Towards electrodeless EMG linear envelope signal recording for myo-activated prostheses control

After amputation, the residual muscles of the limb may function in a normal way, enabling the electromyogram (EMG) signals recorded from them to be used to drive a replacement limb. These replacement limbs are called myoelectric prosthesis. The prostheses that use EMG have always been the first choice for both clinicians and engineers. Unfortunately, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.); researchers have aspired to find suitable alternatives. One proposes the dry-contact, low-cost sensor based on a force-sensitive resistor (FSR) as a valid alternative which instead of detecting electrical events, detects mechanical events of muscle. FSR sensor is placed on the skin through a hard, circular base to sense the muscle contraction and to acquire the signal. Similarly, to reduce the output drift (resistance) caused by FSR edges (creep) and to maintain the FSR sensitivity over a wide input force range, signal conditioning (Voltage output proportional to force) is implemented. This FSR signal acquired using FSR sensor can be used directly to replace the EMG linear envelope (an important control signal in prosthetics applications). To find the best FSR position(s) to replace a single EMG lead, the simultaneous recording of EMG and FSR output is performed. Three FSRs are placed directly over the EMG electrodes, in the middle of the targeted muscle and then the individual (FSR1, FSR2 and FSR3) and combination of FSR (e.g. FSR1+FSR2, FSR2-FSR3) is evaluated. The experiment is performed on a small sample of five volunteer subjects. The result shows a high correlation (up to 0.94) between FSR output and EMG linear envelope. Consequently, the usage of the best FSR sensor position shows the ability of electrode less FSR-LE to proportionally control the prosthesis (3-D claw). Furthermore, FSR can be used to develop a universal programmable muscle signal sensor that can be suitable to control the myo-activated prosthesis

Dynamics, Electromyography and Vibroarthrography as Non-Invasive Diagnostic Tools: Investigation of the Patellofemoral Joint

The knee joint plays an essential role in the human musculoskeletal system. It has evolved to withstand extreme loading conditions, while providing almost frictionless joint movement. However, its performance may be disrupted by disease, anatomical deformities, soft tissue imbalance or injury. Knee disorders are often puzzling, and accurate diagnosis may be challenging. Current evaluation approach is usually limited to a detailed interview with the patient, careful physical examination and radiographic imaging. The X-ray screening may reveal bone degeneration, but does not carry sufficient information of the soft tissue conditions. More advanced imaging tools such as MRI or CT are available, but expensive, time consuming and can be used only under static conditions. Moreover, due to limited resolution the radiographic techniques cannot reveal early stage arthritis. The arthroscopy is often the only reliable option, however due to its semi-invasive nature, it cannot be considered as a practical diagnostic tool. Therefore, the motivation for this work was to combine three scientific methods to provide a comprehensive, non-invasive evaluation tool bringing insight into the in vivo, dynamic conditions of the knee joint and articular cartilage degeneration. Electromyography and inverse dynamics were employed to independently determine the forces present in several muscles spanning the knee joint. Though both methods have certain limitations, the current work demonstrates how the use of these two methods concurrently enhances the biomechanical analysis of the knee joint conditions, especially the performance of the extensor mechanism. The kinetic analysis was performed for 12 TKA, 4 healthy individuals in advanced age and 4 young subjects. Several differences in the knee biomechanics were found between the three groups, identifying age-related and post-operative decrease in the extensor mechanism efficiency, explaining the increased effort of performing everyday activities experienced by the elderly and TKA subjects. The concept of using accelerometers to assess the cartilage degeneration has been proven based on a group of 23 subjects with non-symptomatic knees and 52 patients suffering from knee arthritis. Very high success (96.2%) of pattern classification obtained in this work clearly demonstrates that vibroarthrography is a promising, non-invasive and low-cost technique offering screening capabilities

Non-Invasive Investigation of Human Foot Muscles Function

Appropriate functioning of the human foot is fundamental for good quality of life. The intrinsic foot muscles (IFM) are a crucial component of the foot, but their natural behaviour and contribution to good foot health is currently poorly understood. Recording muscle activation from IFM has been attempted with invasive techniques, but these generally only allow assessment of one muscle at a time and are not much used in many clinical populations (e.g. children, patients with peripheral neuropathy or on blood thinning medication). Here a novel application of multi-channel surface electromyography (sEMG) electrodes is presented to non-invasively, record sEMG and quantify activation patterns of IFMs from across the plantar region of the foot. sEMG (13×5 array), kinematics and force plate data were recorded from 30 healthy adult volunteers who completed six postural balance tasks (e.g. bipedal stance, one-foot stance, two-foot tip-toe). Linear (amplitude based) and non-linear (entropy based) methodologies were used to evaluate the physiological features of the sEMG, the patterns of activation, the association with whole body and foot biomechanics and the neuromuscular drive to the IFM. EMG signals features (amplitude and frequency) were shown to be in the physiological ranges reported in the literature (Basmajian and De Luca, 1985), with spatially clustered patterns of high activation corresponding to the Flexor digitorum brevis muscle. IFMs responded differently based on the direction of postural sway, with greater activations associated with sways in the mediolateral direction. Entropy based, non-linear analysis revealed that neuromuscular drive to IFM depends on the balance demand of the postural task, with greater drive evident for more challenging tasks (i.e. standing on tiptoe). Combining non-invasive measures of IFM activation and entropy based assessment of temporal organisation (or structure) of EMG signal variability is therefore revealing of IFM function and will enable a more detailed assessment of IFM function across healthy and clinical populations

Evaluation of Spatiotemporal Patterns of the Spinal Muscle Coordination Output during Walking in the Exoskeleton

Recent advances in the performance and evaluation of walking in exoskeletons use various assessments based on kinematic/kinetic measurements. While such variables provide general characteristics of gait performance, only limited conclusions can be made about the neural control strategies. Moreover, some kinematic or kinetic parameters are a consequence of the control implemented on the exoskeleton. Therefore, standard indicators based on kinematic variables have limitations and need to be complemented by performance measures of muscle coordination and control strategy. Knowledge about what happens at the spinal cord output level might also be critical for rehabilitation since an abnormal spatiotemporal integration of activity in specific spinal segments may result in a risk for abnormalities in gait recovery. Here we present the PEPATO software, which is a benchmarking solution to assess changes in the spinal locomotor output during walking in the exoskeleton with respect to reference data on normal walking. In particular, functional and structural changes at the spinal cord level can be mapped into muscle synergies and spinal maps of motoneuron activity. A user-friendly software interface guides the user through several data processing steps leading to a set of performance indicators as output. We present an example of the usage of this software for evaluating walking in an unloading exoskeleton that allows a person to step in simulated reduced (the Moon's) gravity. By analyzing the EMG activity from lower limb muscles, the algorithms detected several performance indicators demonstrating differential adaptation (shifts in the center of activity, prolonged activation) of specific muscle activation modules and spinal motor pools and increased coactivation of lumbar and sacral segments. The software is integrated at EUROBENCH facilities to benchmark the performance of walking in the exoskeleton from the point of view of changes in the spinal locomotor output

Muscle synergy analysis of lower-limb movements

Dissertação de mestrado integrado em Biomedical Engineering (área de especialização em Medical Electronics)Neurological disorders and trauma often lead to impaired lower-limb motor coordination. Understanding how muscles combine to produce movement can directly benefit assistive solutions to those afflicted with these impairments. A theory in neuromusculoskeletal research, known as muscle synergies, has shown promising results in applications for this field. This hypothesis postulates that the Central Nervous System controls motor tasks through the time-variant combinations of modules (or synergies), each representing the co-activation of a group of muscles. There is, however, no unifying, evidence-based framework to ascertain muscle synergies, as synergy extraction methods vary greatly in the literature. Publications also focus on gait analysis, leaving a knowledge gap when concerning motor tasks important to daily life such as sitting and standing. The purpose of this dissertation is the development of a robust, evidence-based, task-generic synergy extraction framework unifying the divergent methodologies of this field of study, and to use this framework to study healthy muscle synergies on several activities of daily living: walking, sit-to-stand, stand-to-sit and knee flexion and extension. This was achieved by designing and implementing a cross-validated Non-Negative Matrix Factorization process and applying it to muscle electrical activity data. A preliminary study was undertaken to tune this configuration regarding cross-validating proportions, data structuring prior to factorization and evaluating criteria quantifying accuracy in modularity findings. Muscle synergies results were then investigated for different performing speeds to determine if their structure differed, and for consistency across subjects, to ascertain if a common set of muscle synergies underlay control on all subjects equally. Results revealed that the implemented framework was consistent in its ability to capture modularity (p < 0:05). The movements’ synergies also did not differ across the studied range of speeds (except one module in Knee Flexion) (p < 0:05). Additionally, a common set of muscle synergies was present across several subjects (p < 0:05), but shared commonality across every participant was only observed for the walking trials, for which much larger amounts of data were collected. Overall, the established framework is versatile and applicable for different lower-limb movements; muscle synergies findings for the examined movements may also be used as control references in assistive devices.As perturbações e traumas neurológicos afetam frequentemente a coordenação motora dos membros inferiores. Uma teoria recente em investigação neuromusculo-esquelética, denominada de sinergias musculares, tem demonstrado resultados promissores em soluções de assistência à população afetada por estes distúrbios. Esta teoria propõe que o Sistema Nervoso Central controla as tarefas motoras através de combinações variantes no tempo de módulos (ou sinergias), sendo que cada um representa a co-ativação de um grupo de músculos. No entanto, não existe nenhum processo uniformizante, empiricamente justificado para determinar sinergias musculares, porque os métodos de extração de sinergias variam muito na literatura. Para além disso, as publicações normalmente focam-se em análise da marcha, deixando uma lacuna de conhecimento em tarefas motoras do dia-a-dia, tais como sentar e levantar. O objetivo desta dissertação é o desenvolvimento de um processo robusto, genérico e empiricamente justificado de extração de sinergias em várias tarefas motoras, unindo as metodologias divergentes neste campo de estudo, e subsequentemente utilizar este processo para estudar sinergias musculares de sujeitos saudáveis em várias atividades do dia-a-dia: marcha, erguer-se de pé partir de uma posição sentada, sentar-se a partir de uma posição de pé e extensão e flexão do joelho. Isto foi alcançado através da implementação de um processo de cross-validated Non-Negative Matrix Factorization e subsequente aplicação em dados de atividade elétrica muscular. Um estudo preliminar foi realizado para configurar este processo relativamente às proporções de cross-validation, estruturação de dados antes da fatorização e seleção de critério que quantifique o sucesso da representação modular dos dados. Os resultados da extração de sinergias de diferentes velocidades de execução foram depois examinados no sentido de descobrir se este fator influenciava a estrutura dos módulos motores, assim como se semelhanças entre as sinergias de diferentes sujeitos apontavam para um conjunto comum de sinergias musculares subjacente ao controlo do movimento. Os resultados revelaram que o processo implementado foi consistente na sua capacidade de capturar a modularidade nos dados recolhidos (p < 0:05). As sinergias de todos os movimentos também não diferiram para toda a gama de velocidades estudada (exceto um módulo na flexão do joelho) (p < 0:05). Por fim, um conjunto comum de sinergias musculares esteve presente em vários sujeitos (p < 0:05), mas só esteve presente em todos os sujeitos de igual forma para a marcha, para a qual a quantidade de dados recolhida foi muito maior. Globalmente, o processo implementado é versátil e aplicável a diferentes movimentos dos membros inferiores; os resultados das sinergias musculares para os movimentos examinados podem também ser utilizado como referências de controlo para dispositivos de assistência

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

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

Intelligent signal processing for digital healthcare monitoring

Ein gesunder Gang ist ein komplexer Prozess und erfordert ein Gleichgewicht zwischen verschiedenen neurophysiologischen Systemen im Körper und gilt als wesentlicher Indikator für den physischen und kognitiven Gesundheitszustand einer Person. Folglich würden Anwendungen im Bereich der Bioinformatik und des Gesundheitswesens erheblich von den Informationen profitieren, die sich aus einer längeren oder ständigen Überwachung des Gangs, der Gewohnheiten und des Verhaltens von Personen unter ihren natürlichen Lebensbedingungen und bei ihren täglichen Aktivitäten mit Hilfe intelligenter Geräte ergeben. Vergleicht man Trägheitsmess- und stationäre Sensorsysteme, so bieten erstere hervorragende Möglichkeiten für Ganganalyseanwendungen und bieten mehrere Vorteile wie geringe Größe, niedriger Preis, Mobilität und sind leicht in tragbare Systeme zu integrieren. Die zweiten gelten als der Goldstandard, sind aber teuer und für Messungen im Freien ungeeignet. Diese Arbeit konzentriert sich auf die Verbesserung der Zeit und Qualität der Gangrehabilitation nach einer Operation unter Verwendung von Inertialmessgeräten, indem sie eine neuartige Metrik zur objektiven Bewertung des Fortschritts der Gangrehabilitation in realen Umgebungen liefert und die Anzahl der verwendeten Sensoren für praktische, reale Szenarien reduziert. Daher wurden die experimentellen Messungen für eine solche Analyse in einer stark kontrollierten Umgebung durchgeführt, um die Datenqualität zu gewährleisten. In dieser Arbeit wird eine neue Gangmetrik vorgestellt, die den Rehabilitationsfortschritt anhand kinematischer Gangdaten von Aktivitäten in Innen- und Außenbereichen quantifiziert und verfolgt. In dieser Arbeit wird untersucht, wie Signalverarbeitung und maschinelles Lernen formuliert und genutzt werden können, um robuste Methoden zur Bewältigung von Herausforderungen im realen Leben zu entwickeln. Es wird gezeigt, dass der vorgeschlagene Ansatz personalisiert werden kann, um den Fortschritt der Gangrehabilitation zu verfolgen. Ein weiteres Thema dieser Arbeit ist die erfolgreiche Anwendung von Methoden des maschinellen Lernens auf die Ganganalyse aufgrund der großen Datenmenge, die von den tragbaren Sensorsystemen erzeugt wird. In dieser Arbeit wird das neuartige Konzept des ``digitalen Zwillings'' vorgestellt, das die Anzahl der verwendeten Wearable-Sensoren in einem System oder im Falle eines Sensorausfalls reduziert. Die Evaluierung der vorgeschlagenen Metrik mit gesunden Teilnehmern und Patienten unter Verwendung statistischer Signalverarbeitungs- und maschineller Lernmethoden hat gezeigt, dass die Einbeziehung der extrahierten Signalmerkmale in realen Szenarien robust ist, insbesondere für das Szenario mit Rehabilitations-Gehübungen in Innenräumen. Die Methodik wurde auch in einer klinischen Studie evaluiert und lieferte eine gute Leistung bei der Überwachung des Rehabilitationsfortschritts verschiedener Patienten. In dieser Arbeit wird ein Prototyp einer mobilen Anwendung zur objektiven Bewertung des Rehabilitationsfortschritts in realen Umgebungen vorgestellt

Influence of the robotic exoskeleton Lokomat on the control of human gait: an electromyographic and kinematic analysis

Dissertação de mestrado integrado em Engenharia BiomédicaNowadays there is an increasing percentage of elderly people and it is expected that this percentage will continue increasing. This aging of the population carries huge costs to the government, especially in the provision of health care. Among those health care, there is the motor rehabilitation after a stroke. The recent robotic devices for gait training are pointed out as an excellent solution to solve this problem, because besides the cost savings they can provide longer and more innovative trainings. All the advantages presented by such devices can trigger more research in this area as well as more government investments. There are already some control strategies implemented in these devices, which should be improved to create new motor rehabilitation interventions. One strategy that can be used in the future is to provide the amount of motor assistance as the patient really needs to achieve certain goals. Lokomat is one of these rehabilitation devices, which allows changing the percentage of assistance provided to the user. However, it is necessary to study the effects of such strategy in the physiological response of the users. There is more and more consensus about the need to obtain muscular activation patterns and kinematic patterns during walking in devices as Lokomat very similar to those obtained by healthy subjects during non-assisted walking. Recent scientific investigations make us to believe that the nervous system controls human gait via a simple modular structure. It is important to understand how this structure works when the walking is assisted by robotic devices. Thus, this work had three main objectives: to study the muscular electric activity during walking in Lokomat, by varying the total assistance provided by the device, as well as the walking speed; to analyze kinematic changes obtained during Lokomat-assisted walking, as well as the interaction forces between each user and the robotic device; to understand how this modular organization of the nervous system involved in synchronization of the muscular activity works during walking assisted by robotic devices. Only healthy subjects participated in our study. Therefore, our work generated a basis of comparison for future control strategies to be implemented in motor rehabilitation. We obtained quite encouraging results, which allow us to formulate new strategies for motor rehabilitation. In the future, these strategies will be implemented and it expected that post-stroke people can restore their normal gait more quickly.Actualmente verifica-se um aumento crescente da percentagem de pessoas idosas e prevê-se que essa percentagem continue a aumentar. Este envelhecimento da população acarreta enormes custos para o estado, sobretudo na prestação dos cuidados de saúde. Entre esses cuidados, está a reabilitação motora após um AVC (acidente vascular cerebral). Os novos dispositivos robóticos de treino da marcha são apontados como uma excelente solução para este problema, pois além da poupança de custos poderão proporcionar treinos de maior duração e mais inovadores. Todas as vantagens apresentadas por este tipo de dispositivos podem servir para o despoletar de cada vez mais investigação nesta área e investimentos governamentais. Existem já algumas estratégias de controlo implementadas nestes dispositivos, que devem ser melhoradas para se criarem novas intervenções de reabilitação motora. Uma estratégia que se poderá utilizar futuramente nesses dispositivos consiste em providenciar somente a ajuda motora necessária para que o paciente atinja determinados objectivos. O Lokomat é um destes dispositivos, que permite variar a percentagem de ajuda providenciada. É no entanto necessário estudar os efeitos de tal estratégia na resposta fisiológica dos utilizadores. Cada vez se verifica maior consenso acerca da necessidade de se obterem padrões de activação muscular e padrões cinemáticos durante a marcha em dispositivos como o Lokomat muito similares aos obtidos por indivíduos saudáveis em marcha não assistida. Recentes investigações científicas levam-nos a crer que o sistema nervoso controla a marcha humana através de uma estrutura modular simples. É importante saber como actua essa estrutura quando a marcha é assistida por dispositivos robóticos. Assim, este trabalho teve três objectivos principais: estudar a actividade eléctrica muscular durante a marcha em Lokomat, variando a ajuda total providenciada pelo dispositivo, bem como a velocidade da marcha; analisar as diferenças cinemáticas obtidas durante a marcha em Lokomat, bem como as forças de interacção entre cada usuário e o dispositivo robótico; perceber como actua a organização modular do sistema nervoso envolvida na sincronização da actividade muscular durante a marcha em dispositivos robóticos. Apenas indivíduos saudáveis participaram neste estudo. Assim, este estudo gerou uma base de comparação para futuras estratégias de controlo utilizadas em reabilitação motora. Os resultados foram bastante animadores e permitam-nos formular novas estratégias de reabilitação motora. No futuro, estas estratégias serão levadas a cabo de modo a que pessoas afectadas por AVCs possam restabelecer mais rapidamente a sua marcha normal