Gamified motor learning through high-fidelity sensor technology.

P Mavromoustakos-Blom; V Mylonas; N Nikodelis; V Konstantakos; P. Spronck; T Loizidis; Igor Mayer

doi:10.1109/SeGAH57547.2023.10253778

Abstract

In this paper, we present a framework for gamified motor learning through the use of a serious game and high-fidelity motion capture sensors. Our implementation features an Inertial Measurement Unit and a set of Force Plates in order to obtain real-time, high-frequency measurements of patients' core movements and centre of pressure displacement during physical rehabilitation sessions. The aforementioned signals enable two mechanisms, namely a) a game avatar controlled through patient motor skills and b) a rich data stream for post-game motor performance analysis. Our main contribution is a fine-grained processing pipeline for sensor signals, enabling the extraction of a reliable and accurate mapping between patient motor movements, in-game avatar controls and overall motor performance. Moreover, we discuss the potential of this framework towards the implementation of personalised therapeutic sessions and present a pilot study conducted in that direction.

Original language	English
DOIs	https://doi.org/10.1109/SeGAH57547.2023.10253778
Publication status	Published - 30 Aug 2023
Event	SeGHA - Panteion University of Social and Political Sciences , Athens, Greece Duration: 28 Aug 2023 → 30 Aug 2023 https://www.segah.org/2023/program.html

Conference

Conference	SeGHA
Country/Territory	Greece
City	Athens
Period	28/08/23 → 30/08/23
Internet address	https://www.segah.org/2023/program.html

Keywords

Motor learning
gamification
motion sensors
physical rehabilitation

Access to Document

10.1109/SeGAH57547.2023.10253778

https://www.segah.org/2023/program.html

Cite this

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title = "Gamified motor learning through high-fidelity sensor technology.",

abstract = "In this paper, we present a framework for gamified motor learning through the use of a serious game and high-fidelity motion capture sensors. Our implementation features an Inertial Measurement Unit and a set of Force Plates in order to obtain real-time, high-frequency measurements of patients' core movements and centre of pressure displacement during physical rehabilitation sessions. The aforementioned signals enable two mechanisms, namely a) a game avatar controlled through patient motor skills and b) a rich data stream for post-game motor performance analysis. Our main contribution is a fine-grained processing pipeline for sensor signals, enabling the extraction of a reliable and accurate mapping between patient motor movements, in-game avatar controls and overall motor performance. Moreover, we discuss the potential of this framework towards the implementation of personalised therapeutic sessions and present a pilot study conducted in that direction.",

keywords = "Motor learning, gamification, motion sensors, physical rehabilitation",

author = "P Mavromoustakos-Blom and V Mylonas and N Nikodelis and V Konstantakos and P. Spronck and T Loizidis and Igor Mayer",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; SeGHA ; Conference date: 28-08-2023 Through 30-08-2023",

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AU - Mavromoustakos-Blom, P

AU - Mylonas, V

AU - Nikodelis, N

AU - Konstantakos, V

AU - Spronck, P.

AU - Loizidis, T

AU - Mayer, Igor

PY - 2023/8/30

Y1 - 2023/8/30

N2 - In this paper, we present a framework for gamified motor learning through the use of a serious game and high-fidelity motion capture sensors. Our implementation features an Inertial Measurement Unit and a set of Force Plates in order to obtain real-time, high-frequency measurements of patients' core movements and centre of pressure displacement during physical rehabilitation sessions. The aforementioned signals enable two mechanisms, namely a) a game avatar controlled through patient motor skills and b) a rich data stream for post-game motor performance analysis. Our main contribution is a fine-grained processing pipeline for sensor signals, enabling the extraction of a reliable and accurate mapping between patient motor movements, in-game avatar controls and overall motor performance. Moreover, we discuss the potential of this framework towards the implementation of personalised therapeutic sessions and present a pilot study conducted in that direction.

AB - In this paper, we present a framework for gamified motor learning through the use of a serious game and high-fidelity motion capture sensors. Our implementation features an Inertial Measurement Unit and a set of Force Plates in order to obtain real-time, high-frequency measurements of patients' core movements and centre of pressure displacement during physical rehabilitation sessions. The aforementioned signals enable two mechanisms, namely a) a game avatar controlled through patient motor skills and b) a rich data stream for post-game motor performance analysis. Our main contribution is a fine-grained processing pipeline for sensor signals, enabling the extraction of a reliable and accurate mapping between patient motor movements, in-game avatar controls and overall motor performance. Moreover, we discuss the potential of this framework towards the implementation of personalised therapeutic sessions and present a pilot study conducted in that direction.

KW - Motor learning

KW - gamification

KW - motion sensors

KW - physical rehabilitation

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DO - 10.1109/SeGAH57547.2023.10253778

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