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

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)

Design of a Programmable and Modular Neuromuscular Electrical Stimulator Integrated into a Wireless Body Sensor Network

Neuromuscular electrical stimulation finds application in several fields, from basic neurophysiology, to motor rehabilitation and cardiovascular conditioning. Despite the progressively increasing interest in this technique, its State-of-the-Art technology is mainly based on monolithic, mostly wired devices, leading to two main issues. First, these devices are often bulky, limiting their usability in applied contexts. Second, the possibility of interfacing these stimulation devices with external systems for the acquisition of electrophysiological and biomechanical variables to control the stimulation output is often limited. The aim of this work is to describe the design and development of an innovative electrical stimulator, specifically developed to contend with these issues. The developed device is composed of wireless modules that can be programmed and easily interfaced with third-party instrumentation. Moreover, benefiting from the system modular architecture, stimulation may be delivered concurrently to different sites while greatly reducing cable encumbrance. The main design choices and experimental tests are documented, evidencing the practical potential of the device in use-case scenarios

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)

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

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

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

Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

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