Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System

2019 ◽  
Author(s):  
Ji-Hoon Jeong ◽  
Kyung-Hwan Shim ◽  
Dong-Joo Kim ◽  
Seong-Whan Lee
Keyword(s):  
Robot Arm ◽  
Reaching Movement ◽  
Arm Reaching

Evaluation of Power-Assist System by Computer Simulation

2016 ◽  
Vol 20 (3) ◽  
pp. 477-483 ◽  
Author(s):  
Yoshiaki Taniai ◽  
◽  
Tomohide Naniwa ◽  
Yasutake Takahashi ◽  
Masayuki Kawai
Keyword(s):  
Computer Simulation ◽  
Evaluation Framework ◽  
Metabolic Energy ◽  
Evaluation Index ◽  
Optimality Principle ◽  
Upper Arm ◽  
Reaching Movement ◽  
Effective Power ◽  
Arm Reaching ◽  
Motor Paralysis

Powered exoskeletons have been proposed and developed in various works with the aim of compensating for motor paralysis or reducing weight, workload, or metabolic energy consumption. However, development of the power-assist system depends on the development and evaluation of real powered exoskeletons, and few studies have evaluated the performance of the power-assist system by means of computer simulation. In this paper, we propose an evaluation framework based on computer simulation for the development of an effective power-assist system and demonstrate an analysis of a power-assisted upper-arm reaching movement. We employed the optimality principle to obtain the adapted movements of humans for power-assist systems and compared the performances of power- and non-power-assisted movements in terms of the evaluation index of the power-assist system.

scholarly journals The duration of reaching movement is longer than predicted by minimum variance

2016 ◽  
Vol 116 (5) ◽  
pp. 2342-2345 ◽  
Author(s):  
Chunji Wang ◽  
Yupeng Xiao ◽  
Etienne Burdet ◽  
James Gordon ◽  
Nicolas Schweighofer
Keyword(s):  
Minimum Variance ◽  
Reaching Movements ◽  
Movement Duration ◽  
Slow Movement ◽  
Reaching Movement ◽  
End Point ◽  
Parsimonious Explanation ◽  
Arm Reaching ◽  
The Central Nervous System ◽  
Duration Condition

Whether the central nervous system minimizes variability or effort in planning arm movements can be tested by measuring the preferred movement duration and end-point variability. Here we conducted an experiment in which subjects performed arm reaching movements without visual feedback in fast-, medium-, slow-, and preferred-duration conditions. Results show that 1) total end-point variance was smallest in the medium-duration condition and 2) subjects preferred to carry out movements that were slower than this medium-duration condition. A parsimonious explanation for the overall pattern of end-point errors across fast, medium, preferred, and slow movement durations is that movements are planned to minimize effort as well as end-point error due to both signal-dependent and constant noise.

Online gain update for manual following response accompanied by gaze shift during arm reaching

2015 ◽  
Vol 113 (4) ◽  
pp. 1206-1216 ◽  
Author(s):  
Naotoshi Abekawa ◽  
Hiroaki Gomi
Keyword(s):  
Continuous Monitoring ◽  
Visual Motion ◽  
Target Location ◽  
Body Movement ◽  
Gaze Shift ◽  
Reaching Movement ◽  
The Gaze ◽  
Arm Reaching ◽  
Online Corrections ◽  
The Brain

To capture objects by hand, online motor corrections are required to compensate for self-body movements. Recent studies have shown that background visual motion, usually caused by body movement, plays a significant role in such online corrections. Visual motion applied during a reaching movement induces a rapid and automatic manual following response (MFR) in the direction of the visual motion. Importantly, the MFR amplitude is modulated by the gaze direction relative to the reach target location (i.e., foveal or peripheral reaching). That is, the brain specifies the adequate visuomotor gain for an online controller based on gaze-reach coordination. However, the time or state point at which the brain specifies this visuomotor gain remains unclear. More specifically, does the gain change occur even during the execution of reaching? In the present study, we measured MFR amplitudes during a task in which the participant performed a saccadic eye movement that altered the gaze-reach coordination during reaching. The results indicate that the MFR amplitude immediately after the saccade termination changed according to the new gaze-reach coordination, suggesting a flexible online updating of the MFR gain during reaching. An additional experiment showed that this gain updating mostly started before the saccade terminated. Therefore, the MFR gain updating process would be triggered by an ocular command related to saccade planning or execution based on forthcoming changes in the gaze-reach coordination. Our findings suggest that the brain flexibly updates the visuomotor gain for an online controller even during reaching movements based on continuous monitoring of the gaze-reach coordination.

scholarly journals Target of initial sub-movement in multi-component arm-reaching strategy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Luka Peternel ◽  
Jan Babič
Keyword(s):  
Spatial Dispersion ◽  
The Other ◽  
Target Size ◽  
Reaching Movement ◽  
Movement Trajectories ◽  
Other Hand ◽  
Arm Reaching ◽  
Movement Distance ◽  
Peak Dispersion ◽  
Actual Target

AbstractGoal-directed human reaching often involves multi-component strategy with sub-movements. In general, the initial sub-movement is fast and less precise to bring the limb’s endpoint in the vicinity of the target as soon as possible. The final sub-movement then corrects the error accumulated during the previous sub-movement in order to reach the target. We investigate properties of a temporary target of the initial sub-movement. We hypothesise that the peak spatial dispersion of movement trajectories in the axis perpendicular to the movement is in front of the final reaching target, and that it indicates the temporary target of the initial sub-movement. The reasoning is that the dispersion accumulates, due to signal-dependent noise during the initial sub-movement, until the final corrective sub-movement is initiated, which then reduces the dispersion to successfully reach the actual target. We also hypothesise that the reaching movement distance and size of the actual target affect the properties of the temporary target of the initial sub-movement. The increased reaching movement distance increases the magnitude of peak dispersion and moves its location away from the actual target. On the other hand, the increased target size increases the magnitude of peak dispersion and moves its location closer to the actual target.

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