Origins of Intermittency in Slow Movements


It has been reported in the literature that the smoothness of human subjects' arm/hand movements vanishes as the movements become slower. Intermittencies in the movement are observed as distinct peaks in the speed profile. Doeringer and Hogan (1998) proposed two possibilities for the origin of intermittency in slow movements: (1) noise in neuromuscular circuitry, and (2) a movement planner that can only construct simple movements. They showed that the intermittency can not be due to noise or delays in visual feedback.

In the first part of the study in collaboration with Dr. Zhigang Deng and Qin (Eric) Gu from the Department of Computer Science at University of Houston (Computer Graphics and Interactive Media Lab - CGIM), we designed an experiment to evaluate the two propositions. We used a Vicon 3D motion capture system to record trajectories of fingertip, wrist, elbow and shoulder as five participants completed a simple manual circular tracking task at various constant speed levels. Statistical analyses indicated that movement intermittency, quantified by a number of peaks metric, increased in distal direction. When the movement execution is thought as a serial process where more noise gets introduced at every joint or muscle involved in the movement, this finding supports the neuromuscular noise model for origins of intermittency. Additionally, movement speed was determined to have a significant effect on intermittency, while orientation of the task plane showed no significance.

In the second part of the study, we are developing neuromuscular models of human arm movement in order to determine whether intermittency in slow movements can be attributed to muscle actuation mechanisms. Specifically, we are focusing on motor unit recruitment models for muscles undergoing non-isometric contractions in combination with human arm dynamics. We hope to arrive at a disambiguation of the terms movement intermittency, movement variability and submovements. An insight that will be gained from exploring sources of movement variability in slow arm movements of healthy individuals can lead to improved and more reliable motor function improvement measures for stroke patients.