Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis

Abstract

Background: In spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA), specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs) can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs) of the prosthesis.

Methods: A clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects) was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group) was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days.

Results: Subjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric PCA-based controller. Experimental trials showed that the simultaneous use of the two differential channels paradigm was successful.

Conclusions: This work demonstrates that the proposed two-DoFs myoelectric controller based on PCA allows to drive in real-time a prosthetic hand emulator into different prehensile patterns with excellent performance. These results open up promising possibilities for the development of intuitive, effective myoelectric hand controllers.

Figure 1

System overview. The experimental setup included the EMG acquisition module (AM, with four active electrodes placed on the user’s forearm and an acquisition board) and the software control system (CS), which ran on a laptop and was interfaced with the hand (RH) via serial port. The CS acquired and decoded the four EMG signals to generate two independent input signals; these were fed into the PCA-based algorithm that generated and sent the six motor control commands to the robotic hand.

Figure 2

Prosthetic hand emulator and electrodes positioning. Experimental set-up showing the prosthetic hand emulator and the four EMG sensors on the targeted muscles: the flexor (FCR) and extensor carpi radialis (ECR), the extensor pollicis longus (EPL) and the flexor carpi ulnaris (FCU).

Figure 3

Two DoFs control signal generation. Wrist movements re-mapped into Ch₁ and Ch₂ signals variations, used to generate input commands for the PCA-based algorithm. Extending (ext) or flexing (flex) the wrist affected the input control signal Ch₁. Adduction (add) and abduction (abd) movements influenced Ch₂.

Figure 4

CyberHand postures distribution. CyberHand postures over the Ch₁,Ch₂ input signals plane, sampled using a 5×5 grid. Blue, red or dark green backgrounds are used to denote areas corresponding to those hand configurations which are functional for achieving respectively a power, precision or lateral grasp. Faded colours are used to indicate areas where more than one grasp type could be achieved. A black background denotes the open-hand neutral position.

Figure 5

Results: average task-completion rates. Task-completion rates for group G2 on day 1–3 (d1–d3) and for group G1, considering both sets for each object. Black bars represent power grasps, white bars refer to precision grasps and light gray ones to lateral grasps.

Figure 6

Results: object-grasp and task-completion times distributions. Box & whiskers plots representing (A) object-grasp time (Tg) and (B) task-completion time (Tc) distributions for subjects in G2 on day 1,2,3 (d1, d2, d3) and for group G1. Black boxes refer to power grasps, white boxes to precision grasps and the gray ones to lateral grasps. Each box is delimited by the first and third quartile values; thick horizontal lines, instead, highlight median values. Whiskers show the extent of the rest of the data, while crosses represent the outliers.

Figure 7

Object-grasp and task-completion time trends throughout days for each subjects in G2. (A) Object-grasp (Tg) and (B) task-completion (Tc) times for power, precision and lateral grasps throughout trials. Data are sorted following their temporal execution order (day 1: set 1 and 2, day 2: set 1 and 2, day 3: set 1 and 2). Each dataset is fitted with a decreasing exponential function (black curve) demonstrating the improvement in performance.

Figure 8

Control signal values corresponding to the reaching of stable precision grasps. Precision grasp points distribution over the Ch₁,Ch₂ plane for all subjects in G2. Circles denote grasps on the first day, triangles correspond to grasps on day 2 and day 3.