Assessment of Motor Control during Three-Dimensional Movements Tracking with Position-Varying Gravity Compensation

Abstract

Active movements are important in the rehabilitation training for patients with neurological motor disorders, while weight of upper limb impedes movements due to muscles weakness. The objective of this study is to develop a position-varying gravity compensation strategy for a cable-based rehabilitation robot. The control strategy can estimate real-time gravity torque according to position feedback. Then, the performance of this control strategy was compared with the other two kinds of gravity compensation strategies (i.e., without compensation and with fixed compensation) during movements tracking. Seven healthy subjects were invited to conduct tracking tasks along four different directions (i.e., upward, forward, leftward, and rightward). The performance of movements with different compensation strategies was compared in terms of root mean square error (RMSE) between target and actual moving trajectories, normalized jerk score (NJS), mean velocity ratio (MVR) of main motion direction, and the activation of six muscles. The results showed that there were significant effects in control strategies in all four directions with the RMSE and NJS values in the following order: without compensation > fixed compensation > position-varying compensation and MVR values in the following order: without compensation < fixed compensation < position-varying compensation (p < 0.05). Comparing with movements without compensation in all four directions, the activation of muscles during movements with position-varying compensation showed significant reductions, except the activations of triceps and in forward and leftward movements, the activations of upper trapezius and middle parts of deltoid in upward movements and the activations of posterior parts of deltoid in all four directions (p < 0.05). Therefore, with position-varying gravity compensation, the upper limb cable-based rehabilitation robotic system might assist subjects to perform movements with higher quality and improve the participation of robot-aided rehabilitation training. Further studies are needed to explore the effectiveness and clinic application across pathologies.

Kinematic Analysis of Trajectory Dimension-Dependent Sensorimotor Control in Arm Tracking

“…Step 4: The probability function ϕ m averaged all the similarities from any matrices in the time series to another and is expressed as (N , n, r) (11) where fApEn 3D is used to assess the incremental comparisons between the m-and the m + 1-dimensional space.…”

“…3c), which demonstrates that movements made by subjects became less smooth, and the multiple joints were less coordinated. Recent studies have revealed that increasing both the target distance [34] and movement direction [11] influences the coordinated movement response. Other studies have illustrated that to achieve necessary positions, the sensorimotor system should regulate sequential muscle control for the subsequent coordination [6], [15].…”

“…Many factors, such as target size [13], target speed [1], movement direction [11] and delayed visual feedback information [17], are all related to the underlying sensorimotor control during arm tracking. Huysmans et al [13] manipulated the target size to change the precision level in a twodimensional (2D) tracking task that was performed with a pen, and the results showed that increased precision demands were accommodated by both the different organization of submovements and increased muscle activity.…”

“…Ao et al [1] detected the effects of the target speed at 6 levels on feedforward-feedback control during elbow tracking tasks using sinusoidal trajectories, which indicated a shift from feedback to feedforward control as the target speed increased. Huang et al [11] compared the tracking behaviors along four different directions during multijoint upper limb movements, and parametric differences in all four directions reflected notable influences on the movement direction in sensorimotor control. Limanowski et al [17] changed the visual feedback delays of hand movements at eight lengths during sinusoidal tracking tasks at 0.5 Hz in three-dimensional (3D) space and tracked the accuracy and brain activity using functional magnetic resonance imaging (fMRI), which showed that a distributed network of brain regions was used to process visual information at different stages to accurately guide one's actions online.…”

Target-centric angle variables in pursuit tracking

“…The complexity of experimental designs that have been used is quite broad with target and pursuer trajectories embedded in varying dimensionalities of an original fixed coordinate system and various pursuer position generation mechanisms can be involved. These variations can range from target and pursuer trajectories being embedded in a one-dimensional space [8] to both trajectories being embedded in a three dimensional space, such as that used in robotic rehabilitation [9]. Embedding space dimensionalities for trajectories within a specific pursuit tracking task design are not necessarily uniform.…”