Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface

An Assessment of Single-Channel EMG Sensing for Gestural Input

Wearable devices of all kinds are becoming increasingly popular. One problem that plagues wearable devices, however, is how to interact with them. In this paper we construct a prototype electromyography (EMG) sensing device that captures a single channel of EMG sensor data corresponding to user gestures. We also implement a machine learning pipeline to recognize gestural input received via our prototype sensing device. Our goal is to assess the feasibility of using a BITalino EMG sensor to recognize gestural input on a mobile health (mHealth) wearable device known as Amulet. We conduct three experiments in which we use the EMG sensor to collect gestural input data from (1) the wrist, (2) the forearm, and (3) the bicep. Our results show that a single channel EMG sensor located near the wrist may be a viable approach to reliably recognizing simple gestures without mistaking them for common daily activities such as drinking from a cup, walking, or talking while moving your arms

A virtual reality input device for sports-related rehabilitation

Abstract. This work entails the hardware design, manufacturing and implementation of a VR controller device tailored for people with specific sports-related injuries. The target case of this thesis is the tennis elbow injury, where the designed controller helps them interface easily to the VR environment that is designed for their therapy. The sensors used are carefully selected in order to adequately capture the therapy exercise movements related to this kind of injury. For example, the use FSRs (Force Sensitive Resistors) that are put on the surface of a test object helps to detect a grasp during the exercise. The hardware design and manufacturing was done for a VR controller device that would give the desired performance, using Arduino IDE for its software development. In addition to this, the design of the VR environment allowed for an immersive VR experience for the rehabilitation. An experiment was carried out with eight participants, where they were asked to perform two exercises that involve grasping the test object. A series of questions were asked to them as part of the experimental evaluation. The results showed positive indications about the participants’ experience

The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures