The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control

Abstract

Users of myoelectric prostheses can often find them difficult to control. This can lead to passive-use of the device or total rejection, which can have detrimental effects on the contralateral limb due to overuse. Current clinically available prostheses are "open loop" systems, and although considerable effort has been focused on developing biofeedback to "close the loop," there is evidence from laboratory-based studies that other factors, notably improving predictability of response, may be as, if not more, important. Interestingly, despite a large volume of research aimed at improving myoelectric prostheses, it is not currently known which aspect of clinically available systems has the greatest impact on overall functionality and everyday usage. A protocol has, therefore, been designed to assess electromyographic (EMG) skill of the user and predictability of the prosthesis response as significant parts of the control chain, and to relate these to functionality and everyday usage. Here, we present the protocol and results from early pilot work. A set of experiments has been developed. First, to characterize user skill in generating the required level of EMG signal, as well as the speed with which users are able to make the decision to activate the appropriate muscles. Second, to measure unpredictability introduced at the skin-electrode interface, in order to understand the effects of the socket-mounted electrode fit under different loads on the variability of time taken for the prosthetic hand to respond. To evaluate prosthesis user functionality, four different outcome measures are assessed. Using a simple upper limb functional task prosthesis users are assessed for (1) success of task completion, (2) task duration, (3) quality of movement, and (4) gaze behavior. To evaluate everyday usage away from the clinic, the symmetricity of their real-world arm use is assessed using activity monitoring. These methods will later be used to assess a prosthesis user cohort to establish the relative contribution of each control factor to the individual measures of functionality and everyday usage (using multiple regression models). The results will support future researchers, designers, and clinicians in concentrating their efforts on the area that will have the greatest impact on improving prosthesis use.

Keywords: activity monitoring; control; functionality assessment; myoelectric; prosthesis; upper limb.

Figure 1

Donders proposes that reaction times are made up of a series of cognitive and motor processes. According to Donders’ subtraction method, the choice reaction time minus simple reaction time provides the time taken to decide which muscle to activate based on the stimulus.

Figure 2

Reaction time test: (A) Experimental setup and (B) underlying instrumentation. Matlab generates the wait time and LED number and sends them to Arduino1 which starts the test. The user acknowledges that they are ready by pressing the button. The goniometer begins recording and the central LED lights up for 1 s. After a period of 2.5–3 s, one of the larger LED’s lights up and the user moves their hand. Arduino2 connected to the goniometer sends the movement data to Matlab where it is analyzed and a reaction time is sent back to the user.

Figure 3

(A) Static tracking task – participants must aim to keep their signal within the boundaries for a 3 s period. (B) Dynamic tracking task – participants must navigate a car through gaps in approaching walls using muscle contraction and relaxation.

Figure 4

Three electrode interface conditions will be assessed. (A) “Ideal”: no socket, electrodes bandaged to residual limb, (B) “Normal”: prosthetic socket-housed electrodes, and (C) “Additional load”: prosthetic socket + 500 g load.

Figure 5

Prosthesis simulator for use with anatomically intact subjects. The socket is designed to fit over the forearm and fist. Straps allow the socket to be tightened to the persons arm. It is not possible to tailor electrode placement to each person.

Figure 6

Analysis of goniometer data recorded after the LED stimulus presentation in the reaction time experiment. The red marker signifies the point identified as the onset of movement.

Figure 7

Average simple and choice reaction times for the anatomically intact participant and prosthesis users. The decision time is calculated as the difference between the mean CRT and the mean SRT.

Figure 8

(A) Results of the static tracking task. Participants were provided with three opportunities to achieve their best signal. Here, we present the percentage of time the signal was within the boundaries over the 3-second period. (B) Signals from the two prosthesis users – the blue line is the signal being tested, the red dashed line shows the signal from the muscle that should remain relaxed.

Figure 9

Result of reaction time tests to assess “unpredictability” introduced at the electrode–skin interface, by the electrode fit. Prosthesis User 2 demonstrates a larger amount of variability with the prosthetic socket than when using the “ideal” electrode contact setup with the electrodes bandaged to the limb.

Figure 10

Reaction times (hand opening) using the socket-housed electrodes with additional load added to the hand. Prosthesis User 1 noticed slower movement of the hand with the addition of the load, whereas Prosthesis User 2 experienced a large amount of difficulty in overcoming the close function while trying to open the hand.

Figure 11

(A) Mean task duration for each of the difficulty levels (Easy “A,” Medium “B,” and Hard “C”), (B) mean aperture “reach plateau” length as a percentage of the reach phase, (C) mean aperture onset delay as a percentage of the reach phase.

Figure 12

Example eye tracking video – the crosshair shows the point of gaze fixation. Top: both Prosthesis Users looked at the hand at a point in the reach to check their hand aperture. Bottom: the different strategies employed to complete “task B” can be seen – left: simulator user, middle: Prosthesis User 1, and right: Prosthesis User 2 – Prosthesis User 2 struggled to complete this task and would drop the cylinder when the arm was brought to the horizontal, therefore, he delayed this movement.

Figure 13

Results of the gaze analysis for the first successful trial of the medium difficulty task (“task B”) for each of the prosthesis users.

Figure 14

Bilateral arm use (left: 7 days, right: 24 h). The stack at −7 signifies unilateral dominant arm use (anatomical arm), +7 signifies unilateral non-dominant arm use (prosthesis), and 0 signifies both limbs contributing to activity at the same level. Each marker represents 1 s of data and the color density is a count of the number of data points. (A) Top: bilateral arm use for anatomically intact control subject. Arm use is symmetrical across both arms, regardless of limb dominance. (B) Middle: bilateral arm use for Prosthesis User 1. (C) Bottom: bilateral arm use for Prosthesis User 2.