Dexterous control of a prosthetic hand using fine-wire intramuscular electrodes in targeted extrinsic muscles

Abstract

Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless human-machine interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees of freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into 12 postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.

Fig. 1

Experimental setup. (a) Block diagram of the myoelectric controller. FPL: flexor pollicis longus. FDP₁, FDP₂: first and second compartment of the flexor digitorum profundis. APL: abductor pollicis longus. ADC: analog to digital converter. MAV: mean absolute value. Pt: posture control command sent to the robotic hand. (b) Subject wearing an orthopedic splint on the experimental hand sat in front of a computer screen and the robotic hand. Computer screen presented desired posture cues while the hand was controlled in real-time. (c) Pictures of the 12 target postures P1..P12 used in experiment 1. Last two fingers of the hand are not shown for clarity (since these were not under direct control). It is worth noting that the target position of the fingers in postures P5 and P10 are similar but the two postures required activations with different timing in order to position the thumb properly (under the index and middle in posture P5; over the index and middle in P10). (d), (e) Representation of the sinusoids that subjects tracked in experiment 2A–B, respectively.

Fig. 2

Posture matching experiment representative trial. Recorded EMG signals (in black—left Y axis) and actual position of the DoF in the robotic hand (in blue—right Y axis) for the four DoFs, in a representative posture (i.e., posture P3—a palmar grasp). Superimposed on the position trajectories is the desired target position (continuous horizontal line) within the ±15% envelope (dotted horizontal lines). Gray time window (the same for all four graphs) denotes when the four DoFs were positioned within the ±15% envelope; thus time metric Tc is by definition at the end of such a gray window.

Fig. 3

Posture matching experiment outcomes. Whiskers denote the standard error of the mean. (a) Performance metrics achieved by subjects S3 and S4. In the bottom panel, bars refer to the minimum posture error (MPE) whereas circles refer to the End-of-trial mean Posture Error (EPE) and were computed only for unsuccessful trials. (b) Completion rate (CR) and completion time (T_c) as a function of the number of controlled degrees of freedom.

Fig. 4

Sinusoid tracking experiment outcomes. (a) EMG signals and relative controlled DoF positions for a representative sinusoid. (b) Differences in performance metrics between subjects S1 and S2 and subjects S3 and S4. Asterisks denote statistically significant differences as computed by Wilcoxon rank sum tests. (c) Confusion matrices of the relative mean activity (RMA) and relative variance activity (RVA) from subjects S1 and S2 (experiment 2B). (d) Confusion matrices of the RMA and RVA from subjects S3 and S4 (experiment 2A).