Electromyogram Synergy Control of a Dexterous Artificial Hand to Unscrew and Screw Objects

Due to their limited dexterity, it is currently not possible to use a commercially available prosthetic hand to unscrew or screw objects without using elbow and shoulder movements. For these tasks, prosthetic hands function like a wrench, which is unnatural and limits their use in tight working environments. Results from timed rotational tasks with human subjects demonstrate the clinical need for increased dexterity of prosthetic hands, and a clinically viable solution to this problem is presented for an anthropomorphic artificial hand

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human-Computer Interface

http://doi.org/10.4018/978-1-4666-6090-8 ISBN 13 : 9781466660908 EISBN13: 9781466660915International audienceMuscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition

Restoring Fine Motor Skills through Neural Interface Technology.

Loss of motor function in the upper-limb, whether through paralysis or through loss of the limb itself, is a profound disability which affects a large population worldwide. Lifelike, fully-articulated prosthetic hands exist and are commercially available; however, there is currently no satisfactory method of controlling all of the available degrees of freedom. In order to generate better control signals for this technology, and help restore normal movement, it is necessary to interface directly with the nervous system. This thesis is intended to address several of the limitations of current neural interfaces and enable the long-term extraction of control signals for fine movements of the hand and fingers. The first study addresses the problems of low signal amplitudes and short implant lifetimes in peripheral nerve interfaces. In two rhesus macaques, we demonstrate the successful implantation of regenerative peripheral nerve interfaces (RPNI), which allowed us to record high amplitude, functionally-selective signals from peripheral nerves up to 20 months post-implantation. These signals could be accurately decoded into intended movement, and used to enable monkeys to control a virtual hand prosthesis. The second study presents a novel experimental paradigm for intracortical neural interfaces, which enables detailed investigation of fine motor information contained in primary motor cortex. We used this paradigm to demonstrate accurate decoding of continuous fingertip position and enable a monkey to control a virtual hand in closed-loop. This is the first demonstration of volitional control of fine motor skill enabled by a cortical neural interface. The final study presents the design and testing of a wireless implantable neural recording system. By extracting signal power in a single, configurable frequency band onboard the device, this system achieves low power consumption while maintaining decode performance, and is applicable to cortical, peripheral, and myoelectric signals. Taken together, these results represent a significant step towards clinical reality for neural interfaces, and towards restoration of full and dexterous movement for people with severe disabilities.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120648/1/irwinz_1.pd

Online tracking of the phase difference between neural drives to antagonist muscle pairs in essential tremor patients

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses

Causes of Performance Degradation in Non-invasive Electromyographic Pattern Recognition in Upper Limb Prostheses

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems

Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

Cortical Excitability Changes Emerge in M1 Following Training With a Novel Bimanual Coordination Pattern

Conceptually, motor skill memory has been divided into two distinct forms, which are explicit and implicit memory representations. These two memory components have distinct neural processing pathways. Extensive studies focusing on discrete and serial reaction time tasks (SRTT) have been done to explore these processing pathways to establish a link between memory consolidation processes and cortical excitability changes in motor cortex (M1) after training. Research has revealed distinct cortical excitability changes in M1 that differentiate a SRTT as either implicit or explicit. In the area of motor skill/learning, rhythmic bimanual coordination tasks are often treated as different from SRTT for a variety of reasons. The primary goal of this study was to determine if cortical excitability changes in M1 following training with a 90° bimanual coordination pattern would be more like changes observed after training with an SRTT in an implicit or explicit context. To accomplish the goal, transcranial magnetic stimulation (TMS) was used to probe M1 excitability before and after training. A secondary goal of this study was to examine whether or not training altered participants’ ability to perceptually discriminate aspects of the trained coordination pattern. A feature of explicit representation is the ability to recall the sequence after training, a feature not characteristic of an implicit representation. A recognition test introduced after a delayed-retention test of the trained 90° pattern was used to determine if training and delay interval interacted to establish changes in perceptual discrimination ability. The bimanual task required participants to produce the 90˚ relative phase pattern with finger abduction/adduction motions. Training was facilitated by using a Lissajous plot to provide concurrent feedback. Before training and at 6 and 21 minutes after training motor evoked potentials (MEPs) were measured from the first dorsal interosseous (FDI) muscle. Participants had either a 30 minute or 6-hour delay after training before performing a set of retention test trials of the 90° bimanual pattern and then performing a recognition test of the finger motions used to produce the 90° pattern. At the end of training participants produced the 90˚ phase pattern with smaller error and variability compared to the beginning of training, and maintained the skill level gained at the end of practice until the delayed retention test. Cortical excitability increased above baseline at the 6 min and 21 min TMS probes after training, consistent with the pattern observed following training with an implicit SRTT. Participants were able to perceptually discriminate finger motions of the trained 90° pattern during the recognition test. The results suggest that participants’ developed an explicit representation of the bimanual 90˚ pattern. However, the ability to both produce and perceptually discriminate coordination patterns based on relative phase also suggest that relative phase as an order parameter links perception to action and thereby constrains and facilitates both action and perception processes in a similar manner