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.

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

“…TMR has shown great success for high-level upper-limb amputees [9]. Studies have successfully simulated IMES in human subjects using fine-wire intramuscular electrodes [11], and the technique has recently been implemented in humans for the first time [12].…”

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

“…This procedure limits the decoding quality of the neural interface because the global EMG is a spatio-temporal summation of action potentials that creates correlations amongst multiple channels and therefore determines an ill posed inverse problem for decoding 20 . Similarly, control methods based on more selective intramuscular EMG recordings have been so far based on global EMG analysis without decoding the contributions of individual motor neurons 21,22 or on a very small number of decoded motor neurons (e.g., two experimentally decoded motor neurons from intramuscular EMG signals in 23 ). Decoupling the neural information contained in the EMG signals, which exactly correspond to the timings of discharge of the efferent nerve fibers, from the shapes of the muscle fiber action potentials would determine a direct interface with the spinal motor neurons.…”

Humans Can Integrate Augmented Reality Feedback in Their Sensorimotor Control of a Robotic Hand

“…Such feedback could include multiple variables to provide comprehensive information about the state of the prosthesis and/or highly dynamic signals such as feedback on EMG to allow for predictive control, as demonstrated in a recent study [36]. This is an advantage over other feedback paradigms particularly in the case of multiarticulated prostheses with independently controllable degrees of freedom [37] that could require more sophisticated feedback systems. Additionally, when considering a greater number of variables or higher bandwidth of the transmitted signals, the advantage of AR feedback over traditional solutions is twofold: first, it is not influenced by the same drawbacks of other feedback paradigms, such as increased power consumption, bulkiness, and weight (e.g., an array of vibrators would be needed in order to code the additional information, while with AR feedback the visual output can just be recoded); secondly, because of the larger bandwidth of the communication channel, the effectiveness of visual feedback increases with task complexity [21].…”