Neural Interfaces for Control of Upper Limb Prostheses: The State of the Art and Future Possibilities

Abstract

Current treatment of upper limb amputation restores some degree of functional ability, but this ability falls far below the standard set by the natural arm. Although acceptance rates can be high when patients are highly motivated and receive proper training and care, current prostheses often fail to meet the daily needs of amputees and frequently are abandoned. Recent advancements in science and technology have led to promising methods of accessing neural information for communication or control. Researchers have explored invasive and noninvasive methods of connecting with muscles, nerves, or the brain to provide increased functionality for patients experiencing disease or injury, including amputation. These techniques offer hope of more natural and intuitive prosthesis control, and therefore increased quality of life for amputees. In this review, we discuss the current state of the art of neural interfaces, particularly those that may find application within the prosthetics field.

Recent Trends in EMG-Based Control Methods for Assistive Robots

“…Further, EMG based control methods of upper-limb exoskeleton robots and prostheses are compared considering their country of origin, input signals, structure of the controller and special features. [8,13]. It was found that all methods of EMG processing belong to one of three main categories: time domain, frequency domain and time-frequency domain [5].…”

“…Therefore, in the integration of the human with a robot, the selection of a proper control input signal to reflect the correct motion intention would be very important. So far research is being carried out considering different biological signals such as Electromyography (EMG), Mechnanomyogram (MMG), Electroencephalography (EEG) Electrooculography (EOG) and Electrocorticogram (EcoG) [13,14,15] as the main input signal to the robot controller. Among them, the EMG signal, which is the measurement of the electrical activity of muscles at rest and during contraction, has obtained promising results in the case of controlling robotic prostheses and orthoses by correctly interpreting the human motion intention [16,17].…”

3D-printing soft sEMG sensing structures

“…EMG gives useful information about the activity of individual muscles and is therefore widely used for the control of hand and arm assistive devices and prosthesis [1]- [6]. An example of EMG control is the Pisa/IIT SoftHand, an EMG driven soft robotic hand [7].…”

Cortical Alpha Oscillation During 3 kHz Steady-State Sinusoidal Electric Current Stimulation

“…Electric pulse stimulus has also been extensively used with this purpose, [9] and although it has been reported that it seems to evoke tactile perception artificially in a given intensity [9], it is known that it provides recruitment of thick fibers firstly (Aβ) and thin fibers in a second moment (Aδ and C), especially with increasing stimulus intensities [10,11]. Selectively activating Aβ-fibers could be rather useful for disease evaluation -i.e., diagnosis and progression of conditions that involve sensorial and tactile loss, such as stroke, diabetes mellitus, phantom limb pain and leprosy [12][13][14][15][16].…”

“…During the past two decades, studies have shown that selectively activating Aβ-fibers ought to be attainable by using sinusoidal electrical stimulation of high frequencies, such as 2 kHz and 3 kHz, at the sensory threshold (ST) level as a psychophysical evaluation [13][14][15][16][17]. This is usually assessed through subjective questionnaire answers, where participants have to decide on words from a given list, that describe best the sensations felt after stimulation.…”