Cursor control by Kalman filter with a non-invasive body–machine interface

Objective We describe a novel human–machine interface for the control of a two-dimensional (2D) computer cursor using four inertial measurement units (IMUs) placed on the user’s upper-body. Approach A calibration paradigm where human subjects follow a cursor with their body as if they were controlling it with their shoulders generates a map between shoulder motions and cursor kinematics. This map is used in a Kalman filter to estimate the desired cursor coordinates from upper-body motions. We compared cursor control performance in a centre-out reaching task performed by subjects using different amounts of information from the IMUs to control the 2D cursor. Main results Our results indicate that taking advantage of the redundancy of the signals from the IMUs improved overall performance. Our work also demonstrates the potential of non-invasive IMU-based body–machine interface systems as an alternative or complement to brain–machine interfaces for accomplishing cursor control in 2D space. Significance The present study may serve as a platform for people with high-tetraplegia to control assistive devices such as powered wheelchairs using a joystick.

[1]  F. Mussa-Ivaldi,et al.  Functional reorganization of upper-body movement after spinal cord injury , 2010, Experimental Brain Research.

[2]  John P. Cunningham,et al.  Neural prosthetic systems: Current problems and future directions , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  S. Hesse,et al.  Upper and lower extremity robotic devices for rehabilitation and for studying motor control , 2003, Current opinion in neurology.

[4]  Hermano Igo Krebs,et al.  Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy , 2003, Auton. Robots.

[5]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[6]  J R Flanagan,et al.  Trajectory adaptation to a nonlinear visuomotor transformation: evidence of motion planning in visually perceived space. , 1995, Journal of neurophysiology.

[7]  Michael J. Black,et al.  Closed-loop neural control of cursor motion using a Kalman filter , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  E N Brown,et al.  A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.

[9]  J. L. Roux An Introduction to the Kalman Filter , 2003 .

[10]  Raul Benitez,et al.  Motor adaptation as a greedy optimization of error and effort. , 2007, Journal of neurophysiology.

[11]  Michael I. Jordan,et al.  Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study , 1995, Experimental Brain Research.

[12]  Shirley G Fitzgerald,et al.  Demographic and socioeconomic factors associated with disparity in wheelchair customizability among people with traumatic spinal cord injury. , 2004, Archives of physical medicine and rehabilitation.

[13]  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.

[14]  J. Wolpaw,et al.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects , 2009, IEEE Reviews in Biomedical Engineering.

[15]  R.A. Cooper,et al.  Electric powered wheelchairs , 2005, IEEE Control Systems.

[16]  Wei Wu,et al.  Neural Decoding of Cursor Motion Using a Kalman Filter , 2002, NIPS.

[17]  D. Wolpert,et al.  Internal models in the cerebellum , 1998, Trends in Cognitive Sciences.

[18]  Alexey N. Pavlov,et al.  Wavelet analysis in neurodynamics , 2012 .

[19]  M Hallett,et al.  Reorganization of corticospinal pathways following spinal cord injury , 1991, Neurology.

[20]  Maureen K. Holden,et al.  Virtual Environments for Motor Rehabilitation: Review , 2005, Cyberpsychology Behav. Soc. Netw..

[21]  L. Paninski,et al.  Spatiotemporal tuning of motor cortical neurons for hand position and velocity. , 2004, Journal of neurophysiology.

[22]  Kara Edwards,et al.  A survey of adult power wheelchair and scooter users , 2010, Disability and rehabilitation. Assistive technology.

[23]  J Galvez,et al.  Robotic gait training: toward more natural movements and optimal training algorithms , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Michael J. Black,et al.  Inferring Hand Motion from Multi-Cell Recordings in Motor Cortex using a Kalman Filter , 2002 .

[25]  M. Casadio,et al.  Body machine interface: Remapping motor skills after spinal cord injury , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[26]  Yiannis Demiris,et al.  Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  John Cadwell,et al.  Focal magnetic coil stimulation reveals motor cortical system reorganized in humans after traumatic quadriplegia , 1990, Brain Research.

[28]  C. Prablanc,et al.  Postural invariance in three-dimensional reaching and grasping movements , 2000, Experimental Brain Research.

[29]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[30]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Marcia Kilchenman O'Malley,et al.  Progressive shared control for training in virtual environments , 2009, World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[32]  U. Frese,et al.  Applying a 3DOF Orientation Tracker as a Human-Robot Interface for Autonomous Wheelchairs , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[33]  L. G. Cohen,et al.  Nervous system reorganization following injury , 2002, Neuroscience.

[34]  Shirley G Fitzgerald,et al.  Comparison of virtual and real electric powered wheelchair driving using a position sensing joystick and an isometric joystick. , 2002, Medical engineering & physics.

[35]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[36]  Vikash Gilja,et al.  A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces. , 2011, Journal of neurophysiology.

[37]  M Hallett,et al.  Mechanisms of Cortical Reorganization in Lower-Limb Amputees , 1998, The Journal of Neuroscience.

[38]  J D Simeral,et al.  Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia , 2011, Journal of neural engineering.

[39]  J. M. Carmena,et al.  Closed-Loop Decoder Adaptation on Intermediate Time-Scales Facilitates Rapid BMI Performance Improvements Independent of Decoder Initialization Conditions , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  Nicolas Schweighofer,et al.  An Adaptive Automated Robotic Task-Practice System for Rehabilitation of Arm Functions After Stroke , 2009, IEEE Transactions on Robotics.

[41]  Michael J. Black,et al.  Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[42]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[43]  A. Mihailidis,et al.  The development of an adaptive upper-limb stroke rehabilitation robotic system , 2011, Journal of NeuroEngineering and Rehabilitation.

[44]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[45]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[46]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[47]  François Routhier,et al.  Driving performance in a power wheelchair simulator , 2012, Disability and rehabilitation. Assistive technology.

[48]  Konrad Paul Kording,et al.  Estimating the sources of motor errors for adaptation and generalization , 2008, Nature Neuroscience.

[49]  N. Hogan An organizing principle for a class of voluntary movements , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.