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Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study

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Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study

Nathanaël Jarrassé et al. Front Bioeng Biotechnol.

Abstract

Transhumeral amputees face substantial difficulties in efficiently controlling their prosthetic limb, leading to a high rate of rejection of these devices. Actual myoelectric control approaches make their use slow, sequential and unnatural, especially for these patients with a high level of amputation who need a prosthesis with numerous active degrees of freedom (powered elbow, wrist, and hand). While surgical muscle-reinnervation is becoming a generic solution for amputees to increase their control capabilities over a prosthesis, research is still being conducted on the possibility of using the surface myoelectric patterns specifically associated to voluntary Phantom Limb Mobilization (PLM), appearing naturally in most upper-limb amputees without requiring specific surgery. The objective of this study was to evaluate the possibility for transhumeral amputees to use a PLM-based control approach to perform more realistic functional grasping tasks. Two transhumeral amputated participants were asked to repetitively grasp one out of three different objects with an unworn eight-active-DoF prosthetic arm and release it in a dedicated drawer. The prosthesis control was based on phantom limb mobilization and myoelectric pattern recognition techniques, using only two repetitions of each PLM to train the classification architecture. The results show that the task could be successfully achieved with rather optimal strategies and joint trajectories, even if the completion time was increased in comparison with the performances obtained by a control group using a simple GUI control, and the control strategies required numerous corrections. While numerous limitations related to robustness of pattern recognition techniques and to the perturbations generated by actual wearing of the prosthesis remain to be solved, these preliminary results encourage further exploration and deeper understanding of the phenomenon of natural residual myoelectric activity related to PLM, since it could possibly be a viable option in some transhumeral amputees to extend their control abilities of functional upper limb prosthetics with multiple active joints without undergoing muscular reinnervation surgery.

Keywords: myoelectric control; pattern recognition; phantom limb; prosthetics; transhumeral amputation; voluntary phantom limb mobility.

Figures

Figure 1
Figure 1
(Left) Global view of the experimental setup during one of the functional tasks of grasping an object (here the foam tennis ball) and releasing it in the dedicated container, with the arm prosthesis controlled through the associated mobilization of the phantom limb. (Right) Photo of the setup being used with P2.
Figure 2
Figure 2
The arm prosthesis prototype includes a motorized elbow (1), an embedded controller based on a Raspberry Pi 3 (2), an electronic wrist rotator (3), and a Touch Bionics Robolimb (4).
Figure 3
Figure 3
View of the residual limbs with the connected six optimal (P1, left), respectively initial twelve (P2, right) pair of electrodes.
Figure 4
Figure 4
P1 typical sEMG patterns associated to the voluntary mobilization of the phantom limb recorded by the six selected electrodes when performing successively 8 different phantom limb movements.
Figure 5
Figure 5
The objects used for the grasping task. From the left to the right, a cylinder made from Balsa wood from the kit of objects from the SHAP (Light et al., 2002) (diameter 60 mm, weight 30 g), a compliant foam tennis ball (diameter 70 mm, weight 12 g), and a clothespin from the Rolyan Graded Pinch Exerciser kit (model Yellow, 1 lb Pinch Exerciser, weight 20 g).
Figure 6
Figure 6
Confusion matrix of online control of the prosthesis for P1 (12 repetitions of the 8 movements, i.e., 92 movements, performed) and P2 (6 repetitions of the 8 movements, i.e., 48 movements, performed). Confusion matrix color scale is normalized across methods and increases from white to black as a function of increasing classification rate.
Figure 7
Figure 7
Visualization of representative grasping sequences of P1.
Figure 8
Figure 8
Representative profiles (associated to the 1–9 action indexes shown on Figure 7) of participant P1 for grasping and releasing the three objects (A: cylinder, B: ball, C: clothespin). Recognized phantom movements (output of the classification algorithm) are shown for each task, along with the associated measured kinematic variations of the 4 joints: elbow and wrist angles, and percentages of closing of hand and pinch. The action indexes (from 1 to 9) are related to the similar indices shown on Figure 7.
Figure 9
Figure 9
(A) Averaged time (± standard error) to grasp the three different objects for the amputated participants controlling the prosthesis with their phantom limb (blue), as well as for the healthy participants by sequential control through a dedicated GUI (red). (B) Averaged time (± SE) to return and release the three different objects for the two groups. (C) Averaged number of actions for completing the 3 “grasp and release” tasks.
Figure 10
Figure 10
Total grasp-and-release time as a function of the number of actions needed to complete the task for all trials and objects and for each patient (in red) and healthy control (in blue). Each symbol represents one trial. The best fitting linear relation and their equations use the same color code.
Figure 11
Figure 11
Plots of the averaged joint kinematic profiles of both elbow and wrist joints, normalized in time and averaged between repetitions and participants for the amputated (blue) and control (red) participants, and the three objects. Standard error is represented by the transparent envelopes around the curves.

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