Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

Device wearability and operating time are trending topics in recent state-of-art works on surface ElectroMyoGraphic (sEMG) muscle monitoring. No optimal trade-off, able to concurrently address several problems of the acquisition system like robustness, miniaturization, versatility, and power efficiency, has yet been found. In this tutorial we present a solution to most of these issues, embedding in a single device both an sEMG acquisition channel, with our custom event-driven hardware feature extraction technique (named Average Threshold Crossing), and a digital part, which includes a microcontroller unit, for (optionally) sEMG sampling and processing, and a Bluetooth communication, for wireless data transmission. The knowledge acquired by the research group brought to an accurate selection of each single component, resulting in a very efficient prototype, with a comfortable final size (57.8mm x 25.2mm x 22.1mm) and a consistent signal-to-noise ratio of the acquired sEMG (higher than 15 dB). Furthermore, a precise design of the firmware has been performed, handling both signal acquisition and Bluetooth transmission concurrently, thanks to a FreeRTOS custom implementation. In particular, the system adapts to both sEMG and ATC transmission, with an application throughput up to 2 kB s-1 and an average operating time of 80 h (for high resolution sEMG sampling), relaxable to 8Bs-1 throughput and about 230 h operating time (considering a 110mAh battery), in case of ATC acquisition only. Here we share our experience over the years in designing wearable systems for the sEMG detection, specifying in detail how our event-driven approach could benefit the device development phases. Some previous basic knowledge about biosignal acquisition, electronic circuits and programming would certainly ease the repeatability of this tutorial

Intra-Body Communications for Nervous System Applications: Current Technologies and Future Directions

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.Comment: https://www.sciencedirect.com/science/article/pii/S138912862300163

A Biomimetic Multichannel Synergistic Calibration for Event-Driven Functional Electrical Stimulation

In this paper, we present the Profile Extraction (PE) algorithm, which allows the computation of a multi-channel profile highly correlated with voluntary muscle activity. This event-based profile can be used as biomimetic control during the calibration phase of a Functional Electrical Stimulation (FES) system. The adoption of the PE technique represents the preliminary step to extend the applicability of our event-driven paradigm to control the coordinated multi-joint movements. Through an experimental campaign, we tested the improvements made by the use of PE in the FES calibration, assessing the reproducibility between the voluntary and stimulated movements. Results show a 2 % increase of the median correlation value for a single-channel exercise and a 3.6 % increase for a dual-channel one. A statistical decrease of normalized Root Mean Square Error has been obtained for the dual-channel exercise (p < 0.05)

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work

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