Xavier Electromyographic Wheelchair Control and Virtual Training

For a variety of individuals, limited hand dexterity yields complications to independently control a power wheelchair. Neurological conditions, including quadriplegia, and traumatic brain injuries can all reduce or eliminate fine motor skills necessary to operate a joystick. For patients with progressive disorders, this acute or chronic progression can affect the hands and limbs at an early stage of the disease. In an effort to extend independence and autonomous mobility, the Xavier system was developed to utilize electromyography signals measured on the temporalis muscles on the face to enable control of a power wheelchair. This study looks to document the human to machine interaction and control scheme as well as discuss the development of a clinical trial protocol to quantify the effectiveness and meaning of the technology on patients. Patient selection in this pilot study was focused on people living with Amyotrophic Lateral Sclerosis (ALS) in conjunction with Mayo Clinic Jacksonville. A review of the clinical protocol and assessment techniques is joined with a discussion about the role of virtual training via designed vehicle simulation to develop muscle isolation and driving mechanics.

[1]  Holly A. Yanco,et al.  Wheelesley: A Robotic Wheelchair System: Indoor Navigation and User Interface , 1998, Assistive Technology and Artificial Intelligence.

[2]  A. L. La Spada,et al.  ALS motor phenotype heterogeneity, focality, and spread , 2009, Neurology.

[3]  Hiroki Tamura,et al.  A Study of the Electric Wheelchair Hands-Free Safety Control System Using the Surface-Electromygram of Facial Muscles , 2010, ICIRA.

[4]  Junuk Chu,et al.  Wearable EMG-based HCI for Electric-Powered Wheelchair Users with Motor Disabilities , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[5]  Bernd Freisleben,et al.  HaWCoS: the "hands-free" wheelchair control system , 2002, ASSETS.

[6]  Jeong-Su Han,et al.  Human-machine interface for wheelchair control with EMG and its evaluation , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[7]  Krista L Best,et al.  The wheelchair skills test (version 2.4): measurement properties. , 2004, Archives of physical medicine and rehabilitation.

[8]  R. L. Kirby,et al.  The Wheelchair Skills Test: a pilot study of a new outcome measure. , 2002, Archives of physical medicine and rehabilitation.

[9]  J. R. Brinkmann,et al.  The natural history of amyotrophic lateral sclerosis , 1993, Neurology.

[10]  Lynn A. Worobey,et al.  Wheelchair Skills Capacity and Performance of Manual Wheelchair Users With Spinal Cord Injury. , 2016, Archives of physical medicine and rehabilitation.

[11]  Huosheng Hu,et al.  EMG-based hands-free wheelchair control with EOG attention shift detection , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[12]  L Fehr,et al.  Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. , 2000, Journal of rehabilitation research and development.

[13]  J. Jutai,et al.  Psychosocial Impact of Assistive Devices Scale (PIADS) , 2002 .

[14]  J. Weiss,et al.  Easy EMG: A Guide to Performing Nerve Conduction Studies and Electromyography , 2004 .

[15]  Niels Birbaumer,et al.  Depression and quality of life in patients with amyotrophic lateral sclerosis. , 2008, Deutsches Arzteblatt international.