Myoelectric Control: an alternative to Mirror Therapy

Evidence suggests that fifty to eighty percent (50-80%) of amputees conserve sensation in their missing limb after removal due to the presence of associated nerve endings. Most importantly, a large percentage of amputees experience episodic pain in the missing limb. This physiological phenomenon called phantom limb pain (PLP) has shown resistance to pharmaceutical treatments, but can be treated through mirror therapy. However, mirror therapy only yields temporary results and does not apply to bilateral amputees. Overcoming these challenges are the objectives of the present study. Using a surface electromyographic signal classification approach, this investigation intends to simulate the control of a missing limb within an immersive virtual environment. We predict that replacing mirror therapy with a more immersive “virtual therapy” can serve as a prolonged psychological solution to phantom limb pain.

A stretchable and adhesive ionic conductor based on polyacrylic acid and deep eutectic solvents

Hydrogels are a widely used ionic conductor in on-skin electronic and iontronic devices. However, hydrogels dehydrate in the open air and freeze at low temperatures, limiting their real applications when they are attached on skin or exposed to low temperatures. Polymer-ionic liquid gels can overcome these two obstacles, but synthetic ionic liquids are expensive and toxic. In this work, we present an ionic conductor based on polyacrylic acid (PAAc) and deep eutectic solvents (DESs) that well addresses the aforementioned challenges. We polymerize acrylic acid in DESs to get the PAAc–DES gel, which exhibits excellent stretchability (> 1000%), high electrical conductivity (1.26 mS cm−1), high adhesion to the skin (~ 100 N m−1), as well as good anti-drying and anti-freezing properties. We also demonstrate that the PAAc-DES gel can be used as an on-skin electrode to record the surface electromyographic signal with high signal quality, or as a transparent stretchable electrode in iontronic devices that can work at –20 °C. We believe that the PAAc–DES gels are an ideal candidate as epidermal electrodes or transparent stretchable electrodes.

Surface Electromyographic Signal Acquisition System for Real Time Monitoring of Upper Limbs Muscles

The main goal of this paper is to present the design of a surface electromyography acquisition, processing and amplification system with low power consumption. Based on a micro-controller and a Bluetooth module, it must send the data to a cell phone in real time. The main topology is based on an operational amplifier and passive components in order to produce filters and an instrumentation amplifier applied to Electromyography (EMG). This paper also shows the equations used during design and describes each step of development, from simulations and testing to acquired data and microcontroller programming. In order to produce a low-cost circuit that can be later used as an acquisition tool for portable mechanisms and prosthesis, the design of the main circuit considers the lowest number of components while it does not compromise efficiency.

A Novel Surface Electromyographic Signal-Based Hand Gesture Prediction Using a Recurrent Neural Network

Surface electromyographic signal (sEMG) is a kind of bioelectrical signal, which records the data of muscle activity intensity. Most sEMG-based hand gesture recognition, which uses machine learning as the classifier, depends on feature extraction of sEMG data. Recently, a deep leaning-based approach such as recurrent neural network (RNN) has provided a choice to automatically learn features from raw data. This paper presents a novel hand gesture prediction method by using an RNN model to learn from raw sEMG data and predict gestures. The sEMG signals of 21 short-term hand gestures of 13 subjects were recorded with a Myo armband, which is a non-intrusive, low cost, commercial portable device. At the start of the gesture, the trained model outputs an instantaneous prediction for the sEMG data. Experimental results showed that the more time steps of data that were known, the higher instantaneous prediction accuracy the proposed model gave. The predicted accuracy reached about 89.6% when the data of 40-time steps (200 ms) were used to predict hand gesture. This means that the gesture could be predicted with a delay of 200 ms after the hand starts to perform the gesture, instead of waiting for the end of the gesture.

Evaluation of the training objectives with surface electromyography

In this work, the multifractal analysis of the kinesiological surface electromyographic signal is proposed. The goal was to investigate the level of neuromuscular activation during complex movements on the laparoscopic trainer. The basic issue of this work concerns the changes observed in the signal obtained from the complete beginner in the field of using laparoscopic tools and the same person subjected to the series of training. To quantify the complexity of the kinesiological surface electromyography, the nonlinear analysis technique, namely, the multifractal detrended fluctuation analysis, was adopted. The analysis was based on the parameters describing the multifractal spectrum – the Hurst exponent – and the spectrum width. The statistically significant differences for a selected group of muscles at the different states (before and after training) are presented. In addition, as the base case, the relaxation state was considered and compared with the working states.