Sensorimotor Control of Tracking Movements at Various Speeds for Stroke Patients as Well as Age-Matched and Young Healthy Subjects

There are aging- and stroke-induced changes on sensorimotor control in daily activities, but their mechanisms have not been well investigated. This study explored speed-, aging-, and stroke-induced changes on sensorimotor control. Eleven stroke patients (affected sides and unaffected sides) and 20 control subjects (10 young and 10 age-matched individuals) were enrolled to perform elbow tracking tasks using sinusoidal trajectories, which included 6 target speeds (15.7, 31.4, 47.1, 62.8, 78.5, and 94.2 deg/s). The actual elbow angle was recorded and displayed on a screen as visual feedback, and three indicators, the root mean square error (RMSE), normalized integrated jerk (NIJ) and integral of the power spectrum density of normalized speed (IPNS), were used to investigate the strategy of sensorimotor control. Both NIJ and IPNS had significant differences among the four groups (P<0.01), and the values were ranked in the following order: young controls < age-matched controls <unaffected sides of stroke patients <affected sides of stroke patients, which could be explained by the stroke- and aging-induced increase in reliance on feedback control. The RMSE increased with the increase in the target speed and the NIJ and IPNS initially declined and then remained steady for all four groups, which indicated a shift from feedback to feedforward control as the target speed increased. The feedback-feedforward trade-off induced by stroke, aging and speed might be explained by a change in the transmission delay and neuromotor noise. The findings in this study improve our understanding of the mechanism underlying the sensorimotor control and neurological changes caused by stroke and aging.

[1]  K. J. Craik THEORY OF THE HUMAN OPERATOR IN CONTROL SYSTEMS , 1948 .

[2]  R. Miall,et al.  Visuomotor tracking with delayed visual feedback , 1985, Neuroscience.

[3]  R A Abrams,et al.  Optimality in human motor performance: ideal control of rapid aimed movements. , 1988, Psychological review.

[4]  C. Trombly Deficits of reaching in subjects with left hemiparesis: a pilot study. , 1992, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[5]  R. Miall,et al.  Intermittency in human manual tracking tasks. , 1993, Journal of motor behavior.

[6]  Kang G. Shin,et al.  Computing time delay and its effects on real-time control systems , 1995, IEEE Trans. Control. Syst. Technol..

[7]  V. Gullapalli,et al.  Visual Information and Object Size in the Control of Reaching. , 1996, Journal of motor behavior.

[8]  C. Winstein,et al.  The locus of age-related movement slowing: sensory processing in continuous goal-directed aiming. , 1996, The journals of gerontology. Series B, Psychological sciences and social sciences.

[9]  Daeyeol Lee,et al.  Manual interception of moving targets II. On-line control of overlapping submovements , 1997, Experimental Brain Research.

[10]  G. Stelmach,et al.  Parkinsonism Reduces Coordination of Fingers, Wrist, and Arm in Fine Motor Control , 1997, Experimental Neurology.

[11]  D. Pélisson,et al.  From Eye to Hand: Planning Goal-directed Movements , 1998, Neuroscience & Biobehavioral Reviews.

[12]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[13]  J R Carey,et al.  Tracking control in the nonparetic hand of subjects with stroke. , 1998, Archives of physical medicine and rehabilitation.

[14]  D M Wolpert,et al.  Multiple paired forward and inverse models for motor control , 1998, Neural Networks.

[15]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[16]  R. Miall,et al.  Digital Object Identifier (DOI) 10.1007/s002219900286 RESEARCH ARTICLE , 2022 .

[17]  W. Rymer,et al.  Deficits in the coordination of multijoint arm movements in patients with hemiparesis: evidence for disturbed control of limb dynamics , 2000, Experimental Brain Research.

[18]  G. P. van Galen,et al.  Error, stress and the role of neuromotor noise in space oriented behaviour. , 2000, Biological psychology.

[19]  Scott T. Grafton,et al.  Forward modeling allows feedback control for fast reaching movements , 2000, Trends in Cognitive Sciences.

[20]  Tamar Flash,et al.  Computational approaches to motor control , 2001, Current Opinion in Neurobiology.

[21]  N. Hogan,et al.  Movement Smoothness Changes during Stroke Recovery , 2002, The Journal of Neuroscience.

[22]  Arthur D Kuo,et al.  The relative roles of feedforward and feedback in the control of rhythmic movements. , 2002, Motor control.

[23]  Antony J Hodgson,et al.  Time and magnitude of torque generation is impaired in both arms following stroke , 2003, Muscle & nerve.

[24]  R. Miall,et al.  On-line feedback control of human visually guided slow ramp tracking: effects of spatial separation of visual cues , 2003, Neuroscience Letters.

[25]  Derek G. Kamper,et al.  Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability , 2003, Neural Computation.

[26]  Sybert H. Stroeve,et al.  Learning combined feedback and feedforward control of a musculoskeletal system , 1996, Biological Cybernetics.

[27]  R. D Seidler,et al.  Feedforward and feedback processes in motor control , 2004, NeuroImage.

[28]  J. Eng,et al.  Consequences of increased neuromotor noise for reaching movements in persons with stroke , 2005, Experimental Brain Research.

[29]  David J Ostry,et al.  Generalization of motor learning based on multiple field exposures and local adaptation. , 2005, Journal of neurophysiology.

[30]  R. Shadmehr,et al.  Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration. , 2005, Journal of neurophysiology.

[31]  K. Newell,et al.  Independence between the amount and structure of variability at low force levels , 2006, Neuroscience Letters.

[32]  M. Levin,et al.  Feedback and Cognition in Arm Motor Skill Reacquisition After Stroke , 2006, Stroke.

[33]  Eliseo Stefano Maini,et al.  Using Kinematic Analysis to Evaluate Constraint-Induced Movement Therapy in Chronic Stroke Patients , 2008, Neurorehabilitation and neural repair.

[34]  M. Desmurget,et al.  Computational motor control: feedback and accuracy , 2008, The European journal of neuroscience.

[35]  Sarah F Tyson,et al.  Sensory Loss in Hospital-Admitted People With Stroke: Characteristics, Associated Factors, and Relationship With Function , 2008, Neurorehabilitation and neural repair.

[36]  K. Tong,et al.  Evaluation of velocity-dependent performance of the spastic elbow during voluntary movements. , 2008, Archives of physical medicine and rehabilitation.

[37]  Qinyin Qiu,et al.  Incorporating Haptic Effects Into Three-Dimensional Virtual Environments to Train the Hemiparetic Upper Extremity , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Dagmar Sternad,et al.  Sensitivity of Smoothness Measures to Movement Duration, Amplitude, and Arrests , 2009, Journal of motor behavior.

[39]  Evangelos A. Christou,et al.  Removal of visual feedback alters muscle activity and reduces force variability during constant isometric contractions , 2009, Experimental Brain Research.

[40]  George E. Stelmach,et al.  Movement structure in young and elderly adults during goal-directed movements of the left and right arm , 2009, Brain and Cognition.

[41]  J F Kalaska,et al.  Integration of predictive feedforward and sensory feedback signals for online control of visually guided movement. , 2009, Journal of neurophysiology.

[42]  Hideyuki Tanaka,et al.  Contributions of vision–proprioception interactions to the estimation of time-varying hand and target locations , 2009, Experimental Brain Research.

[43]  Janice I. Glasgow,et al.  Assessment of Upper-Limb Sensorimotor Function of Subacute Stroke Patients Using Visually Guided Reaching , 2010, Neurorehabilitation and neural repair.

[44]  H. Heuer,et al.  Adaptation to a direction-dependent visuomotor gain in the young and elderly , 2010, Psychological research.

[45]  J. Krakauer,et al.  Error correction, sensory prediction, and adaptation in motor control. , 2010, Annual review of neuroscience.

[46]  Simon J. Bennett,et al.  Movement strategies in vertical aiming of older adults , 2012, Experimental Brain Research.

[47]  Richard B Ivry,et al.  Task goals influence online corrections and adaptation of reaching movements. , 2011, Journal of neurophysiology.

[48]  Ji-Woon Park,et al.  Influence of Movement Speed on Accuracy of Tracking Performance Following Stroke , 2011 .

[49]  John G Milton,et al.  The delayed and noisy nervous system: implications for neural control , 2011, Journal of neural engineering.

[50]  Dagmar Sternad,et al.  Neuromotor Noise, Error Tolerance and Velocity-Dependent Costs in Skilled Performance , 2011, PLoS Comput. Biol..

[51]  I. Hwang,et al.  Eye-Hand Synergy and Intermittent Behaviors during Target-Directed Tracking with Visual and Non-visual Information , 2012, PloS one.

[52]  Alexander Münchau,et al.  Increased sensory feedback in Tourette syndrome , 2012, NeuroImage.

[53]  Rong Song,et al.  EMG and kinematic analysis of sensorimotor control for patients after stroke using cyclic voluntary movement with visual feedback , 2013, Journal of NeuroEngineering and Rehabilitation.

[54]  Yoko Yamaguchi,et al.  Advances in Cognitive Neurodynamics (III) , 2013, Springer Netherlands.

[55]  R. Marshall,et al.  Stroke, Cognitive Deficits, and Rehabilitation: Still an Incomplete Picture , 2013, International journal of stroke : official journal of the International Stroke Society.

[56]  Yutaka Sakaguchi,et al.  Mechanisms for Generating Intermittency During Manual Tracking Task , 2013 .

[57]  Gerhard Bauch,et al.  A Three-Component Model of the Control Error in Manual Tracking of Continuous Random Signals , 2013, Hum. Factors.

[58]  M. Hallett,et al.  Sensory aspects of movement disorders , 2014, The Lancet Neurology.