Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art

Testing the Validity and Reliability of Electromyography Acquisition Capabilities of a Wearable EMG Device, Sense3, by Strive

There is a growing demand in the sports world for wearable technology, particularly those with electromyography acquisition capabilities. Electromyography (EMG) is technique for measuring the electrical activity that occurs during muscle contraction and relaxation. Basic practical applications of EMG use in sports include, but are not limited to: measuring activation timing of a muscle, measuring levels of activation, and detecting fatigue. The sports performance company Strive has designed an EMG wearable, called Sense3, that targets the following muscles of the lower limb: Quadriceps, Hamstrings, and Glutes. Sense3 must pass reliability assays to determine the validity of the EMG system in order for Sense3 to be accessible as a commercialized product. This study was designed to compare the EMG acquisition performance of Sense3 to the performance of a traditional EMG acquisition device, MA-300, during slow and controlled movements, simulated by use of a dynamometer, and during dynamic movements. Statistics from the reliability assays showed Sense3 to be reliable in the Rectus Femoris and Biceps Femoris during dynamometer trials. Sense3 was unable to consistently record useable EMG signals for analysis during dynamic exercise trials. The ability to record EMG signals during dynamic movement was the main determinant for validity of Sense3’s EMG acquisition system. The results suggest that Sense3 is not a valid EMG acquisition system for sports-based, dynamic use

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Systematic review of textile-based electrodes for long-term and continuous surface electromyography recording

This systematic review concerns the use of smart textiles enabled applications based on myoelectric activity. Electromyography (EMG) is the technique for recording and evaluating electric signals related to muscle activity (myoelectric). EMG is a well-established technique that provides a wealth of information for clinical diagnosis, monitoring, and treatment. Introducing sensor systems that allow for ubiquitous monitoring of health conditions using textile integrated solutions not only opens possibilities for ambulatory, long-term, and continuous health monitoring outside the hospital, but also for autonomous self-administration. Textile-based electrodes have demonstrated potential as a fully operational alternative to \u27standard\u27 Ag/AgCl electrodes for recording surface electromyography (sEMG) signals. As a substitute for Ag/AgCl electrodes fastened to the skin by taping or pre-gluing adhesive, textile-based electrodes have the advantages of being soft, flexible, and air permeable; thus, they have advantages in medicine and health monitoring, especially when self-administration, real-time, and long-term monitoring is required. Such advances have been achieved through various smart textile techniques; for instance, adding functions in textiles, including fibers, yarns, and fabrics, and various methods for incorporating functionality into textiles, such as knitting, weaving, embroidery, and coating. In this work, we reviewed articles from a textile perspective to provide an overview of sEMG applications enabled by smart textile strategies. The overview is based on a literature evaluation of 41 articles published in both peer-reviewed journals and conference proceedings focusing on electrode materials, fabrication methods, construction, and sEMG applications. We introduce four textile integration levels to further describe the various textile electrode sEMG applications reported in the reviewed literature. We conclude with suggestions for future work along with recommendations for the reporting of essential benchmarking information in current and future textile electrode applications

Designing Embroidered Electrodes for Wearable Surface Electromyography

This work was supported by the UK Crafts Council as part of the Parallel Practices project, by the Seventh Framework Programme of the European Commission under grant agreement 287728 in the framework of EU project STIFF-FLOP and by the Horizon 2020 Research and Innovation Programme under grant agreement 637095 in the framework of EU project FourByThree

Validation of polymer-based screen-printed textile electrodes for surface EMG detection

In recent years, the variety of textile electrodes developed for electrophysiological signal detection has increased rapidly. Among the applications that could benefit from this advancement, those based on surface electromyography (sEMG) are particularly relevant in rehabilitation, training and muscle function assessment. In this work, we validate the performance of polymer-based screen-printed textile electrodes for sEMG signal detection. We obtained these electrodes by depositing poly-3,4-ethylenedioxythiophene doped with poly(styrene sulfonate) (PEDOT:PSS) onto cotton fabric, and then selectively changing the physical properties of the textile substrate. The manufacturing costs are low and this process meets the requirements of textile-industry production lines. The validation of these electrodes was based on their functional and electrical characteristics, assessed for two different electrode sizes and three skin-interface conditions (dry, solid hydrogel or saline solution), and compared to those of conventional disposable gelled electrodes. Results show high similarity in terms of noise amplitude and electrode-skin impedance between the conventional and textile electrodes with the addition of solid hydrogel or saline solution. Furthermore, we compared the shape of the electrically-induced sEMG, as detected by conventional and textile electrodes from tibialis anterior. The comparison yielded an R2 value higher than 97% for all measurement conditions. Preliminary tests in dynamic conditions (walking) revealed the exploitability of the proposed electrode technology with saline application for the monitoring of sEMG for up to 35 minutes of activity. These results suggest that the proposed screen-printed textile electrodes may be an effective alternative to the conventional gelled electrodes for sEMG acquisition, thereby providing new opportunities in clinical and wellness fields

Hand Gestures Recognition for Human-Machine Interfaces: A Low-Power Bio-Inspired Armband

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 2.92mA of current absorption during active functioning and 1.34mA prediction latency, this prototype confirmed our expectations and can be an appealing solution for long-term (up to 60 h) medical and consumer applications

Design and Characterization of a Textile Electrode System for the Detection of High-Density sEMG

Muscle activity monitoring in dynamic conditions is a crucial need in different scenarios, ranging from sport to rehabilitation science and applied physiology. The acquisition of surface electromyographic (sEMG) signals by means of grids of electrodes (High-Density sEMG, HD-sEMG) allows obtaining relevant information on muscle function and recruitment strategies. During dynamic conditions, this possibility demands both a wearable and miniaturized acquisition system and a system of electrodes easy to wear, assuring a stable electrode-skin interface. While recent advancements have been made on the former issue, detection systems specifically designed for dynamic conditions are at best incipient. The aim of this work is to design, characterize, and test a wearable, HD-sEMG detection system based on textile technology. A 32-electrodes, 15 mm inter-electrode distance textile grid was designed and prototyped. The electrical properties of the material constituting the detection system and of the electrode-skin interface were characterized. The quality of sEMG signals was assessed in both static and dynamic contractions. The performance of the textile detection system was comparable to that of conventional systems in terms of stability of the traces, properties of the electrode-skin interface and quality of the collected sEMG signals during quasi-isometric and highly dynamic tasks