8 research outputs found
A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN
Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance
The Effect of Spinal Manipulation on the Electrophysiological and Metabolic Properties of the Tibialis Anterior Muscle
There is growing evidence showing that spinal manipulation increases muscle strength in healthy individuals as well as in people with some musculoskeletal and neurological disorders. However, the underlying mechanism by which spinal manipulation changes muscle strength is less clear. This study aimed to assess the effects of a single spinal manipulation session on the electrophysiological and metabolic properties of the tibialis anterior (TA) muscle. Maximum voluntary contractions (MVC) of the ankle dorsiflexors, high-density electromyography (HDsEMG), intramuscular EMG, and near-infrared spectroscopy (NIRS) were recorded from the TA muscle in 25 participants with low level recurring spinal dysfunction using a randomized controlled crossover design. The following outcomes: motor unit discharge rate (MUDR), strength (force at MVC), muscle conduction velocity (CV), relative changes in oxy- and deoxyhemoglobin were assessed pre and post a spinal manipulation intervention and passive movement control. Repeated measures ANOVA was used to assess within and between-group differences. Following the spinal manipulation intervention, there was a significant increase in MVC (p = 0.02; avg 18.87 ± 28.35%) and a significant increase in CV in both the isometric steady-state (10% of MVC) contractions (p < 0.01; avg 22.11 ± 11.69%) and during the isometric ramp (10% of MVC) contractions (p < 0.01; avg 4.52 ± 4.58%) compared to the control intervention. There were no other significant findings. The observed TA strength and CV increase, without changes in MUDR, suggests that the strength changes observed following spinal manipulation are, in part, due to increased recruitment of larger, higher threshold motor units. Further research needs to investigate the longer term and potential functional effects of spinal manipulation in various patients who may benefit from improved muscle function and greater motor unit recruitment
Towards Natural Control of Artificial Limbs
The use of implantable electrodes has been long thought as the solution for a more natural control of artificial limbs, as these offer access to long-term stable and physiologically appropriate sources of control, as well as the possibility to elicit appropriate sensory feedback via neurostimulation. Although these ideas have been explored since the 1960’s, the lack of a long-term stable human-machine interface has prevented the utilization of even the simplest implanted electrodes in clinically viable limb prostheses.In this thesis, a novel human-machine interface for bidirectional communication between implanted electrodes and the artificial limb was developed and clinically implemented. The long-term stability was achieved via osseointegration, which has been shown to provide stable skeletal attachment. By enhancing this technology as a communication gateway, the longest clinical implementation of prosthetic control sourced by implanted electrodes has been achieved, as well as the first in modern times. The first recipient has used it uninterruptedly in daily and professional activities for over one year. Prosthetic control was found to improve in resolution while requiring less muscular effort, as well as to be resilient to motion artifacts, limb position, and environmental conditions.In order to support this work, the literature was reviewed in search of reliable and safe neuromuscular electrodes that could be immediately used in humans. Additional work was conducted to improve the signal-to-noise ratio and increase the amount of information retrievable from extraneural recordings. Different signal processing and pattern recognition algorithms were investigated and further developed towards real-time and simultaneous prediction of limb movements. These algorithms were used to demonstrate that higher functionality could be restored by intuitive control of distal joints, and that such control remains viable over time when using epimysial electrodes. Lastly, the long-term viability of direct nerve stimulation to produce intuitive sensory feedback was also demonstrated.The possibility to permanently and reliably access implanted electrodes, thus making them viable for prosthetic control, is potentially the main contribution of this work. Furthermore, the opportunity to chronically record and stimulate the neuromuscular system offers new venues for the prediction of complex limb motions and increased understanding of somatosensory perception. Therefore, the technology developed here, combining stable attachment with permanent and reliable human-machine communication, is considered by the author as a critical step towards more functional artificial limbs
Pattern recognition-based real-time myoelectric control for anthropomorphic robotic systems : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatū, New Zealand
All copyrighted Figures have been removed but may be accessed via their source cited in their respective captions.Advanced human-computer interaction (HCI) or human-machine interaction (HMI) aims to help
humans interact with computers smartly. Biosignal-based technology is one of the most promising
approaches in developing intelligent HCI systems. As a means of convenient and non-invasive
biosignal-based intelligent control, myoelectric control identifies human movement intentions from
electromyogram (EMG) signals recorded on muscles to realise intelligent control of robotic systems.
Although the history of myoelectric control research has been more than half a century, commercial
myoelectric-controlled devices are still mostly based on those early threshold-based methods. The
emerging pattern recognition-based myoelectric control has remained an active research topic in
laboratories because of insufficient reliability and robustness. This research focuses on pattern
recognition-based myoelectric control. Up to now, most of effort in pattern recognition-based
myoelectric control research has been invested in improving EMG pattern classification accuracy.
However, high classification accuracy cannot directly lead to high controllability and usability for
EMG-driven systems. This suggests that a complete system that is composed of relevant modules,
including EMG acquisition, pattern recognition-based gesture discrimination, output equipment and its
controller, is desirable and helpful as a developing and validating platform that is able to closely emulate
real-world situations to promote research in myoelectric control.
This research aims at investigating feasible and effective EMG signal processing and pattern
recognition methods to extract useful information contained in EMG signals to establish an intelligent,
compact and economical biosignal-based robotic control system. The research work includes in-depth
study on existing pattern recognition-based methodologies, investigation on effective EMG signal
capturing and data processing, EMG-based control system development, and anthropomorphic robotic
hand design. The contributions of this research are mainly in following three aspects:
Developed precision electronic surface EMG (sEMG) acquisition methods that are able to
collect high quality sEMG signals. The first method was designed in a single-ended signalling
manner by using monolithic instrumentation amplifiers to determine and evaluate the analog
sEMG signal processing chain architecture and circuit parameters. This method was then
evolved into a fully differential analog sEMG detection and collection method that uses
common commercial electronic components to implement all analog sEMG amplification and
filtering stages in a fully differential way. The proposed fully differential sEMG detection and collection method is capable of offering a higher signal-to-noise ratio in noisy environments
than the single-ended method by making full use of inherent common-mode noise rejection
capability of balanced signalling. To the best of my knowledge, the literature study has not
found similar methods that implement the entire analog sEMG amplification and filtering chain
in a fully differential way by using common commercial electronic components.
Investigated and developed a reliable EMG pattern recognition-based real-time gesture
discrimination approach. Necessary functional modules for real-time gesture discrimination
were identified and implemented using appropriate algorithms. Special attention was paid to
the investigation and comparison of representative features and classifiers for improving
accuracy and robustness. A novel EMG feature set was proposed to improve the performance
of EMG pattern recognition.
Designed an anthropomorphic robotic hand construction methodology for myoelectric control
validation on a physical platform similar to in real-world situations. The natural anatomical
structure of the human hand was imitated to kinematically model the robotic hand. The
proposed robotic hand is a highly underactuated mechanism, featuring 14 degrees of freedom
and three degrees of actuation.
This research carried out an in-depth investigation into EMG data acquisition and EMG signal pattern
recognition. A series of experiments were conducted in EMG signal processing and system
development. The final myoelectric-controlled robotic hand system and the system testing confirmed
the effectiveness of the proposed methods for surface EMG acquisition and human hand gesture
discrimination. To verify and demonstrate the proposed myoelectric control system, real-time tests were
conducted onto the anthropomorphic prototype robotic hand. Currently, the system is able to identify
five patterns in real time, including hand open, hand close, wrist flexion, wrist extension and the rest
state. With more motion patterns added in, this system has the potential to identify more hand
movements. The research has generated a few journal and international conference publications