Raspberry Pi based Modular System for Multichannel Event-Driven Functional Electrical Stimulation Control

This paper describes the implementation and testing of a modular software for multichannel control of Functional Electrical Stimulation (FES). Moving towards an embedded scenario, the core of the system is a Raspberry Pi, whose different models (with different computing powers) best suit two different system use-cases: user-supervised and stand-alone. Given the need for real-time and reliable FES applications, software processing timings were analyzed for multiple configurations, along with hardware resources utilization. Among the results, the simultaneous use of eight channels has been functionally achieved (0% lost packets) while minimizing system timing failures (excessive processing latency). Further investigations included stressing the system using more constraining acquisition parameters, eventually limiting the usable channels (only for the stand-alone use-case)

Hilbert squares of degeneracy loci

Let $S$ be the first degeneracy locus of a morphism of vector bundles corresponding to a general matrix of linear forms in $\mathbb{P}^s$. We prove that, under certain positivity conditions, its Hilbert square $\mathrm{Hilb}^2(S)$ is isomorphic to the zero locus of a global section of an irreducible homogeneous vector bundle on a product of Grassmannians. Our construction involves a naturally associated Fano variety, and an explicit description of the isomorphism.Comment: 20 pages, comments welcome

Combining Action Observation Treatment with a Brain–Computer Interface System: Perspectives on Neurorehabilitation

Action observation treatment (AOT) exploits a neurophysiological mechanism, matching an observed action on the neural substrates where that action is motorically represented. This mechanism is also known as mirror mechanism. In a typical AOT session, one can distinguish an observation phase and an execution phase. During the observation phase, the patient observes a daily action and soon after, during the execution phase, he/she is asked to perform the observed action at the best of his/her ability. Indeed, the execution phase may sometimes be difficult for those patients where motor impairment is severe. Although, in the current practice, the physiotherapist does not intervene on the quality of the execution phase, here, we propose a stimulation system based on neurophysiological parameters. This perspective article focuses on the possibility to combine AOT with a brain–computer interface system (BCI) that stimulates upper limb muscles, thus facilitating the execution of actions during a rehabilitation session. Combining a rehabilitation tool that is well-grounded in neurophysiology with a stimulation system, such as the one proposed, may improve the efficacy of AOT in the treatment of severe neurological patients, including stroke patients, Parkinson’s disease patients, and children with cerebral palsy

Live Demonstration: A Real-Time Bio-Mimetic System for Multichannel FES Control

This demonstration presents a bio-mimetic system for the real-time multichannel control of Functional Electrical Stimulation (FES). The intensities of the FES profiles are directly mapped by processing surface ElectroMyoGraphic (sEMG) signals detected from synergistic muscles, thus achieving a user-comfortable stimulation that follows the monitored physiological patterns. Furthermore, a user-dedicated calibration routine and multiple versatile operating configurations allow the system to be integrated into standard rehabilitation protocols to enhance the restoration of motor functionalities

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)

Motion Analysis for Experimental Evaluation of an Event-Driven FES System

In this work, a system for controlling Functional Electrical Stimulation (FES) has been experimentally evaluated. The peculiarity of the system is to use an event-driven approach to modulate stimulation intensity, instead of the typical feature extraction of surface ElectroMyoGraphic (sEMG) signal. To validate our methodology, the system capability to control FES was tested on a population of 17 subjects, reproducing 6 different movements. Limbs trajectories were acquired using a gold standard motion tracking tool. The implemented segmentation algorithm has been detailed, together with the designed experimental protocol. A motion analysis was performed through a multiparametric evaluation, including the extraction of features such as the trajectory area and the movement velocity. The obtained results show a median cross-correlation coefficient of 0.910 and a median delay of 800 ms, between each couple of voluntary and stimulated exercise, making our system comparable w.r.t. state-of-the-art works. Furthermore, a 97.39% successful rate on movement replication demonstrates the feasibility of the system for rehabilitation purposes

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

Low Latency Protocols Investigation for Event-Driven Wireless Body Area Networks

Nowadays distributed electronic health and fitness monitoring are hot-topics in bio-engineering, however common solutions for Wireless Body Area Networks (WBANs) featuring high-density sampled data transmission still stumbles over the trade-off among data rate, application throughput, and latency. Therefore, the Bluetooth Low Energy (BLE) and the IEEE 802.15.4 protocols are here investigated, with the aim of developing an event-driven WBAN to support a threshold-crossing surface ElectroMyoGraphy (sEMG) acquisition approach. We then implemented a custom protocol to overcome their limitations and fulfil all the requirements, resulting in a transmission latency of 0.856 ms ± 1 µs and enabling a functional operating time up to 110 h

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

© 2001 CRL Publishing Ltd Hierarchical modelling of complex control systems: dependability analysis

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