The Falling of Momo: A Myo-Electric Controlled Game to Support Research in Prosthesis Training

Myoelectric control makes use of the electrical signals created by the body's muscles as an input to control a device, such as a prosthetic limb or powered orthosis. While myoelectric prostheses have been in use and under constant R&D for almost 50 years, they are not without problems. For myoelectric control to be effective the user has to learn how to activate and control muscles in isolation and in ways that are unintuitive but easily interpreted by the system. Learning how to control muscles in this way can be a frustrating and time consuming process. In this paper, we outline our work to develop a training game that aims to setup prosthesis wearers for success by mapping typical controls used for prostheses to game input. Furthermore, our game provides a wide range of options that allow the input controls and game difficulty to be scaled appropriately to the skill of the player. Above all, our game aims to be fun and, unlike previous myoelectric training games, it focuses on providing a fully featured casual game.

[1]  Yodchanan Wongsawat,et al.  EMG-based upper-limb rehabilitation via music synchronization with augmented reality , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[2]  Albert A. Rizzo,et al.  Development and evaluation of low cost game-based balance rehabilitation tool using the microsoft kinect sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  J. H. You,et al.  Effects of innovative virtual reality game and EMG biofeedback on neuromotor control in cerebral palsy. , 2014, Bio-medical materials and engineering.

[4]  Raoul M. Bongers,et al.  Task-Oriented Gaming for Transfer to Prosthesis Use , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Kathrin Maria Gerling,et al.  Participatory Design of Therapeutic Video Games for Young People with Neurological Vision Impairment , 2015, CHI.

[6]  Yee Mon Aung,et al.  Development of augmented reality rehabilitation games integrated with biofeedback for upper limb , 2011 .

[7]  R.J. Vogelstein,et al.  Air-Guitar Hero: A real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[8]  Erik Scheme,et al.  Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use. , 2011, Journal of rehabilitation research and development.

[9]  Yves G. Losier,et al.  A Bus-Based Smart Myoelectric Electrode/Amplifier—System Requirements , 2011, IEEE Transactions on Instrumentation and Measurement.

[10]  S. Saini,et al.  A low-cost game framework for a home-based stroke rehabilitation system , 2012, 2012 International Conference on Computer & Information Science (ICCIS).

[11]  David R. Flatla,et al.  Calibration games: making calibration tasks enjoyable by adding motivating game elements , 2011, UIST.

[12]  Raoul M. Bongers,et al.  Guideline for training with a myoelectric prosthesis , 2013 .

[13]  C. K. van der Sluis,et al.  Learning to control opening and closing a myoelectric hand. , 2010, Archives of physical medicine and rehabilitation.