Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach

In recent times, Artificial Intelligence (AI) has become ubiquitous in technological fields, mainly due to its ability to perform computations in distributed systems or the cloud. Nevertheless, for some applications -as the case of EMG signal processing- it may be highly advisable or even mandatory an on-the-edge processing, i.e., an embedded processing methodology. On the other hand, sEMG signals have been traditionally processed using LTI techniques for simplicity in computing. However, making this strong assumption leads to information loss and spurious results. Considering the current advances in silicon technology and increasing computer power, it is possible to process these biosignals with AI-based techniques correctly. This paper presents an embedded-processing-based adaptive filtering system (here termed edge AI) being an outstanding alternative in contrast to a sensor-computer- actuator system and a classical digital signal processor (DSP) device. Specifically, a PYNQ-Z1 embedded system is used. For experimental purposes, three methodologies on similar processing scenarios are compared. The results show that the edge AI methodology is superior to benchmark approaches by reducing the processing time compared to classical DSPs and general standards while maintaining the signal integrity and processing it, considering that the EMG system is not LTI. Likewise, due to the nature of the proposed architecture, handling information exhibits no leakages. Findings suggest that edge computing is suitable for EMG signal processing when an on-device analysis is required

Multimodal Approach for Emotion Recognition Using a Formal Computational Model

International audience— Emotions play a crucial role in human-computer interaction. They are generally expressed and perceived through multiple modalities such as speech, facial expressions, physiological signals. Indeed, the complexity of emotions makes the acquisition very difficult and makes unimodal systems (i.e., the observation of only one source of emotion) unreliable and often unfeasible in applications of high complexity. Moreover the lack of a standard in human emotions modeling hinders the sharing of affective information between applications. In this paper, we present a multimodal approach for the emotion recognition from many sources of information. This paper aims to provide a multi-modal system for emotion recognition and exchange that will facilitate inter-systems exchanges and improve the credibility of emotional interaction between users and computers. We elaborate a multimodal emotion recognition method from Physiological Data based on signal processing algorithms. Our method permits to recognize emotion composed of several aspects like simulated and masked emotions. This method uses a new multidimensional model to represent emotional states based on an algebraic representation. The experimental results show that the proposed multimodal emotion recognition method improves the recognition rates in comparison to the unimodal approach. Compared to the state of art multimodal techniques, the proposed method gives a good results with 72% of correct

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

AN INVESTIGATION OF ELECTROMYOGRAPHIC (EMG) CONTROL OF DEXTROUS HAND PROSTHESES FOR TRANSRADIAL AMPUTEES

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Plymouth University's products or services.There are many amputees around the world who have lost a limb through conflict, disease or an accident. Upper-limb prostheses controlled using surface Electromyography (sEMG) offer a solution to help the amputees; however, their functionality is limited by the small number of movements they can perform and their slow reaction times. Pattern recognition (PR)-based EMG control has been proposed to improve the functional performance of prostheses. It is a very promising approach, offering intuitive control, fast reaction times and the ability to control a large number of degrees of freedom (DOF). However, prostheses controlled with PR systems are not available for everyday use by amputees, because there are many major challenges and practical problems that need to be addressed before clinical implementation is possible. These include lack of individual finger control, an impractically large number of EMG electrodes, and the lack of deployment protocols for EMG electrodes site selection and movement optimisation. Moreover, the inability of PR systems to handle multiple forces is a further practical problem that needs to be addressed. The main aim of this project is to investigate the research challenges mentioned above via non-invasive EMG signal acquisition, and to propose practical solutions to help amputees. In a series of experiments, the PR systems presented here were tested with EMG signals acquired from seven transradial amputees, which is unique to this project. Previous studies have been conducted using non-amputees. In this work, the challenges described are addressed and a new protocol is proposed that delivers a fast clinical deployment of multi-functional upper limb prostheses controlled by PR systems. Controlling finger movement is a step towards the restoration of lost human capabilities, and is psychologically important, as well as physically. A central thread running through this work is the assertion that no two amputees are the same, each suffering different injuries and retaining differing nerve and muscle structures. This work is very much about individualised healthcare, and aims to provide the best possible solution for each affected individual on a case-by-case basis. Therefore, the approach has been to optimise the solution (in terms of function and reliability) for each individual, as opposed to developing a generic solution, where performance is optimised against a test population. This work is unique, in that it contributes to improving the quality of life for each individual amputee by optimising function and reliability. The main four contributions of the thesis are as follows: 1- Individual finger control was achieved with high accuracy for a large number of finger movements, using six optimally placed sEMG channels. This was validated on EMG signals for ten non-amputee and six amputee subjects. Thumb movements were classified successfully with high accuracy for the first time. The outcome of this investigation will help to add more movements to the prosthesis, and reduce hardware and computational complexity. 2- A new subject-specific protocol for sEMG site selection and reliable movement subset optimisation, based on the amputee’s needs, has been proposed and validated on seven amputees. This protocol will help clinicians to perform an efficient and fast deployment of prostheses, by finding the optimal number and locations of EMG channels. It will also find a reliable subset of movements that can be achieved with high performance. 3- The relationship between the force of contraction and the statistics of EMG signals has been investigated, utilising an experimental design where visual feedback from a Myoelectric Control Interface (MCI) helped the participants to produce the correct level of force. Kurtosis values were found to decrease monotonically when the contraction level increased, thus indicating that kurtosis can be used to distinguish different forces of contractions. 4- The real practical problem of the degradation of classification performance as a result of the variation of force levels during daily use of the prosthesis has been investigated, and solved by proposing a training approach and the use of a robust feature extraction method, based on the spectrum. The recommendations of this investigation improve the practical robustness of prostheses controlled with PR systems and progress a step further towards clinical implementation and improving the quality of life of amputees. The project showed that PR systems achieved a reliable performance for a large number of amputees, taking into account real life issues such as individual finger control for high dexterity, the effect of force level variation, and optimisation of the movements and EMG channels for each individual amputee. The findings of this thesis showed that the PR systems need to be appropriately tuned before usage, such as training with multiple forces to help to reduce the effect of force variation, aiming to improve practical robustness, and also finding the optimal EMG channel for each amputee, to improve the PR system’s performance. The outcome of this research enables the implementation of PR systems in real prostheses that can be used by amputees.Ministry of Higher Education and Scientific Research and Baghdad University- Baghdad/Ira

Estimation of the second ventilatory threshold through ventricular repolarization profile analysis

Under the hypothesis that sympathetic control of ventricular repolarization may change once the second ventilatory threshold (VT2) has been reached, a novel methodology for non-invasive VT2 estimation based on the analysis of the T wave from the electrocardiogram (ECG) is proposed, and potential underlying physiological mechanisms are suggested. 25 volunteers (33.4 ± 5.2 years) underwent an incremental power cycle ergometer test (25 W/minute). During the test, respiratory gas exchange and multi-lead ECG were acquired. The former was employed to determine VT2, used here as a reference, whereas the latter was used to compute the temporal profiles of an index of ventricular repolarization instability (dT) and its low-frequency (LF) oscillations (LFdT). The sudden increases observed in dT and LFdT profiles above an established heart rate threshold were employed to derive VT2 estimates, referred to as VT2dT and VT2LFdT, respectively. Estimation errors of -4.7 ± 25.2 W were obtained when considering VT2dT. Errors were lower than the one-minute power increment of 25 W in 68% of the subjects and lower than 50 W in 89.5% of them. When using VT2LFdT, estimation error was of 15.3 ± 32.4 W. Most of the subjects shared common characteristic dT and LFdT profiles, which could be reflecting changes in the autonomic control of ventricular repolarization before and after reaching VT2. The analysis of ventricular repolarization dynamics during exercise allows non-invasive ECG-based estimation of VT2, possibly in relation to changes in the autonomic control of ventricular electrical activity when VT2 is reached

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

Methods and metrics for the improvement of the interaction and the rehabilitation of cerebral palsy through inertial technology

Cerebral palsy (CP) is one of the most limiting disabilities in childhood, with 2.2 cases per 1000 1-year survivors. It is a disorder of movement and posture due to a defect or lesion of the immature brain during the pregnancy or the birth. These motor limitations appear frequently in combination with sensory and cognitive alterations generally result in great difficulties for some people with CP to manipulate objects, communicate and interact with their environment, as well as limiting their mobility. Over the last decades, instruments such as personal computers have become a popular tool to overcome some of the motor limitations and promote neural plasticity, especially during childhood. According to some estimations, 65% of youths with CP that present severely limited manipulation skills cannot use standard mice nor keyboards. Unfortunately, even when people with CP use assistive technology for computer access, they face barriers that lead to the use of typical mice, track balls or touch screens for practical reasons. Nevertheless, with the proper customization, novel developments of alternative input devices such as head mice or eye trackers can be a valuable solution for these individuals. This thesis presents a collection of novel mapping functions and facilitation algorithms that were proposed and designed to ease the act of pointing to graphical elements on the screen—the most elemental task in human-computer interaction—to individuals with CP. These developments were implemented to be used with any head mouse, although they were all tested with the ENLAZA, an inertial interface. The development of such techniques required the following approach: Developing a methodology to evaluate the performance of individuals with CP in pointing tasks, which are usually described as two sequential subtasks: navigation and targeting. Identifying the main motor abnormalities that are present in individuals with CP as well as assessing the compliance of these people with standard motor behaviour models such as Fitts’ law. Designing and validating three novel pointing facilitation techniques to be implemented in a head mouse. They were conceived for users with CP and muscle weakness that have great difficulties to maintain their heads in a stable position. The first two algorithms consist in two novel mapping functions that aim to facilitate the navigation phase, whereas the third technique is based in gravity wells and was specially developed to facilitate the selection of elements in the screen. In parallel with the development of the facilitation techniques for the interaction process, we evaluated the feasibility of use inertial technology for the control of serious videogames as a complement to traditional rehabilitation therapies of posture and balance. The experimental validation here presented confirms that this concept could be implemented in clinical practice with good results. In summary, the works here presented prove the suitability of using inertial technology for the development of an alternative pointing device—and pointing algorithms—based on movements of the head for individuals with CP and severely limited manipulation skills and new rehabilitation therapies for the improvement of posture and balance. All the contributions were validated in collaboration with several centres specialized in CP and similar disorders and users with disability recruited in those centres.La parálisis cerebral (PC) es una de las deficiencias más limitantes de la infancia, con un incidencia de 2.2 casos por cada 1000 supervivientes tras un año de vida. La PC se manifiesta principalmente como una alteración del movimiento y la postura y es consecuencia de un defecto o lesión en el cerebro inmaduro durante el embarazo o el parto. Las limitaciones motrices suelen aparecer además en compañía de alteraciones sensoriales y cognitivas, lo que provoca por lo general grandes dificultades de movilidad, de manipulación, de relación y de interacción con el entorno. En las últimas décadas, el ordenador personal se ha extendido como herramienta para la compensación de parte de estas limitaciones motoras y como medio de promoción de la neuroplasticidad, especialmente durante la infancia. Desafortunadamente, cerca de un 65% de las personas PC que son diagnosticadas con limitaciones severas de manipulación son incapaces de utilizar ratones o teclados convencionales. A veces, ni siquiera la tecnología asistencial les resulta de utilidad ya que se encuentran con impedimentos que hacen que opten por usar dispositivos tradicionales aun sin dominar su manejo. Para estas personas, los desarrollos recientes de ratones operados a través de movimientos residuales con la cabeza o la mirada podrían ser una solución válida, siempre y cuando se personalice su manejo. Esta tesis presenta un conjunto de novedosas funciones de mapeo y algoritmos de facilitaci ón que se han propuesto y diseñado con el ánimo de ayudar a personas con PC en las tareas de apuntamiento de objetos en la pantalla —las más elementales dentro de la interacción con el ordenador. Aunque todas las contribuciones se evaluaron con la interfaz inercial ENLAZA, desarrollada igualmente en nuestro grupo, podrían ser aplicadas a cualquier ratón basado en movimientos de cabeza. El desarrollo de los trabajos se resume en las siguientes tareas abordadas: Desarrollo de una metodología para la evaluación de la habilidad de usuarios con PC en tareas de apuntamiento, que se contemplan como el encadenamiento de dos sub-tareas: navegación (alcance) y selección (clic). Identificación de los tipos de alteraciones motrices presentes en individuos con PC y el grado de ajuste de éstos a modelos estándares de comportamiento motriz como puede ser la ley de Fitts. Propuesta y validación de tres técnicas de facilitación del alcance para ser implementadas en un ratón basado en movimientos de cabeza. La facilitación se ha centrado en personas que presentan debilidad muscular y dificultades para mantener la posición de la cabeza. Mientras que los dos primeros algoritmos se centraron en facilitar la navegación, el tercero tuvo como objetivo ayudar en la selección a través de una técnica basada en pozos gravitatorios de proximidad. En paralelo al desarrollo de estos algoritmos de facilitación de la interacción, evaluamos la posibilidad de utilizar tecnología inercial para el control de videojuegos en rehabilitación. Nuestra validación experimental demostró que este concepto puede implementarse en la práctica clínica como complemento a terapias tradicionales de rehabilitación de la postura y el equilibrio. Como conclusión, los trabajos desarrollados en esta tesis vienen a constatar la idoneidad de utilizar sensores inerciales para el desarrollo de interfaces de accesso alternativo al ordenador basados en movimientos residuales de la cabeza para personas con limitaciones severas de manipulación. Esta solución se complementa con algoritmos de facilitación del alcance. Por otra parte, estas soluciones tecnológicas de interfaz con el ordenador representan igualmente un complemento de terapias tradicionales de rehabilitación de la postura y el equilibrio. Todas las contribuciones se validaron en colaboración con una serie de centros especializados en parálisis cerebral y trastornos afines contando con usuarios con discapacidad reclutados en dichos centros.This thesis was completed in the Group of Neural and Cognitive Engineering (gNEC) of the CAR UPM-CSIC with the financial support of the FP7 Framework EU Research Project ABC (EU-2012-287774), the IVANPACE Project (funded by Obra Social de Caja Cantabria, 2012-2013), and the Spanish Ministry of Economy and Competitiveness in the framework of two projects: the Interplay Project (RTC-2014-1812-1) and most recently the InterAAC Project (RTC-2015-4327-1)Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Juan Manuel Belda Lois.- Secretario: María Dolores Blanco Rojas.- Vocal: Luis Fernando Sánchez Sante