—Where are we now, and where should we go next?Psychonomic bulletin & review 23, 5 (2016), 1316–1340. https://doi.org/10.3758/s13423-015-0982-5
[9]
Alice Nieuwboer, Lynn Rochester, Liesbeth Müncks, and Stephan P Swinnen. 2009. Motor learning in Parkinson’s disease: limitations and potential for rehabilitation. Parkinsonism & related disorders 15 (2009), S53–S58. https://doi.org/10.1016/S1353-8020(09)70781-3
[10]
Arinobu Niijima, Toki Takeda, Ryosuke Aoki, and Shinji Miyahara. 2022. Muscle Synergies Learning with Electrical Muscle Stimulation for Playing the Piano. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–10. https://doi.org/10.1145/3526113.3545666
[11]
Arinobu Niijima, Toki Takeda, Kentaro Tanaka, Ryosuke Aoki, and Yukio Koike. 2021. Reducing Muscle Activity when Playing Tremolo by Using Electrical Muscle Stimulation to Learn Efficient Motor Skills. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 3 (2021), 1–17. https://doi.org/10.1145/3478110
[12]
Jun Nishida, Yudai Tanaka, Romain Nith, and Pedro Lopes. 2022. DigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. 1–12. https://doi.org/10.1145/3526113.3545630
[13]
Romain Nith, Shan-Yuan Teng, Pengyu Li, Yujie Tao, and Pedro Lopes. 2021. DextrEMS: Increasing Dexterity in Electrical Muscle Stimulation by Combining it with Brakes. In The 34th Annual ACM Symposium on User Interface Software and Technology. 414–430. https://doi.org/10.1145/3472749.3474759
[14]
Richard A Schmidt, Timothy D Lee, Carolee Winstein, Gabriele Wulf, and Howard N Zelaznik. 2018. Motor control and learning: A behavioral emphasis. Human kinetics.
[15]
Caitlyn Seim, Nick Doering, Yang Zhang, Wolfgang Stuerzlinger, and Thad Starner. 2017. Passive haptic training to improve speed and performance on a keypad. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 1–13. https://doi.org/10.1145/3132026
[16]
Caitlyn Seim, Tanya Estes, and Thad Starner. 2015. Towards passive haptic learning of piano songs. In 2015 IEEE World Haptics Conference (WHC). IEEE, 445–450. https://doi.org/10.1109/WHC.2015.7177752
[17]
Akifumi Takahashi, Jas Brooks, Hiroyuki Kajimoto, and Pedro Lopes. 2021. Increasing electrical muscle stimulation’s dexterity by means of back of the hand actuation. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–12. https://doi.org/10.1145/3411764.3445761
[18]
Nobuhiro Takahashi, Shinichi Furuya, and Hideki Koike. 2020. Soft exoskeleton glove with human anatomical architecture: production of dexterous finger movements and skillful piano performance. IEEE Transactions on Haptics (2020). https://doi.org/10.1109/TOH.2020.2993445
[19]
Emi Tamaki, Takashi Miyaki, and Jun Rekimoto. 2011. PossessedHand: techniques for controlling human hands using electrical muscles stimuli. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 543–552. https://doi.org/10.1145/1978942.1979018
[20]
Sho Tatsuno, Tomohiko Hayakawa, and Masatoshi Ishikawa. 2017. Supportive training system for sports skill acquisition based on electrical stimulation. In 2017 IEEE World Haptics Conference (WHC). IEEE, 466–471. https://doi.org/10.1109/WHC.2017.7989946
[21]
Gabriele Wulf, Suzete Chiviacowsky, and Priscila Lopes Cardozo. 2014. Additive benefits of autonomy support and enhanced expectancies for motor learning. Human movement science 37 (2014), 12–20. https://doi.org/10.1016/j.humov.2014.06.004

Cited By

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  • (2024)Passive Haptic Rehearsal for Augmented Piano Learning in the WildProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997488:4(1-26)Online publication date: 21-Nov-2024
  • (2024)Motor-Skill-Download System Using Electrical Muscle Stimulation for Enhancing Piano PlayingCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3681942(313-317)Online publication date: 5-Oct-2024

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  1. Learning Effects and Retention of Electrical Muscle Stimulation in Piano Playing

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    cover image ACM Conferences
    ISWC '23: Proceedings of the 2023 ACM International Symposium on Wearable Computers
    October 2023
    145 pages
    ISBN:9798400701993
    DOI:10.1145/3594738
    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: 08 October 2023

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    Author Tags

    1. Electrical muscle stimulation
    2. Learning effect
    3. Piano
    4. Retention

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    • (2024)Passive Haptic Rehearsal for Augmented Piano Learning in the WildProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997488:4(1-26)Online publication date: 21-Nov-2024
    • (2024)Motor-Skill-Download System Using Electrical Muscle Stimulation for Enhancing Piano PlayingCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3681942(313-317)Online publication date: 5-Oct-2024

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