Changes in performance over time while learning to use a myoelectric prosthesis

BACKGROUND: Training increases the functional use of an upper limb prosthesis, but little is known about how people learn to use their prosthesis. The aim of this study was to describe the changes in performance with an upper limb myoelectric prosthesis during practice. The results provide a basis to develop an evidence-based training program. METHODS: Thirty-one able-bodied participants took part in an experiment as well as thirty-one age- and gender-matched controls. Participants in the experimental condition, randomly assigned to one of four groups, practiced with a myoelectric simulator for five sessions in a two-weeks period. Group 1 practiced direct grasping, Group 2 practiced indirect grasping, Group 3 practiced fixating, and Group 4 practiced a combination of all three tasks. The Southampton Hand Assessment Procedure (SHAP) was assessed in a pretest, posttest, and two retention tests. Participants in the control condition performed SHAP two times, two weeks apart with no practice in between. Compressible objects were used in the grasping tasks. Changes in end-point kinematics, joint angles, and grip force control, the latter measured by magnitude of object compression, were examined. RESULTS: The experimental groups improved more on SHAP than the control group. Interestingly, the fixation group improved comparable to the other training groups on the SHAP. Improvement in global position of the prosthesis leveled off after three practice sessions, whereas learning to control grip force required more time. The indirect grasping group had the smallest object compression in the beginning and this did not change over time, whereas the direct grasping and the combination group had a decrease in compression over time. Moreover, the indirect grasping group had the smallest grasping time that did not vary over object rigidity, while for the other two groups the grasping time decreased with an increase in object rigidity. CONCLUSIONS: A training program should spend more time on learning fine control aspects of the prosthetic hand during rehabilitation. Moreover, training should start with the indirect grasping task that has the best performance, which is probably due to the higher amount of useful information available from the sound hand

Effect of Feedback during Virtual Training of Grip Force Control with a Myoelectric Prosthesis

The aim of this study was to determine whether virtual training improves grip force control in prosthesis use, and to examine which type of augmented feedback facilitates its learning most. Thirty-two able-bodied participants trained grip force with a virtual ball-throwing game for five sessions in a two-week period, using a myoelectric simulator. They received either feedback on movement outcome or on movement execution. Sixteen controls received training that did not focus on force control. Variability over learning was examined with the Tolerance-Noise-Covariation approach, and the transfer of grip force control was assessed in five test-tasks that assessed different aspects of force control in a pretest, a posttest and a retention test. During training performance increased while the variability in performance was decreased, mainly by reduction in noise. Grip force control only improved in the test-tasks that provided information on performance. Starting the training with a task that required low force production showed no transfer of the learned grip force. Feedback on movement execution was detrimental to grip force control, whereas feedback on movement outcome enhanced transfer of grip force control to tasks other than trained. Clinical implications of these results regarding virtual training of grip force control are discussed

Virtual Training of the Myosignal

To investigate which of three virtual training methods produces the largest learning effects on discrete and continuous myocontrol. The secondary objective was to examine the relation between myocontrol and manual motor control tests.A cohort analytic study.University laboratory.3 groups of 12 able-bodied participants (N = 36).Participants trained the control over their myosignals on 3 consecutive days. Training was done with either myosignal feedback on a computer screen, a virtual myoelectric prosthetic hand or a computer game. Participants performed 2 myocontrol tests and 2 manual motor control tests before the first and after the last training session. They were asked to open and close a virtual prosthetic hand on 3 different velocities as a discrete myocontrol test and followed a line with their myosignals for 30 seconds as a continuous myocontrol test. The motor control tests were a pegboard and grip-force test.Discrete myocontrol test: mean velocities. Continuous myocontrol test: error and error SD. Pegboard test: time to complete. Grip-force test: produced forces.No differences in learning effects on myocontrol were found for the different virtual training methods. Discrete myocontrol ability did not significantly improve as a result of training. Continuous myocontrol ability improved significantly as a result of training, both on average control and variability. All correlations between the motor control and myocontrol test outcome measures were below .50.Three different virtual training methods showed comparable results when learning myocontrol. Continuous myocontrol was improved by training while discrete myocontrol was not. Myocontrol ability could not be predicted by the manual motor control tests

Motor Control Processes When Learning To Use A Prosthetic Device

Prosthetic devices are designed to increase the action possibilities of an amputee. Appropriate actions with upper-extremity prostheses are only possible when these devices can be controlled dexterously. Importantly, the control signals of the neuromotor system necessary to perform a goal-directed action with a prosthesis differ from those control signals used to perform an action with an intact limb. To discuss what it means for the neuromotor system to learn to control an upper limb prosthetic device, the current presentation will start from Bernstein’s (Russian original from 1940, published in English in 1996) insightful treatise on the hierarchical levels for the control of movement. From this overview we aim to make recommendations regarding the issues that research on learning to control a prosthetic device for the upper extremity should focus on

Bernstein's levels of construction of movements applied to upper limb prosthetics

This article addresses the neuromotor control processes underlying the use of an upper limb prosthesis. Knowledge of these processes is used to make recommendations as to how prostheses and prosthesis training should develop to advance the functionality of upper limb prostheses. Obviously, modern-day prostheses are not optimally integrated in neuromotor functioning. The current article frames the problems underlying the handling of upper limb prosthetic devices in the hierarchical levels of construction of movement as proposed by Bernstein (1996). It follows that 1) postural disturbances resulting from prosthetic use should be considered in training and in the development of prosthetic devices, 2) training should take into account that new synergies have to be learned, 3) the feedback about the state of the prosthesis should improve, and 4) the alteration between different grip patterns should be made easy and fast. We observed that many of the current innovations in the prosthetics field are in line with the aim to integrate the prosthesis in sensory-motor functioning.</p

Performance of an arbitrary selected participant of the simple sine pattern and the blocked pattern during the pretest (above) and the retention test (below).

<p>The dashed line represents thepattern asked by the computer, the performance of the participant is shown with the thick line. Increasing the applied grip force was easier to control than letting go, shown by larger drops in the signal. This performance was seen in many participants.</p

Overview of the experimental design.

<p>LF  =  landing position feedback; TF  =  trajectory feedback; CO  =  control; FB  =  feedback; TD  =  target distance.</p

Performance error (SE) of the participants for each of the five blocks of 15 trials in the practiced targets (target 1 to target 6) for both groups that practiced in the order 20-80-40-100-60-120 and 120-60-100-40-80-20 (4A) and the error plotted against each of the target distances (4B) for each of the five blocks of 15 trials within each target distance.

<p>Performance error (SE) of the participants for each of the five blocks of 15 trials in the practiced targets (target 1 to target 6) for both groups that practiced in the order 20-80-40-100-60-120 and 120-60-100-40-80-20 (4A) and the error plotted against each of the target distances (4B) for each of the five blocks of 15 trials within each target distance.</p

Summary of the test-tasks.

<p>Summary of the test-tasks.</p

Experimental setup; a participant in action with the prosthetic simulator attached to the right forearm, controlling ball release by pressing a button with the left hand (A), the measurement setup with the handle (B), and two screenshots of a thrown ball with trajectory feedback (C) and with landing position feedback (D).

<p>Experimental setup; a participant in action with the prosthetic simulator attached to the right forearm, controlling ball release by pressing a button with the left hand (A), the measurement setup with the handle (B), and two screenshots of a thrown ball with trajectory feedback (C) and with landing position feedback (D).</p