Work

Gamified Motor Learning Through High-Fidelity Sensor Technology

Year: 2023

Type: conference-paper

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.

Source: SeGAH ... IEEE ... International Conference on Serious Games and Applications for Health/IEEE International Conference on Serious Games and Applications for Health

DOI: 10.1109/segah57547.2023.10253778

Authors: Paris Mavromoustakos Blom, Vasileios Mylonas, Thomas Nikodelis, Vasileios Konstantakos, Pieter Spronck, Theodoros Loizidis, Igor Mayer

Institutions: Tilburg University, Aristotle University of Thessaloniki, St. Luke's Hospital, Breda University of Applied Sciences

Language: English

Topic: Stroke Rehabilitation and Recovery

Subfield: Rehabilitation

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4386953189

FWCI:

Cites: 33

Cited by:

Related to: 10