Online Muscle Activation Onset Detection Using Likelihood of Conditional Heteroskedasticity of Electromyography Signals

Surface electromyography (sEMG) signals are crucial in developing human-machine interfaces, as they contain rich information about human neuromuscular activities. &lt;italic&gt;Objective:&lt;/italic&gt; The real-time, accurate detection of muscle activation onset (MAO) is significant for EMG-triggered control strategies in embedded applications like prostheses and exoskeletons. &lt;italic&gt;Methods:&lt;/italic&gt; This paper investigates sEMG signals using the generalized autoregressive conditional heteroskedasticity (GARCH) model, focusing on variance. A novel feature, the likelihood of conditional heteroskedasticity (LCH) extracted from the maximum likelihood estimation of GARCH parameters, is proposed. This feature effectively distinguishes signal from noise based on heteroskedasticity, allowing for the detection of MAO through the LCH feature and a basic threshold classifier. For online calculation, the model parameter estimation is simplified, enabling direct calculation of the LCH value using fixed parameters. &lt;italic&gt;Results:&lt;/italic&gt; The proposed method was validated on two open-source datasets and demonstrated superior performance over existing methods. The mean absolute error of onset detection, compared with visual detection results, is approximately 65 ms under online conditions, showcasing high accuracy, universality, and noise insensitivity. &lt;italic&gt;Conclusion:&lt;/italic&gt; The results indicate that the proposed method using the LCH feature from the GARCH model is highly effective for real-time detection of muscle activation onset in sEMG signals. &lt;italic&gt;Significance:&lt;/italic&gt; This novel approach shows great potential and possibility for real-world applications, reflecting its superior performance in accuracy, universality, and insensitivity to noise.</p

On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Data and Sensor Fusion Using FMG, sEMG and IMU Sensors for Upper Limb Prosthesis Control

Whether someone is born with a missing limb or an amputation occurs later in life, living with this disability can be extremely challenging. The robotic prosthetic devices available today are capable of giving users more functionality, but the methods available to control these prostheses restrict their use to simple actions, and are part of the reason why users often reject prosthetic technologies. Using multiple myography modalities has been a promising approach to address these control limitations; however, only two myography modalities have been rigorously tested so far, and while the results have shown improvements, they have not been robust enough for out-of-lab use. In this work, a novel multi-modal device that allows data to be collected from three myography modalities was created. Force myography (FMG), surface electromyography (sEMG), and inertial measurement unit (IMU) sensors were integrated into a wearable armband and used to collect signal data while subjects performed gestures important for the activities of daily living. An established machine learning algorithm was used to decipher the signals to predict the user\u27s intent/gesture being held, which could be used to control a prosthetic device. Using all three modalities provided statistically-significant improvements over most other modality combinations, as it provided the most accurate and consistent classification results. This work provides justification for using three sensing modalities and future work is suggested to explore this modality combination to decipher more complex actions and tasks with more sophisticated pattern recognition algorithms

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

A Brain-computer Interface Architecture Based On Motor Mental Tasks And Music Imagery

This present research proposes a Brain-Computer Interface (BCI) architecture adapted to motor mental tasks and music imagery. For that purpose the statistical properties of the electroencephalographic signal (EEG) were studied, such as its probability distribution function, stationarity, correlation and signal-to-noise ratio (SNR), in order to obtain a minimal empirical and well-founded parameter system for online classification. Stationarity tests were used to estimate the length of the time windows and a minimum length of 1.28 s was obtained. Four algorithms for artifact reduction were tested: threshold analysis, EEG filtering and two Independent Component Analysis (ICA) algorithms. This analysis concluded that the algorithm fastICA is suitable for online artifact removal. The feature extraction used the Power Spectral Density (PSD) and three methods were tested for automatic selection of features in order to have a training step independent of the mental task paradigm, with the best performance obtained with the Kullback-Leibler symmetric divergence method. For the classification, the Linear Discriminant Analysis (LDA) was used and a step of reclassification is suggested. A study of four motor mental tasks and a non-motor related mental task is performed by comparing their periodograms, Event-Related desynchronization/synchronization (ERD/ERS) and SNR. The mental tasks are the imagination of either movement of right and left hands, both feet, rotation of a cube and sound imagery. The EEG SNR was estimated by a comparison with the correlation between the ongoing average and the final ERD/ERS curve, in which we concluded that the mental task of sound imagery would need approximately five times more epochs than the motor-related mental tasks. The ERD/ERS could be measured even for frequencies near 100 Hz, but in absolute amplitudes, the energy variation at 100 Hz was one thousand times smaller than for 10 Hz, which implies that there is a small probability of online detection for BCI applications in high frequency. Thus, most of the usable information for online processing and BCIs corresponds to the &#945;/µ band (low frequency). Finally, the ERD/ERS scalp maps show that the main difference between the sound imagery task and the motor-related mentaltasks is the absence of ERD at the µ band, in the central electrodes, and the presence of ERD at the &#945;band in the temporal and lateral-frontal electrodes, which correspond tothe auditory cortex, the Wernickes area and the Brocas area