Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Isometric Finger Pose Recognition with Sparse Channel SpatioTemporal EMG Imaging

© 2018 IEEE. High fidelity myoelectric control of prostheses and orthoses isparamount to restoring lost function to amputees and neuro-muscular disease sufferers. In this study we prove that patio-temporal imaging can be used to allow convolutional neural networks to classify sparse channel EMG samples from a consumer-grade device with over 94% accuracy. 10,572 images are generated from 960 samples of simple and complex isometric finger poses recorded from 4 fully intact subjects. Real-time classification of 12 poses is achieved with a 250ms continuous overlapping window

Current state of digital signal processing in myoelectric interfaces and related applications

This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.Peer reviewe

Control of multifunctional prosthetic hands by processing the electromyographic signal

The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Implementation of a neural network-based electromyographic control system for a printed robotic hand

3D printing has revolutionized the manufacturing process reducing costs and time, but only when combined with robotics and electronics, this structures could develop their full potential. In order to improve the available printable hand designs, a control system based on electromyographic (EMG) signals has been implemented, so that different movement patterns can be recognized and replicated in the bionic hand in real time. This control system has been developed in Matlab/ Simulink comprising EMG signal acquisition, feature extraction, dimensionality reduction and pattern recognition through a trained neural-network. Pattern recognition depends on the features used, their dimensions and the time spent in signal processing. Finding balance between this execution time and the input features of the neural network is a crucial step for an optimal classification.Ingeniería Biomédic

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

Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement

Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge of the real-time detection of human grasp intent is the identification of dynamic EMG from hand movements. Previous studies mainly implemented steady-state EMG classification with a small number of grasp patterns on dynamic situations, which are insufficient to generate differentiated control regarding the muscular activity variation in practice. In order to better detect dynamic movements, more EMG variability could be integrated into the model. However, only limited research were concentrated on such detection of dynamic grasp motions, and most existing assessments on non-static EMG classification either require supervised ground-truth timestamps of the movement status, or only contain limited kinematic variations. In this study, we propose a framework for classifying dynamic EMG signals into gestures, and examine the impact of different movement phases, using an unsupervised method to segment and label the action transitions. We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on common sequences of the grasp movements. The classifier for identifying the gesture label was constructed afterwards based on the dynamic EMG signal, with no supervised annotation of kinematic movements required. Finally, we evaluated the performances of several training strategies using EMG data from different movement phases, and explored the information revealed from each phase. All experiments were evaluated in a real-time style with the performance transitions over time presented.Comment: arXiv admin note: text overlap with arXiv:2104.0389