Submovements composition and quality assessment of reaching movements in subjects with Parkinson's Disease

The segmentation of seemingly continuous movements into segments has been theorized for many years. These segments may be considered as “primitive” movements, or building blocks of more complex movements. The existence of these fragments, or sub-movements as they are called, has been supported by a wide range of studies over the past 100 years. Evidence for the existence of discrete sub-movements underlying continuous human movement has motivated many attempts to “extract” them. Recently, the sub-movement theory gained a great appeal in the rehabilitation field. In fact, understanding movement deficits following CNS lesions, and the relationships between these deficits and functional ability, is fundamental to the development of successful rehabilitation therapies. So, here a novel sub-movements decomposition method is proposed; it is based on a constrained-Expectation-Maximization. This representation allowed us to explore whether the movements are built up of elementary kinematic units by decomposing each signal into a weighted combination of 2D Gaussian functions. These can be used to assess the quality of reaching movements in subjects with Parkinson's Disease.

[1]  G. D'Addio,et al.  Kinematic and EMG patterns evaluation of upper arm reaching movements , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[2]  Carole Fraser,et al.  Measurement of recovery of function in the hemiparetic upper limb following stroke: A preliminary report , 1984 .

[3]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  Joseph A. Doeringer,et al.  Intermittency in preplanned elbow movements persists in the absence of visual feedback. , 1998, Journal of neurophysiology.

[5]  Maria Romano,et al.  Relationships of kinematics indexes with amplitude and velocity of upper arm reaching movement , 2013, 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[6]  Fong-Chin Su,et al.  The Constructs of Kinematic Measures for Reaching Performance in Stroke Patients , 2008 .

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

[8]  Loredana Zollo,et al.  Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation , 2011, Medical & Biological Engineering & Computing.

[9]  J. Wessberg,et al.  Organization of motor output in slow finger movements in man. , 1993, The Journal of physiology.

[10]  Maria Romano,et al.  Kinematic Indexes’ Reproducibility of Horizontal Reaching Movements , 2014 .

[11]  Robert Sessions Woodworth,et al.  THE ACCURACY OF VOLUNTARY MOVEMENT , 1899 .

[12]  Mario Cesarelli,et al.  Kinematics patterns of upper arm reaching movement in robot-mediated therapy , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[13]  P. Morasso Spatial control of arm movements , 2004, Experimental Brain Research.

[14]  T. Flash,et al.  Moving gracefully: quantitative theories of motor coordination , 1987, Trends in Neurosciences.

[15]  T. Milner,et al.  A model for the generation of movements requiring endpoint precision , 1992, Neuroscience.

[16]  P. Bifulco,et al.  Quantitative assessment of the EMG patterns of upper limb muscles during robotic rehabilitation , 2014 .

[17]  G. Fullerton Psychology and physiology. , 1896 .

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

[19]  Paolo Bifulco,et al.  Kinematic evaluation of horizontal reaching movements in rotator cuff disease during robotic rehabilitation , 2014 .

[20]  A. Schnitzler,et al.  The neural basis of intermittent motor control in humans , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[21]  M. Romano,et al.  EMG Patterns in Robot Assisted Reaching Movements of Upper Arm , 2011 .

[22]  Fong-Chin Su,et al.  Kinematical measure for spastic reaching in children with cerebral palsy. , 2005, Clinical biomechanics.

[23]  T. Milner,et al.  The effect of accuracy constraints on three-dimensional movement kinematics , 1990, Neuroscience.

[24]  Margaret A. Finley,et al.  Effect of gravity on robot-assisted motor training after chronic stroke: a randomized trial. , 2011, Archives of physical medicine and rehabilitation.

[25]  Etienne Burdet,et al.  Quantization of human motions and learning of accurate movements , 1998, Biological Cybernetics.

[26]  K. Mauritz,et al.  Motor learning after recovery from hemiparesis , 1994, Neuropsychologia.

[27]  J.-J.J. Chen,et al.  A quantitative and qualitative description of electromyographic linear envelopes for synergy analysis , 1992, IEEE Transactions on Biomedical Engineering.

[28]  K. Flowers,et al.  Programming and execution of movement in Parkinson's disease. , 1987, Brain : a journal of neurology.

[29]  P. Morasso,et al.  Trajectory formation and handwriting: A computational model , 1982, Biological Cybernetics.

[30]  M. Hallett,et al.  A physiological mechanism of bradykinesia. , 1980, Brain : a journal of neurology.

[31]  Maria Romano,et al.  Comparison of measured and predicted reaching movements with a robotic rehabilitation device , 2014, 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA).

[32]  K. Flowers,et al.  Movement variability and bradykinesia in Parkinson's disease. , 1990, Brain : a journal of neurology.

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