Ergotact introduces the possibility of including force-based rehabilitation activities of the upper limb of post-stroke survivors. These activities are integrated into a dedicated game which is deployed on a tabletop. The patient interacts in the game with a tangible object which has to be moved, rotated, tightened/untightened and lifted according to the gameplay. The surface of the object is equipped with a matrix of force sensors which allows to introduce force-based activities into the game; for the purpose of the game, the object also includes an accelerometer and a gyroscope. The paper presents the concept and first feedbacks from therapists.

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Cited By

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Islam MSultana MAhmed EIslam ARahman AAli AAmin M(2024)An Integrated System for Stroke Rehabilitation Exercise Assessment Using KINECT v2 and Machine LearningIntelligent Human Computer Interaction10.1007/978-3-031-53827-8_20(207-213)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53827-8_20
Pouplin SBonnyaud CBouchigny SMégard CBertholier LGoulamhoussen RFoulon PBensmail DBarbot FRoche N(2023)Feasibility of a serious game system including a tangible object for post stroke upper limb rehabilitation: a pilot randomized clinical studyFrontiers in Neurology10.3389/fneur.2023.117607114Online publication date: 9-Jun-2023
https://doi.org/10.3389/fneur.2023.1176071
Mégard CBouchigny SPouplin SBonnyaud CBertholier LGoulamhoussen RFoulon PRoche NBarbot F(2022)Including Grip Strength Activities into Tabletop Training EnvironmentsSmart Multimedia10.1007/978-3-031-22061-6_19(261-271)Online publication date: 25-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-22061-6_19
Show More Cited By

Index Terms

Ergotact: Including Force-based Activities into Post-stroke Rehabilitation
1. Applied computing
  1. Life and medical sciences
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Tactile and hand-based interfaces

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CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

3673 pages

ISBN:9781450359719

DOI:10.1145/3290607

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
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Anna Cox
University College London, UK
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Vassilis Kostakos
University of Melbourne, Australia

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Islam MSultana MAhmed EIslam ARahman AAli AAmin M(2024)An Integrated System for Stroke Rehabilitation Exercise Assessment Using KINECT v2 and Machine LearningIntelligent Human Computer Interaction10.1007/978-3-031-53827-8_20(207-213)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53827-8_20
Pouplin SBonnyaud CBouchigny SMégard CBertholier LGoulamhoussen RFoulon PBensmail DBarbot FRoche N(2023)Feasibility of a serious game system including a tangible object for post stroke upper limb rehabilitation: a pilot randomized clinical studyFrontiers in Neurology10.3389/fneur.2023.117607114Online publication date: 9-Jun-2023
https://doi.org/10.3389/fneur.2023.1176071
Mégard CBouchigny SPouplin SBonnyaud CBertholier LGoulamhoussen RFoulon PRoche NBarbot F(2022)Including Grip Strength Activities into Tabletop Training EnvironmentsSmart Multimedia10.1007/978-3-031-22061-6_19(261-271)Online publication date: 25-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-22061-6_19
Caldeira CCosta Figueiredo MDodakian Lde Souza CCramer SChen Y(2021)Towards Supporting Data-Driven Practices in Stroke Telerehabilitation TechnologyProceedings of the ACM on Human-Computer Interaction10.1145/34490995:CSCW1(1-33)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449099
Koutsiana ELadakis IFotopoulos DChytas AKilintzis VChouvarda I(2020)Serious Gaming Technology in Upper Extremity Rehabilitation: Scoping ReviewJMIR Serious Games10.2196/190718:4(e19071)Online publication date: 11-Dec-2020
https://doi.org/10.2196/19071

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sEMG-based continuous estimation of joint angles of human legs by using BP neural network

Stroke rehabilitation with a sensing surface

Detecting Compensatory Trunk Movements in Stroke Survivors using a Wearable System

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