last edited: 2016.10.01


The ability to coordinate the movement and stabilization of the many joints in the upper extremity is critically important for simple tasks of daily living to complex athletic endeavors. Consider the seemingly simple task of bringing a spoonful of soup to your mouth. An inability to stabilize the hand as it moves toward the mouth would defeat the task and diminish independent living.  In fact this simple undertaking is a challenging control task.  Our multidisciplinary team of biomedical engineers, clinicians and neuroscientists seek to understand how the brain uses sensory information to optimize the control of motion of the arms and hands.  By understanding how the sensorimotor control systems contribute to superb performance and how they degrade due to neural injury (e.g., stroke, concussion, and neurodevelopmental disorders), we seek to provide the knowledge and tools needed to develop and deliver individualized training or therapeutic interventions that optimize motor performance throughout the lifespan.

The following list samples ongoing projects in the NeuroMotor Control Lab:

Coordination and Discoordination of Limb Posture and Movement Following Stroke

This project analyzes disordered control of posture and movement during reaching and stabilization tasks in patients with stroke. Prior studies have shown that control of posture and movement may be differentially impaired, and contribute importantly to disability. Our recent work has shown that normal reaching requires control of both posture and movement, that these two components of the task are specified by different neural mechanisms.  Accuracy of the overall task is dependent on adaptation (learning) of coordination between the two components.

We hypothesize that major impairment in reaching post-stroke arises from improper scaling and timing of muscle coactivation, thus limiting the independence of arm trajectory and position control.  Inability to relax active muscles might be expected to hinder posture and movement control movement because muscle forces may not be able to create joint torques large enough to full compensate for this history-dependent limb postural bias (i.e., strength limitations).  Alternatively, control may be degraded because the coordination (timing) between muscle activation/deactivation is compromised.  We therefore study how stroke degrades the ability to recruit and relax the balanced muscle co-contractions, and the extent to which this control deficit degrades the coordination between limb posture and movement.  Using a planar robot arm and novel EMG biofeedback methods, we characterize the stroke-related changes in: 1) the ability to maintain postural stability throughout the workspace via graded coactivation of antagonist muscles; and 2) the latency and developmental time course of different levels of coactivation.

Related Publications

Bengtson MC, Mrotek LA, Stoeckmann T, Ghez C, Scheidt RA.  Neuromuscular control deficits revealed in an isometric force production task after stroke. In Preparation.

Mrotek LA, Bengtson M, Stoeckmann T, Botzer L, Ghez CP, McGuire J, Scheidt RA (2017) The Arm Movement Detection (AMD) Test– a fast robotic test of proprioceptive acuity in the arm. J Neural Eng Rehab 14:64; DOI 10.1186/s12984-017-0269-3.

Laczko J, Scheidt RA, Simo LS, Piovesan D. (2017) Inter-joint coordination deficits revealed in the decomposition of endpoint jerk during goal-directed arm movement after stroke. IEEE Trans. Neural Sys Rehab Eng. DOI: 10.1109/TNSRE.2017.2652393.

Conrad MO, Gadhoke B, Scheidt RA, Schmit BD (2015) Effect of tendon vibration on Hemiparetic arm stability in unstable workspaces. PLoS One 10(12):e0144377.

Simo LS, Botzer L, Ghez C, Scheidt RA (2014) A robotic test of proprioception within the hemiparetic arm post-stroke. J Neural Eng Rehab 11:77.1-12.

Mrotek LA, Stoeckmann T, Bengtson M, Ghez C, Scheidt RA (2013) Deficits of sensorimotor control and their impact on limb stabilization post-stroke: a case series. Proc Am Soc NeuroRehab, San Diego, CA.

Scheidt RA, Ghez C, Asnani S. (2011) Patterns of hypermetria and terminal co-contraction during point-to-point movements demonstrate independent action of trajectory and postural controllers. J Neurophysiol. 106(5), 2368-2382.

Simo LS, Ghez C, Botzer L, Scheidt RA (2011) A quantitative and standardized robotic method for the evaluation of arm proprioception after stroke. Proc IEEE EMBS Soc, Boston, MA: 8227-30.

Conrad MO, Scheidt RA, Schmit BD. (2011) Effects of wrist tendon vibration on arm tracking in people post-stroke. J. Neurophysiol 106(3), 1480-8.

Conrad MO, Scheidt RA, Schmit BD (2011) Effects of wrist tendon vibration on targeted upper-arm movements in poststroke hemiparesis. Neurorehanil Neural Repair (1), 61-70.

Stoeckmann T, Sullivan K, Scheidt RA. (2009) Elastic, viscous, and mass load effects on post-stroke muscle recruitment and cocontraction during reaching: A pilot study. Phys Ther 89:1-14.

Scheidt RA, Ghez C (2007) Separate adaptive mechanisms for controlling trajectory and final position in reaching. J. Neurophysiol. 98: 3600–3613

Ghez C Scheidt RA, Heijink H (2007) Different learned coordinate frames for planning trajectories and final positions in reaching. J. Neurophysiol. 98: 3614-3626

Scheidt RA, Stoeckmann T (2007) Reach adaptation and final position control amid environmental uncertainty following stroke. J. Neurophysiol. 97: 2824-2836.

Visual, Proprioceptive and Augmented Sensory Feedback Contributions to the Control of Arm and Hand Movements

Adaptive improvements in task performance are likely to be dependent on a variety of feedback sources including vision and the proprioceptive sensors that signal the physical state of the limb (e.g., muscle spindle receptors, Golgi tendon organs and mechanoreceptors in the skin). It is not clear how the central nervous system integrates the different forms of sensory information to drive improvements in task performance. Experimental evidence has shown that sensorimotor adaptation is driven strongly by both visual and proprioceptive feedback of kinematic features of movement including the curvature and/or smoothness of reaching movements. The goal of this research is to characterize how the central nervous system combines sensory feedback visual, proprioceptive, and synthesized vibrotactile feedback of motor performance to optimize motor commands during upper extremity tasks.

This characterization can be applied to several situations.  Understanding the role of the different sensory modalities in the motor adaptation process will likely be critical to the development of new technologies intended to facilitate motor relearning and rehabilitation of patients with neural injury or neurodevelopmental disorders and to optimize motor performance in athletics. For example, we have shown that error augmentation can transiently improve reach performance after stroke. Our research involves technology development that enables us to determine the extent to which sensory feedback can be manipulated to mitigate sensorimotor control deficits such as reduced proprioceptive sensation post-stroke and to optimize motor performance in healthy people.

Related Publications

Ballardini G, Carlini G, Giannoni P, Scheidt RA, Nisky I, Casadio M. Tactile-STAR: A novel tactile STimulator And Recorder system for evaluating and improving tactile perception. In Revision: Frontiers in Neurorobotics

Krueger A, Giannoni P, Casadio M, Scheidt RA (2017) Optimizing vibrotactile feedback to enhance real-time control of the arm during reach and stabilization tasks. J Neural Eng Rehab 14:36; DOI 10.1186/s12984-017-0248-8.

Tzorakoleftherakis E, Murphey TD, Scheidt RA (2016) Augmenting sensorimotor control using goal-aware vibrotactile stimulation during reaching and manipulation behaviors. Exp Brain Res 234: 2403-2414.

Judkins T, Scheidt RA (2014) Visuo-proprioceptive interactions during adaptation of the human reach. J Neurophysiol 111: 868-887. doi:10.1152/jn.00314.2012.

Tzorakoleftherakis E, Muss-Ivaldi FA, Scheidt RA, Murphey TD (2014) Effects of Optimal Tactile Feedback in Balancing Tasks: a Pilot Study IEEE American Control Conf. Portland, OR

Heenan M, Scheidt RA, Woo D, Beardsley SA (2014) Upper extremity motor dysfunction and impairments in sensorimotor control in Multiple Sclerosis – a pilot study. J NeuroEng Rehab. 11:170, doi:10.1186/1743-0003-11-170

Salowitz NMG, Dolan B, Remmel R, Van Hecke A, Mosier KM, Simo L, Scheidt RA. (2014) Simultaneous robotic manipulation and functional magnetic resonance imaging in children with autism spectrum disorders. J System, Cybern & Inform. 12: 67-73.

Carson A, Salowitz N, Scheidt RA, Van Hecke A (2014) EEG Coherence in Children with and without Autism Spectrum Disorders: Decreased Interhemispheric Connectivity in Autism. Autism Research, DOI: 10.1002/aur.1367.

Patton J, Wei Y, Bajaj P, Scheidt RA. (2013) Visuomotor learning enhanced by augmenting instantaneous trajectory error feedback during reaching. PLoS One 8(1): e46466. doi:10.1371/journal .pone.0046466

Salowitz NMG, Eccarius P, Karst J, Meyer A, Schohl K, Stevens S, Vaughan Van Hecke A, Scheidt RA. (2012) Brief Report: Visuo-spatial guidance of movement during gesture imitation and mirror drawing in children with autism spectrum disorders. J Autism Develop Disord. DOI: 10.1007/s10803-012-1631-8.

Heenan ML, Scheidt RA, Beardsley SA (2011) Visual and proprioceptive contributions to stabilization and tracking movements in humans. Proc IEEE EMBS Soc., Boston, MA: 7356-9.

Scheidt RA, Lillis KP, Emerson SJ. (2010) Visual, motor and attentional influences on proprioceptive discrimination between straight and curved hand paths in reaching. Exp. Brain Res. 204:239-254.

Scheidt RA, Conditt M, Secco EL, Mussa-Ivaldi FA (2005) Interaction of visual and proprioceptive feedback during adaptation of human reaching movements J Neurophysiol 93: 3200-13.

Scheidt RA, Dingwell JB, Mussa-Ivaldi FA. (2001) Learning to move amid uncertainty. J Neurophysiol 86, 971-985.

Sensory and Motor Representations of “Task Space” During Learning and Performance of Goal-Directed Movements

Performance and Learning – An important question in the study of goal-directed movement is how the brain coordinates changes within the set of highly-redundant control variables (e.g. motor cortical pyramidal cells, spinal stretch reflex thresholds, muscle forces, joint torques, etc.) to produce desired changes in the low-dimensional state of a controlled element (e.g. hand kinematics and/or kinetics represented in visual, proprioceptive and/or novel vibrotactile displays).  We use seemingly simple tasks to examine this question, such as asking subjects to capture a spatial target using point-to-point movements of the grasped handle of a robotic arm or discrete finger gestures captured with a data glove.  In both cases, the brain must not only discriminate between control variables that influence task performance from those that do not, but it must also determine how much task-relevant control variables should change to bring about the desired performance. Sensory feedback plays a critically important role in differentiating task relevant from task irrelevant control variables.  Resolving this so-called “redundancy problem” require 'structural' and 'parametric' learning. The long-term goal of this work is to understand how the nervous system forms and utilizes the sensory and motor “maps” of space that both define and are spanned by task goals.

Training – The need for effective design of multi-task training schedules is ubiquitous in activities such as sports, music, and motor rehabilitation after brain injury. However, it is unclear how training schedules should be structured to optimize overall performance and learning.  We use computational models of sensorimotor learning and optimal control theory to develop training strategies and novel technologies that optimize learning, retention, and performance of skilled actions involving limb movement and stabilization behaviors. 

Related Publications

Lee JY, Oh Y, Kim SS, Scheidt RA, Schweighofer N (2016) Optimal schedules in multitask motor learning. Neural Comput (4):667-85.

Ranganathan R, Weiser J, Mosier K, Mussa-Ivaldi FA, Scheidt RA.  (2014) Facilitation and interference in learning the geometric structure of goal-directed movements. J Neurosci. 34(24): 8289-8299.

Scheidt RA, Zimbelman J, Salowitz N, Suminski A, Simo L, Houk J, Mosier KM. (2012) Remembering forward: Neural correlates of memory and prediction in human motor adaptation. NeuroImage 59: 582-600. DOI: 10.1016/j.neuroimage.2011.07.072.

Liu X, Mosier KM, Mussa-Ivaldi FA, Casadio M, Scheidt RA. (2011) Reorganization of finger coordination patterns during adaptation to rotation and scaling of a newly-learned sensorimotor transformation. J Neurophysiol. 105:454-473.

Scheidt RA, Lillis KP, Emerson SJ. (2010) Visual, motor and attentional influences on proprioceptive discrimination between straight and curved hand paths in reaching. Exp. Brain Res. 204:239-254.

Liu X, Scheidt RA. (2008) Contributions of online visual feedback to the learning and generalization of novel finger coordination patterns. J. Neurophysiol 99:2546-2557.

Mosier KM, Scheidt RA, Acosta S, Mussa-Ivaldi FA (2005) Remapping hand movements in a novel geometrical environment. J Neurophysiol. 94: 4362–4372.

Scheidt RA, Conditt M, Secco EL, Mussa-Ivaldi FA (2005) Interaction of visual and proprioceptive feedback during adaptation of human reaching movements J Neurophysiol 93: 3200-13.

Scheidt RA, Dingwell JB, Mussa-Ivaldi FA. (2001) Learning to move amid uncertainty. J Neurophysiol 86, 971-985.