Design challenges for wearable EMG applications

Journal of Electrical Engineering, 2019

Nowadays, the technology advancements of signal processing, low-voltage low-power circuits and miniaturized circuits have enabled the design of compact, battery-powered, high performance solutions for a wide range of, particularly, biomedical applications. Novel sensors for human biomedical signals are creating new opportunities for low weight wearable devices which allow continuous monitoring together with freedom of movement of the users. This paper presents the design and implementation of a novel miniaturized low-power sensor in integrated circuit (IC) form suitable for wireless electromyogram (EMG) systems. Signal inputs (electrodes) are connected to this application-specific integrated circuit (ASIC). The ASIC consists of several consecutive parts. Signals from electrodes are fed to an instrumentation amplifier (INA) with fixed gain of 50 and filtered by two filters (a low-pass and high-pass filter), which remove useless signals and noise with frequencies below 20 Hz and above...

—Prosthetic hand control based on the acquisition and processing of surface electromyography signals (sEMG) is a well-established method that makes use of the electric potentials evoked by the physiological contraction processes of one or more muscles. Furthermore intelligent mobile medical devices are on the brink of introducing safe and highly sophisticated systems to help a broad patient community to regain a considerable amount of life quality. The major challenges which are inherent in such integrated system's design are mainly to be found in obtaining a compact system with a long mobile autonomy, capable of delivering the required signal requirements for EMG based prosthetic control with up to 32 simultaneous acquisition channels and – with an eye on a possible future exploitation as a medical device – a proper perspective on a low priced system. Therefore, according to these requirements we present a wireless, mobile platform for acquisition and communication of sEMG signals embedded into a complete mobile control system structure. This environment further includes a portable device such as a laptop providing the necessary computational power for the control and a commercially available robotic hand-prosthesis. Means of communication among those devices are based on the Bluetooth standard. We show, that the developed low cost mobile device can be used for proper prosthesis control and that the device can rely on a continuous operation for the usual daily life usage of a patient.

Biomedical Signal Processing and Control, 2015

IEEE Access

The myoelectric interfaces are being used in rehabilitation technology, assistance and as an input device. This review focuses on an insightful analysis of the data acquisition system of EMG signals from these interfaces. According to applications reported in research articles of the last five years, the properties of the sensors, the number of channels, the pre-processing of the EMG signal, as well as the software and hardware used were identified. This analysis was performed for the following applications: monitoring of muscular activation for rehabilitation, muscle activation plans, and identification of possible pathologies, exoskeletons, electric of wheelchairs, prosthetics control, myoelectric bracelets, handwriting recognition and silent speech recognition. The results presented in this review become a guide of recommendations for the myoelectric signal processing according to the application of the interface. The main developments, degrees of research and open challenges are also presented in this direction.

IEEE Transactions on Biomedical Engineering, 2006

Artificial Organs, 2011

Modern hand and wrist prostheses afford a high level of mechanical sophistication, but the ability to control them in an intuitive and repeatable manner lags. Commercially available systems using surface electromyographic (EMG) or myoelectric control can supply at best two degrees of freedom (DOF), most often sequentially controlled. This limitation is partially due to the nature of surface-recorded EMG, for which the signal contains components from multiple muscle sources. We report here on the development of an implantable myoelectric sensor using EMG sensors that can be chronically implanted into an amputee's residual muscles. Because sensing occurs at the source of muscle contraction, a single principal component of EMG is detected by each sensor, corresponding to intent to move a particular effector. This system can potentially provide independent signal sources for control of individual effectors within a limb prosthesis. The use of implanted devices supports inter-day signal repeatability. We report on efforts in preparation for human clinical trials, including animal testing, and a first-in-human proof of principle demonstration where the subject was able to intuitively and simultaneously control two DOF in a hand and wrist prosthesis.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014

The IMES1 Implantable MyoElectric Sensor device is currently in human clinical trials led by the Alfred Mann Foundation. The IMES is implanted in a residual limb and is powered wirelessly using a magnetic field. EMG signals resulting from the amputee's voluntary movement are amplified and transmitted wirelessly by the IMES to an external controller which controls movement of an external motorized prosthesis. Development of the IMES technology is on-going, producing the next-generation IMES2. Among various improvements, a new feature of the IMES2 is a low-power polling mode. In this low-power mode, the IMES2 power consumption can be dramatically reduced when the limb is inactive through the use of a polled sampling. With the onset of EMG activity, the IMES2 system can switch to the normal higher sample rate to allow the acquisition of high-fidelity EMG data for prosthesis control.

We are developing a multi-channel/multifunction prosthetic hand/arm controller system capable of receiving and processing signals from up to sixteen Implanted MyoElectric Sensors (IMES). The appeal of implanted sensors for myoelectric control is that EMG signals can be measured at their source providing relatively cross-talk free signals that can be treated as independent control sites. Therefore the number of degrees-of-freedom that can be simultaneously controlled and coordinated in an externally-powered prosthesis will be greater than with surface EMG or mechanical control sites. To explore the issue of intra-muscular signal independence and the ability to control them, human subject experiments have been performed in which intra-muscular EMGs were obtained. Choice of muscles was based on a desire to be able to independently control a two degree-of-freedom (DOF) wrist, and 3 DOF prosthetic hand. This paper provide our result so far.

Sensors, 2018

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous.

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016

EMG pattern recognition has been studied for control of prostheses and rehabilitation systems for decades. Existing research platforms for developing EMG pattern recognition algorithms are typically based on MATLAB and the collection of EMG signals is often done by expensive, non-portable data acquisition systems. The requirement of these resources usually limits the use of these platforms in the lab environments and prohibits their widespread to other fields and applications. To address this limitation, this paper presents a low-cost, easy to use, and flexible platform called MyoHMI for developing real-time human machine interfaces for myoelectric controlled applications. MyoHMI facilitates the interface with a commercial EMG-based armband Myo, which costs less than $200 and can be easily worn by the user without the need of special preparation. MyoHMI also provides a highly modular and customizable C/C++ based software engine which seamlessly integrates a variety of interfacing and signal processing modules, from data acquisition through signal processing and pattern recognition, to real-time evaluation and control. The experimental results on able-bodied human subjects for controlling two evaluation platforms in real time verified the merit of the MyoHMI platform and demonstrated the feasibility of a low-cost solution for the development of myoelectric controlled applications. I.

2008 Second International Conference on Pervasive Computing Technologies for Healthcare, 2008

In this contribution we are reporting on the concept and development of an integrated and implantable device for EMG measurement inside of human bodies. Such devices are capable of measuring EMG potentials of muscle activity during dedicated activities and of transmitting such information to the outside of the body. They will serve for extended possibilities of human machine interaction in the future. The wearer will be able to control devices and systems apart from his body by simple muscle activity. Such devices have to be within acceptable technological limits-size, weight-to become accepted. In this contribution we are demonstrating the concept and give insight into technological aspects of the developed systems and present first results on the analysis of the available data.

CONTROL INSTRUMENTATION SYSTEM CONFERENCE, CISCON-2011, 2011

Aim of this paper is to design a low power, low cost single supply wearable EMG extractor unit for EMG monitoring and developing a Myoelectric based gaming platform for rehabilitation purpose. EMG preamplifier section is designed with instrumentation amplifier AD623 located at the site of signal extraction through Biopotential electrode. Acquisition and preprocessing of multichannel EMG signal is done by ARM 7 processor. Data is transmitted to computer with c# platform for signal processing and display with biofeedback. A simple game is developed in C#.net platform which is controlled only through 2 channel EMG signal derived from two opposing muscles in forearm responsible for wrist flexion and extension. Such myoelectric interface helps in regeneration of lost emg signal in amputee and aids in rehabilitation programe, prosthetic and orthotis designs etc.

XIX IMEKO World Congress, Fundamental and …

In this paper we present the novel implementation of the intrabody communication (IBC) system that was specially designed for electromyography (EMG) measurements in kinesiology, sports medicine and rehabilitation. We propose a novel approach to the wireless EMG monitoring system design, which prolongs the battery life by minimizing the power consumption requirements for data transmission. This goal was achieved by a capacitive IBC approach and by developing special-purpose ultra-low power hardware modules, which perform tasks of digital signal modulation and demodulation at a very low-level. We investigate the optimal electrodes placement for IBC and present the results of in vivo measurements.

Limb loss is a growing problem due to the increasing number of accidents worldwide. A cybernetic prosthesis is a device which can assist individuals with hand disabilities by enabling them to have some of the hand capabilities of an able bodied individual. Extracting hand grip force and wrist angle information from forearm electromyogram (EMG) signals is useful to be used as inputs for the control of cybernetic prostheses. By establishing the relationship between forearm EMG and hand grip force/wrist angles, the prosthetic hand can be controlled in a manner that is customised to an amputee’s intent. In this research work, a myoelectric interface which consists of an electronic conditioning circuit to measure EMG signals and software to record and process the EMG signals were developed. Experimental training and testing datasets from five subjects were collected to investigate the relationship between forearm EMG, hand grip force and wrist angle simultaneously.

IEEE Transactions on Biomedical Circuits and Systems

Hand gesture recognition has recently increased its popularity as Human-Machine Interface (HMI) in the biomedical field. Indeed, it can be performed involving many different non-invasive techniques, e.g., surface ElectroMyoGraphy (sEMG) or PhotoPlethysmoGraphy (PPG). In the last few years, the interest demonstrated by both academia and industry brought to a continuous spawning of commercial and custom wearable devices, which tried to address different challenges in many application fields, from tele-rehabilitation to sign language recognition. In this work, we propose a novel 7-channel sEMG armband, which can be employed as HMI for both serious gaming control and rehabilitation support. In particular, we designed the prototype focusing on the capability of our device to compute the Average Threshold Crossing (ATC) parameter, which is evaluated by counting how many times the sEMG signal crosses a threshold during a fixed time duration (i.e., 130 ms), directly on the wearable device. Exploiting the event-driven characteristic of the ATC, our armband is able to accomplish the on-board prediction of common hand gestures requiring less power w.r.t. state of the art devices. At the end of an acquisition campaign that involved the participation of 26 people, we obtained an average classifier accuracy of 91.9% when aiming to recognize in real time 8 active hand gestures plus the idle state. Furthermore, with 2.92 mA of current absorption during active functioning and 1.34 ms prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications.