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Every year, 250,000 people worldwide suffer a spinal cord injury (SCI) that leaves them with chronic paraplegia - permanent loss of ability to move their legs. SCI interrupts axons passing along the spinal cord, thereby isolating motor neurons from brain inputs. To date, there are no effective treatments that can reconnect these interrupted axons. In a recent breakthrough, .NeuroRestore developed the STIMO neuroprosthesis that can restore walking after paralyzing SCI using Epidural Electrical Stimulation (EES) of the lumbar spinal cord. Yet, the calibration of EES requires highly trained personnel and a vast amount of time, and the mechanism by which EES restores movement is not fully understood. In this master thesis, we propose to address this issue using modeling combined with Artificial Neural Networks (ANNs). To do so, we introduce the CyberSpine model to predict EES-induced motor response. The implementation of the model relies on the construction of a multipolar basis of solution of the Poisson equation which is then coupled to an ANN trained against actual data of an implanted STIMO user. Furthermore, we show that our CyberSpine model is particularly well adapted to extract biologically relevant information regarding the efficient connectivity of the patient’s spine. Finally, a user-friendly interactive visualization software is built., Varje år drabbas 250 000 människor i hela världen av en ryggmärgsskada som ger dem kronisk paraplegi - permanent förlust av förmågan att röra benen. Vid en ryggmärgsskada bryts axonerna som passerar längs ryggmärgen, vilket isolerar de motoriska neuronpoolerna från hjärnans ingångar. Hittills finns det inga effektiva behandlingar som kan återansluta dessa avbrutna axoner. NeuroRestore utvecklade nyligen neuroprotesen STIMO som kan återställa gångförmågan efter förlamande ryggmärgsskada med hjälp av epidural elektrisk stimulering (EES) av ländryggmärgen. Kalibreringen av EES-stimuleringar kräver dock högutbildad personal och mycket tid, och den mekanism genom vilken EES återställer rörelse är inte helt klarlagd. I denna masteruppsats föreslår vi att vi tar itu med denna fråga med hjälp av modellering i kombination med artificiell intelligens. För att göra detta introducerar vi CyberSpine-modellen, en modell som kan förutsäga EES-inducerad motorisk respons. Implementeringen av modellen bygger på konstruktionen av en multipolär bas för lösning av Poisson-ekvationen som sedan kopplas till ett artificiellt neuralt nätverk som tränas mot faktiska data från en implanterad STIMO-deltagare. Dessutom visar vi att vår CyberSpine-modell är särskilt väl anpassad för att extrahera biologiskt relevant information om den effektiva anslutningen av patientens ryggrad. Slutligen bygger vi en användarvänlig interaktiv visualiseringsprogramvara.

[Resumen] La rehabilitación es una herramienta esencial que ayuda a las personas a restaurar la movilidad en las extremidades afectadas por diversas afecciones, como enfermedades neurológicas. Las terapias convencionales, que incluyen terapia ocupacional, física y del habla, se han mejorado con nuevas tecnologías, como sistemas robóticos asistidos y juegos de realidad virtual y aumentada, para aumentar la participación y, en consecuencia, la efectividad. Esta investigación se centra en la implementación de un dispositivo portátil de sensores de electromiograma (EMG) de ocho canales, el brazalete Mindrove, para el reconocimiento de gestos. El objetivo es desarrollar un modelo clasificador utilizando el algoritmo de Máquinas de Vectores de Soporte (SVM) para distinguir ocho gestos diferentes de la mano y aplicarlo en un sistema de reconocimiento de gestos. El estudio demuestra la viabilidad de este sistema de reconocimiento y explora la aplicación potencial de esta tecnología en juegos interactivos de Unity para terapia de rehabilitación. Los resultados muestran una precisión prometedora en la clasificación del modelo y se necesita más investigación para abordar los desafíos relacionados con la especificidad del usuario y la precisión del reconocimiento de gestos. El trabajo futuro implica ampliar el repertorio de gestos reconocidos, incorporar datos adicionales del sensor y explorar técnicas de extracción de características más avanzadas para mejorar el rendimiento general del sistema de reconocimiento de gestos en terapias de rehabilitación., [Abstract] Rehabilitation is an essential tool that aids individuals in restoring mobility in limbs affected by various conditions, such as neurological diseases. Conventional therapies, including occupational, physical, and speech therapy, have been improved by new technologies, such as assistive robotic systems, along with virtual and augmented reality games, to enhance engagement and, consequently, effectiveness. This research focuses on implementing an eight-channel electromyogram (EMG) wearable sensor device, Mindrove armband, for gesture recognition. The objective is to develop a classifier model using the Support Vector Machine (SVM) algorithm to distinguish eight different hand gestures and apply it in a gesture recognition system. The study demonstrates the feasibility of this recognition system and explores the potential application of this technology in interactive Unity games for rehabilitation therapy. The results show promising accuracy in model classification, and further research is needed to address challenges related to user specificity and gesture recognition accuracy. Future work involves expanding the repertoire of recognized gestures, incorporating additional sensor data, and exploring more advanced feature extraction techniques to enhance the overall performance of the gesture recognition system in rehabilitation therapies.

Previous studies have identified a 'defensive graded field' in the peripersonal front space where potential threatening stimuli induce stronger blink responses, mainly modulated by top-down mechanisms, which include various factors, such as proximity to the body, stimulus valence, and social cues. However, very little is known about the mechanisms responsible for representation of the back space and the possible role of bottom-up information. By means of acoustic stimuli, we evaluated individuals' representation for front and back space in an ambiguous environment that offered some degree of uncertainty in terms of both distance (close vs. far) and front-back egocentric location of sound sources. We aimed to consider verbal responses about localization of sound sources and EMG data on blink reflex. Results suggested that stimulus distance evaluations were better explained by subjective front-back discrimination, rather than real position. Moreover, blink response data were also better explained by subjective front-back discrimination. Taken together, these findings suggest that the mechanisms that dictate blink response magnitude might also affect sound localization (possible bottom-up mechanism), probably interacting with top-down mechanisms that modulate stimuli location and distance. These findings are interpreted within the defensive peripersonal framework, suggesting a close relationship between bottom-up and top-down mechanisms on spatial representation.

Background In injury prevention or rehabilitation programs, exercises that facilitate enhanced hamstring activity may be beneficial when aiming to enhance knee joint stability during movements in sports with higher risk of acute knee injury. Information about neuromuscular activation of the hamstring muscles in commonly used exercises may improve exercise selection and progression in programs for knee injury prevention or rehabilitation. Purpose To investigate (1) how balance devices with progressing degrees of instability influence the activity of muscles controlling the knee joint in typical balance exercises with different demands on postural control, and (2) if any between-sex differences exist. Study design Cross-sectional study. Methods Twenty habitually active healthy adults (11 males) participated in this cross-sectional study. Single-leg stance, single-leg squat and single-leg landing were performed on the floor and two different balance devices imposing various levels of challenge to postural control. Three-dimensional motion analysis was used to obtain hip and knee joint angles, and as primary outcomes, and peak normalized EMG activity from the hamstrings and quadriceps muscles was measured for comparison between exercises. Results The more challenging in terms of maintaining stable balance the devices were, the higher hamstring muscle activity levels were observed. There was a clear progression across balance devices from single-leg stance to single-leg squat and further to single-leg landing displaying increasing hamstring activity levels. The change in medial hamstring activity across all devices when changing from single-leg squat to single-leg landing was significantly higher for the female participants than for the males reaching a higher level of activity. Conclusion The muscle activity of the hamstrings and quadriceps increased when the motor task was more dynamic. Specifically, single-leg landings were effective in increasing the hamstring mus

This study aimed to investigate the influence of head position on the electromyographical activity of the core muscles during the prone plank exercise. Twenty healthy participants were enrolled in this study. Muscle activation was registered using surface electromyography in the rectus abdominis, external oblique, internal oblique, and the lumbar portion of erector spinae. Three plank conditions were randomly evaluated, varying the position of the cranio-cervical segment during the plank performance (neutral, flexion and extension). The activation of each individual examined muscle and the overall core muscle activity (Total Intensity), as well as the ratings of perceived exertion were analysed with statistical significance set at p < 0.05. Flexion revealed significantly higher Total Intensity values compared to neutral and extension (p < 0.001; effect size (ES) > 0.90). The rectus abdominis presented larger activation in flexion compared to the other variations (p < 0.05; ES > 0.70). Flexion elicited a greater response for both sides of external oblique when compared to neutral and extension, and also for both sides of internal oblique compared to the other conditions (p < 0.05; ES > 0.70). Both flexion and extension reported higher exertion rating values compared to neutral (p < 0.05; ES > 0.90). Head position influenced the electromyographical activation of core musculature, showing highest values when performing the plank in a head flexion.

Postural control depends on attentional resources besides automatic processes. The dualtask paradigm is a possible approach to analyzing the interference and performance between motor and/or cognitive tasks. Various studies showed that, when individuals simultaneously perform two tasks, the postural stability can decline during a dual-task compared with a single-task due to the attentional resources required performing the tasks. However, little is known about the cortical and muscular activity pattern during dual-task performance. Therefore, this study aims to analyze the muscular and prefrontal activity under dual-task performance in healthy young adults. Thirty-four healthy young adults (mean age ± SD = 22.74 ± 3.74 years) were recruited to perform a postural task (standing posture) and a dual-task (maintaining standing posture while performing a cognitive task). Lower-limb muscle activity was bilaterally collected from five muscles using surface electromyography (sEMG), and the co-contraction index (CCI) was also calculated for selected muscle pairings. The oxy- and deoxyhemoglobin concentrations (prefrontal cortex activity) were recorded using functional near-infrared spectroscopy (fNIRS). Data were compared between single- and dual-task performance. Prefrontal activity increased (p < 0.05), and muscle activity decreased in most analyzed muscles (p < 0.05), from the single-task to cognitive dual-task performing. The co-contraction index patterns changed from single- to dual-task conditions in most selected muscle pairs (p < 0.05). We conclude that the cognitive task negatively interfered with motor performance once the muscle activity decreased and the prefrontal cortex activity increased under a dual-task, suggesting that young adults prioritized cognitive task performance, and they allocated more attentional resources to the cognitive task over the motor performance. Understanding the neuromotor changes can help adopt a better clinical practice to prevent inj

Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies

Traditional input methods to interface with computer systems prove to be challenging for individuals with amputations or paralysis. Although several brain-machine interfaces were developed to address this problem, their invasive nature prevents widespread adoption. Alternatively, developing interfaces using non-invasive signals has been shown to be effective but they require large, non-intuitive gestures to function. In this work, we propose a framework to decode the subtle finger movements that occur naturally during typing via analyzing non-invasive EMG signals. Here, we establish synchronized communication with an amplifier to get signal recordings, perform signal preprocessing and utilize deep learning architectures for feature extraction and classification. Our approach achieves a within-session accuracy of up to 89.23% in detecting individual finger movements during a randomized typing task, with an average accuracy of 77.64% across all sessions. The time needed for classification is 4.16 ms per sample, making our framework suitable for real-time operation. Our framework demonstrates the possibility of identifying finger movements during typing in real-time using non-invasive EMG signals and provides a starting point for future work to allow individuals with amputations or disabilities to communicate effectively with computers.

IntroductionThe paralysis that occurs after a spinal cord injury, particularly during the early stages of post-lesion recovery (∼6 weeks), appears to be attributable to the inability to activate motor pools well beyond their motor threshold. In the later stages of recovery, however, the inability to perform a motor task effectively can be attributed to abnormal activation patterns among motor pools, resulting in poor coordination.MethodWe have tested this hypothesis on four adult male Rhesus monkeys (Macaca mulatta), ages 6-10 years, by recording the EMG activity levels and patterns of multiple proximal and distal muscles controlling the upper limb of the Rhesus when performing three tasks requiring different levels of skill before and up to 24 weeks after a lateral hemisection at C7. During the recovery period the animals were provided routine daily care, including access to a large exercise cage (5' × 7' × 10') and tested every 3-4 weeks for each of the three motor tasks.ResultsAt approximately 6-8 weeks the animals were able to begin to step on a treadmill, perform a spring-loaded task with the upper limb, and reaching, grasping, and eating a grape placed on a vertical stick. The predominant changes that occurred, beginning at ∼6-8 weeks of the recovery of these tasks was an elevated level of activation of most motor pools well beyond the pre-lesion level.DiscussionAs the chronic phase progressed there was a slight reduction in the EMG burst amplitudes of some muscles and less incidence of co-contraction of agonists and antagonists, probably contributing to an improved ability to selectively activate motor pools in a more effective temporal pattern. Relative to pre-lesion, however, the EMG patterns even at the initial stages of recovery of successfully performing the different motor tasks, the level of activity of most muscle remained higher. Perhaps the most important concept that emerges from these data is the large combinations of adaptive strategies in the re

Muscle fatigue is a severe problem for elite athletes, and this is due to the long resting times, which can vary. Various mechanisms can cause muscle fatigue which signifies that the specific muscle has reached its maximum force and cannot continue the task. This thesis was about surveying and exploring state-of-the-art methods and systematically, theoretically, and practically testing the applicability and performance of more recent machine learning methods on an existing EMG to muscle fatigue pipeline. Several challenges within the EMG domain exist, such as inadequate data, finding the most suitable model, and how they should be addressed to achieve reliable prediction. This required approaches for addressing these problems by combining and comparing various state-of-the-art methodologies, such as data augmentation techniques for upsampling, spectrogram methods for signal processing, and transfer learning to gain a reliable prediction by various pre-trained CNN models. The approach during this study was to conduct seven experiments consisting of a classification task that aims to predict muscle fatigue in various stages. These stages are divided into 7 classes from 0-6, and higher classes represent a fatigued muscle. In the tabular part of the experiments, the Decision Tree, Random Forest, and Support Vector Machine (SVM) were trained, and the accuracy was determined. A similar approach was made for the spectrogram part, where the signals were converted to spectrogram images, and with a combination of traditional- and intelligent data augmentation techniques, such as noise and DCGAN, the limited dataset was increased. A comparison between the performance of AlexNet, VGG16, DenseNet, and InceptionV3 pre-trained CNN models was made to predict differences in jump heights. The result was evaluated by implementing baseline classifiers on tabular data and pre-trained CNN model classifiers for CWT and STFT spectrograms with and without data augmentation. The evaluation o

Muscle fatigue is a severe problem for elite athletes, and this is due to the long resting times, which can vary. Various mechanisms can cause muscle fatigue which signifies that the specific muscle has reached its maximum force and cannot continue the task. This thesis was about surveying and exploring state-of-the-art methods and systematically, theoretically, and practically testing the applicability and performance of more recent machine learning methods on an existing EMG to muscle fatigue pipeline. Several challenges within the EMG domain exist, such as inadequate data, finding the most suitable model, and how they should be addressed to achieve reliable prediction. This required approaches for addressing these problems by combining and comparing various state-of-the-art methodologies, such as data augmentation techniques for upsampling, spectrogram methods for signal processing, and transfer learning to gain a reliable prediction by various pre-trained CNN models. The approach during this study was to conduct seven experiments consisting of a classification task that aims to predict muscle fatigue in various stages. These stages are divided into 7 classes from 0-6, and higher classes represent a fatigued muscle. In the tabular part of the experiments, the Decision Tree, Random Forest, and Support Vector Machine (SVM) were trained, and the accuracy was determined. A similar approach was made for the spectrogram part, where the signals were converted to spectrogram images, and with a combination of traditional- and intelligent data augmentation techniques, such as noise and DCGAN, the limited dataset was increased. A comparison between the performance of AlexNet, VGG16, DenseNet, and InceptionV3 pre-trained CNN models was made to predict differences in jump heights. The result was evaluated by implementing baseline classifiers on tabular data and pre-trained CNN model classifiers for CWT and STFT spectrograms with and without data augmentation. The evaluation o

Evidence is emerging on how whole-body powered exoskeleton (EXO) use impacts users in basic occupational work scenarios, yet our understanding of how users learn to use this complex technology is limited. We explored how novice users adapted to using an EXO during gait. Six novices and five experienced users completed the study. Novices completed an initial training/familiarization gait session, followed by three subsequent gait sessions using the EXO, while experienced users completed one gait session with the EXO. Spatiotemporal gait measures, pelvis and lower limb joint kinematics, muscle activities, EXO torques, and human-EXO interaction forces were measured. Adaptations among novices were most pronounced in spatiotemporal gait measures, followed by joint kinematics, with smaller changes evident in muscle activity and EXO joint torques. Compared to the experienced users, novices exhibited a shorter step length and walked with significantly greater anterior pelvic tilt and less hip extension. Novices also used lower joint torques from the EXO at the hip and knee, and they had greater biceps femoris activity. Overall, our results may suggest that novices exhibited clear progress in learning, but they had not yet adopted motor strategies similar to those of experienced users after the three sessions. We suggest potential future directions to enhance motor adaptations to powered EXO in terms of both training protocols and human-EXO interfaces.

Objective: To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio. Methods: Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG Results: Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations. Conclusions: Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods. Significance: The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Background: The high incidence of anterior cruciate ligament (ACL) injuries in sports suggests an involvement of both biomechanical and neurocognitive risk factors. Athletes are constantly exposed to challenging sports scenarios, which are often characterised by high-intensity movements combined with a multi-stimuli environment and continuous psychological pressure. Post-injury loss of knee proprioception and long-term injury-associated neuroplasticity arguably place an athlete in a disadvantage when coping with such situations when returning to sports (RTS). This is postulated to contribute to a high rate of re-injuries, seen despite achieving RTS eligibility. Psychological factors such as re-injury fears and anxieties are also suggested to influence central sensorimotor processing and to therefore play a role in the generation and control of functional movements. Their assessment is however based on suboptimal tools, particularly when administered to the athletic population. In general, current clinical assessments focus primarily on coarse outcome measures while disregarding aspects such as multi-joint control and the influence of psychological aspects on motor performance. This thesis focuses on the role of proprioception and re-injury anxiety on functional movement control following ACL injury and reconstruction (ACLR), with implications for risks of re-injury. Methods: This thesis is comprised of four cross-sectional studies (Papers I-IV), that stem from two data collections performed in a motion analysis laboratory. Paper I introduces a novel obstacle clearance test aimed to functionally assess proprioception and sensorimotor control. The goal of the test was to cross an obstacle, downward vision occluded, aiming for minimal foot clearance. Individuals following ACLR and rehabilitation were compared to both mildly active uninjured persons (CTRL) and elite athletes (ATH). A kinematic analysis, using 3D motion capture, included estimates of lower limb movement, Bakgrund: Den höga förekomsten av främre korsbandsskador (ACL-skada) i sport tyder på en inblandning av både biomekaniska och neurokognitiva riskfaktorer. Idrottare utsätts ständigt för utmanande idrottsscenarier som ofta kännetecknas av komplexa rörelser i en miljö med många olika stimuli och även psykologisk press. Förlust av knäproprioception efter ligamentskadan och långvariga neuroplastiska förändringar utgör en utmaning för idrottare i sådana situationer efter återgång till idrott (RTS). Detta antas bidra till den höga frekvensen av sekundära skador, trots att man uppfyllt kriterierna för RTS. Psykologiska faktorer som t.ex. rädsla eller oro för sekundära skador antas också påverka central sensorimotorisk bearbetning och därför spela en roll i hur funktionella rörelser genereras och kontrolleras. Bedömning av rädsla för skada är dock en utmaning och baseras vanligen på suboptimala verktyg, särskilt när de används för elitidrottare. Nuvarande kliniska bedömningar utgår främst från grova utfallsmått samtidigt som man bortser från flerledskontroll och psykologiska aspekter på motorisk prestation. Denna avhandling fokuserar på betydelsen av proprioception och rädsla/oro för sekundär skada med avseende på funktionell rörelsekontroll efter ACL-skada som behandlats med rekonstruktion (ACLR) och rehabilitering. Metoder: Avhandlingen består av fyra tvärsnittsstudier (delarbeten I-IV), som härrör från två datainsamlingar utförda i ett rörelseanalyslaboratorium. Delarbete I introducerar ett nytt test där man kliver över ett hinder med så litet avstånd som möjligt och utan information från nedre synfältet. Testet syftar till att funktionellt bedöma proprioception och sensorimotorisk kontroll. Individer med ACLR och rehabilitering jämfördes med två kontrollgrupper av icke-skadade individer; moderat aktiva personer (CTRL) samt elitidrottare (ATH). En kinematisk analys, med 3D motion capture, inkluderade rörelsenoggrannhet, variabilitet och symmetri i nedre extremiteterna. D

In this paper, we face the problem of task classification starting from physiological signals acquired using wearable sensors with experiments in a controlled environment, designed to consider two different age populations: young adults and older adults. Two different scenarios are considered. In the first one, subjects are involved in different cognitive load tasks, while in the second one, space varying conditions are considered, and subjects interact with the environment, changing the walking conditions and avoiding collision with obstacles. Here, we demonstrate that it is possible not only to define classifiers that rely on physiological signals to predict tasks that imply different cognitive loads, but it is also possible to classify both the population group age and the performed task. The whole workflow of data collection and analysis, starting from the experimental protocol, data acquisition, signal denoising, normalization with respect to subject variability, feature extraction and classification is described here. The dataset collected with the experiments together with the codes to extract the features of the physiological signals are made available for the research community.

Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Postprint (published version)

The cortical mechanisms underlying the act of taking a step-including planning, execution, and modification - are not well understood. We hypothesized that oscillatory communication in a parieto-frontal and corticomuscular network is involved in the neural control of visually guided steps. We addressed this hypothesis using source reconstruction and lagged coherence analysis of electroencephalographic and electromyographic recordings during visually guided stepping and 2 control tasks that aimed to investigate processes involved in (i) preparing and taking a step and (ii) adjusting a step based on visual information. Steps were divided into planning, initiation, and execution phases. Taking a step was characterized by an upregulation of beta/gamma coherence within the parieto-frontal network during planning followed by a downregulation of alpha and beta/gamma coherence during initiation and execution. Step modification was characterized by bidirectional modulations of alpha and beta/gamma coherence in the parieto-frontal network during the phases leading up to step execution. Corticomuscular coherence did not exhibit task-related effects. We suggest that these task-related modulations indicate that the brain makes use of communication through coherence in the context of large-scale, whole-body movements, reflecting a process of flexibly fine-tuning inter-regional communication to achieve precision control during human stepping.

Objective: To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio. Methods: Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG Results: Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations. Conclusions: Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods. Significance: The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Objective: To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio. Methods: Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG Results: Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations. Conclusions: Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods. Significance: The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Objective: To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio. Methods: Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG Results: Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations. Conclusions: Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods. Significance: The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Objective: To compare the utility of MUNIX (motor unit number index) with needle EMG in characterizing motor unit (MU) properties in the biceps brachii (BB) muscle in subjects with remote polio. Methods: Thirty subjects suffering from remote polio were investigated with MUNIX and needle EMG, all with Macro EMG and 16 of these subjects with concentric needle EMG Results: Both MUNIX and the needle EMG methods showed abnormal results. Fiber density (FD) was the most sensitive parameter for showing signs of reinnervation. At a group level, the methods showed neurogenic findings, but there was no correlation between the results of the MUNIX and needle EMG investigations. Conclusions: Both MUNIX and needle EMG are valuable methods for measuring neurogenic involvement in the BB muscle. However, there was a lack of correlation between the MUNIX and needle EMG findings. The cause for this missing correlation may be multifactorial as there are several differences between the methods. Significance: The reason for the lack of correlation between the MUNIX and needle EMG results is discussed. By combining the needle and surface recorded methods one can obtain more information on the denervation and reinnervation process compared to using just one of the methods alone.

Background: The high incidence of anterior cruciate ligament (ACL) injuries in sports suggests an involvement of both biomechanical and neurocognitive risk factors. Athletes are constantly exposed to challenging sports scenarios, which are often characterised by high-intensity movements combined with a multi-stimuli environment and continuous psychological pressure. Post-injury loss of knee proprioception and long-term injury-associated neuroplasticity arguably place an athlete in a disadvantage when coping with such situations when returning to sports (RTS). This is postulated to contribute to a high rate of re-injuries, seen despite achieving RTS eligibility. Psychological factors such as re-injury fears and anxieties are also suggested to influence central sensorimotor processing and to therefore play a role in the generation and control of functional movements. Their assessment is however based on suboptimal tools, particularly when administered to the athletic population. In general, current clinical assessments focus primarily on coarse outcome measures while disregarding aspects such as multi-joint control and the influence of psychological aspects on motor performance. This thesis focuses on the role of proprioception and re-injury anxiety on functional movement control following ACL injury and reconstruction (ACLR), with implications for risks of re-injury. Methods: This thesis is comprised of four cross-sectional studies (Papers I-IV), that stem from two data collections performed in a motion analysis laboratory. Paper I introduces a novel obstacle clearance test aimed to functionally assess proprioception and sensorimotor control. The goal of the test was to cross an obstacle, downward vision occluded, aiming for minimal foot clearance. Individuals following ACLR and rehabilitation were compared to both mildly active uninjured persons (CTRL) and elite athletes (ATH). A kinematic analysis, using 3D motion capture, included estimates of lower limb movement, Bakgrund: Den höga förekomsten av främre korsbandsskador (ACL-skada) i sport tyder på en inblandning av både biomekaniska och neurokognitiva riskfaktorer. Idrottare utsätts ständigt för utmanande idrottsscenarier som ofta kännetecknas av komplexa rörelser i en miljö med många olika stimuli och även psykologisk press. Förlust av knäproprioception efter ligamentskadan och långvariga neuroplastiska förändringar utgör en utmaning för idrottare i sådana situationer efter återgång till idrott (RTS). Detta antas bidra till den höga frekvensen av sekundära skador, trots att man uppfyllt kriterierna för RTS. Psykologiska faktorer som t.ex. rädsla eller oro för sekundära skador antas också påverka central sensorimotorisk bearbetning och därför spela en roll i hur funktionella rörelser genereras och kontrolleras. Bedömning av rädsla för skada är dock en utmaning och baseras vanligen på suboptimala verktyg, särskilt när de används för elitidrottare. Nuvarande kliniska bedömningar utgår främst från grova utfallsmått samtidigt som man bortser från flerledskontroll och psykologiska aspekter på motorisk prestation. Denna avhandling fokuserar på betydelsen av proprioception och rädsla/oro för sekundär skada med avseende på funktionell rörelsekontroll efter ACL-skada som behandlats med rekonstruktion (ACLR) och rehabilitering. Metoder: Avhandlingen består av fyra tvärsnittsstudier (delarbeten I-IV), som härrör från två datainsamlingar utförda i ett rörelseanalyslaboratorium. Delarbete I introducerar ett nytt test där man kliver över ett hinder med så litet avstånd som möjligt och utan information från nedre synfältet. Testet syftar till att funktionellt bedöma proprioception och sensorimotorisk kontroll. Individer med ACLR och rehabilitering jämfördes med två kontrollgrupper av icke-skadade individer; moderat aktiva personer (CTRL) samt elitidrottare (ATH). En kinematisk analys, med 3D motion capture, inkluderade rörelsenoggrannhet, variabilitet och symmetri i nedre extremiteterna. D

Background: The high incidence of anterior cruciate ligament (ACL) injuries in sports suggests an involvement of both biomechanical and neurocognitive risk factors. Athletes are constantly exposed to challenging sports scenarios, which are often characterised by high-intensity movements combined with a multi-stimuli environment and continuous psychological pressure. Post-injury loss of knee proprioception and long-term injury-associated neuroplasticity arguably place an athlete in a disadvantage when coping with such situations when returning to sports (RTS). This is postulated to contribute to a high rate of re-injuries, seen despite achieving RTS eligibility. Psychological factors such as re-injury fears and anxieties are also suggested to influence central sensorimotor processing and to therefore play a role in the generation and control of functional movements. Their assessment is however based on suboptimal tools, particularly when administered to the athletic population. In general, current clinical assessments focus primarily on coarse outcome measures while disregarding aspects such as multi-joint control and the influence of psychological aspects on motor performance. This thesis focuses on the role of proprioception and re-injury anxiety on functional movement control following ACL injury and reconstruction (ACLR), with implications for risks of re-injury. Methods: This thesis is comprised of four cross-sectional studies (Papers I-IV), that stem from two data collections performed in a motion analysis laboratory. Paper I introduces a novel obstacle clearance test aimed to functionally assess proprioception and sensorimotor control. The goal of the test was to cross an obstacle, downward vision occluded, aiming for minimal foot clearance. Individuals following ACLR and rehabilitation were compared to both mildly active uninjured persons (CTRL) and elite athletes (ATH). A kinematic analysis, using 3D motion capture, included estimates of lower limb movement, Bakgrund: Den höga förekomsten av främre korsbandsskador (ACL-skada) i sport tyder på en inblandning av både biomekaniska och neurokognitiva riskfaktorer. Idrottare utsätts ständigt för utmanande idrottsscenarier som ofta kännetecknas av komplexa rörelser i en miljö med många olika stimuli och även psykologisk press. Förlust av knäproprioception efter ligamentskadan och långvariga neuroplastiska förändringar utgör en utmaning för idrottare i sådana situationer efter återgång till idrott (RTS). Detta antas bidra till den höga frekvensen av sekundära skador, trots att man uppfyllt kriterierna för RTS. Psykologiska faktorer som t.ex. rädsla eller oro för sekundära skador antas också påverka central sensorimotorisk bearbetning och därför spela en roll i hur funktionella rörelser genereras och kontrolleras. Bedömning av rädsla för skada är dock en utmaning och baseras vanligen på suboptimala verktyg, särskilt när de används för elitidrottare. Nuvarande kliniska bedömningar utgår främst från grova utfallsmått samtidigt som man bortser från flerledskontroll och psykologiska aspekter på motorisk prestation. Denna avhandling fokuserar på betydelsen av proprioception och rädsla/oro för sekundär skada med avseende på funktionell rörelsekontroll efter ACL-skada som behandlats med rekonstruktion (ACLR) och rehabilitering. Metoder: Avhandlingen består av fyra tvärsnittsstudier (delarbeten I-IV), som härrör från två datainsamlingar utförda i ett rörelseanalyslaboratorium. Delarbete I introducerar ett nytt test där man kliver över ett hinder med så litet avstånd som möjligt och utan information från nedre synfältet. Testet syftar till att funktionellt bedöma proprioception och sensorimotorisk kontroll. Individer med ACLR och rehabilitering jämfördes med två kontrollgrupper av icke-skadade individer; moderat aktiva personer (CTRL) samt elitidrottare (ATH). En kinematisk analys, med 3D motion capture, inkluderade rörelsenoggrannhet, variabilitet och symmetri i nedre extremiteterna. D

Background: The high incidence of anterior cruciate ligament (ACL) injuries in sports suggests an involvement of both biomechanical and neurocognitive risk factors. Athletes are constantly exposed to challenging sports scenarios, which are often characterised by high-intensity movements combined with a multi-stimuli environment and continuous psychological pressure. Post-injury loss of knee proprioception and long-term injury-associated neuroplasticity arguably place an athlete in a disadvantage when coping with such situations when returning to sports (RTS). This is postulated to contribute to a high rate of re-injuries, seen despite achieving RTS eligibility. Psychological factors such as re-injury fears and anxieties are also suggested to influence central sensorimotor processing and to therefore play a role in the generation and control of functional movements. Their assessment is however based on suboptimal tools, particularly when administered to the athletic population. In general, current clinical assessments focus primarily on coarse outcome measures while disregarding aspects such as multi-joint control and the influence of psychological aspects on motor performance. This thesis focuses on the role of proprioception and re-injury anxiety on functional movement control following ACL injury and reconstruction (ACLR), with implications for risks of re-injury. Methods: This thesis is comprised of four cross-sectional studies (Papers I-IV), that stem from two data collections performed in a motion analysis laboratory. Paper I introduces a novel obstacle clearance test aimed to functionally assess proprioception and sensorimotor control. The goal of the test was to cross an obstacle, downward vision occluded, aiming for minimal foot clearance. Individuals following ACLR and rehabilitation were compared to both mildly active uninjured persons (CTRL) and elite athletes (ATH). A kinematic analysis, using 3D motion capture, included estimates of lower limb movement, Bakgrund: Den höga förekomsten av främre korsbandsskador (ACL-skada) i sport tyder på en inblandning av både biomekaniska och neurokognitiva riskfaktorer. Idrottare utsätts ständigt för utmanande idrottsscenarier som ofta kännetecknas av komplexa rörelser i en miljö med många olika stimuli och även psykologisk press. Förlust av knäproprioception efter ligamentskadan och långvariga neuroplastiska förändringar utgör en utmaning för idrottare i sådana situationer efter återgång till idrott (RTS). Detta antas bidra till den höga frekvensen av sekundära skador, trots att man uppfyllt kriterierna för RTS. Psykologiska faktorer som t.ex. rädsla eller oro för sekundära skador antas också påverka central sensorimotorisk bearbetning och därför spela en roll i hur funktionella rörelser genereras och kontrolleras. Bedömning av rädsla för skada är dock en utmaning och baseras vanligen på suboptimala verktyg, särskilt när de används för elitidrottare. Nuvarande kliniska bedömningar utgår främst från grova utfallsmått samtidigt som man bortser från flerledskontroll och psykologiska aspekter på motorisk prestation. Denna avhandling fokuserar på betydelsen av proprioception och rädsla/oro för sekundär skada med avseende på funktionell rörelsekontroll efter ACL-skada som behandlats med rekonstruktion (ACLR) och rehabilitering. Metoder: Avhandlingen består av fyra tvärsnittsstudier (delarbeten I-IV), som härrör från två datainsamlingar utförda i ett rörelseanalyslaboratorium. Delarbete I introducerar ett nytt test där man kliver över ett hinder med så litet avstånd som möjligt och utan information från nedre synfältet. Testet syftar till att funktionellt bedöma proprioception och sensorimotorisk kontroll. Individer med ACLR och rehabilitering jämfördes med två kontrollgrupper av icke-skadade individer; moderat aktiva personer (CTRL) samt elitidrottare (ATH). En kinematisk analys, med 3D motion capture, inkluderade rörelsenoggrannhet, variabilitet och symmetri i nedre extremiteterna. D

Background: The high incidence of anterior cruciate ligament (ACL) injuries in sports suggests an involvement of both biomechanical and neurocognitive risk factors. Athletes are constantly exposed to challenging sports scenarios, which are often characterised by high-intensity movements combined with a multi-stimuli environment and continuous psychological pressure. Post-injury loss of knee proprioception and long-term injury-associated neuroplasticity arguably place an athlete in a disadvantage when coping with such situations when returning to sports (RTS). This is postulated to contribute to a high rate of re-injuries, seen despite achieving RTS eligibility. Psychological factors such as re-injury fears and anxieties are also suggested to influence central sensorimotor processing and to therefore play a role in the generation and control of functional movements. Their assessment is however based on suboptimal tools, particularly when administered to the athletic population. In general, current clinical assessments focus primarily on coarse outcome measures while disregarding aspects such as multi-joint control and the influence of psychological aspects on motor performance. This thesis focuses on the role of proprioception and re-injury anxiety on functional movement control following ACL injury and reconstruction (ACLR), with implications for risks of re-injury. Methods: This thesis is comprised of four cross-sectional studies (Papers I-IV), that stem from two data collections performed in a motion analysis laboratory. Paper I introduces a novel obstacle clearance test aimed to functionally assess proprioception and sensorimotor control. The goal of the test was to cross an obstacle, downward vision occluded, aiming for minimal foot clearance. Individuals following ACLR and rehabilitation were compared to both mildly active uninjured persons (CTRL) and elite athletes (ATH). A kinematic analysis, using 3D motion capture, included estimates of lower limb movement, Bakgrund: Den höga förekomsten av främre korsbandsskador (ACL-skada) i sport tyder på en inblandning av både biomekaniska och neurokognitiva riskfaktorer. Idrottare utsätts ständigt för utmanande idrottsscenarier som ofta kännetecknas av komplexa rörelser i en miljö med många olika stimuli och även psykologisk press. Förlust av knäproprioception efter ligamentskadan och långvariga neuroplastiska förändringar utgör en utmaning för idrottare i sådana situationer efter återgång till idrott (RTS). Detta antas bidra till den höga frekvensen av sekundära skador, trots att man uppfyllt kriterierna för RTS. Psykologiska faktorer som t.ex. rädsla eller oro för sekundära skador antas också påverka central sensorimotorisk bearbetning och därför spela en roll i hur funktionella rörelser genereras och kontrolleras. Bedömning av rädsla för skada är dock en utmaning och baseras vanligen på suboptimala verktyg, särskilt när de används för elitidrottare. Nuvarande kliniska bedömningar utgår främst från grova utfallsmått samtidigt som man bortser från flerledskontroll och psykologiska aspekter på motorisk prestation. Denna avhandling fokuserar på betydelsen av proprioception och rädsla/oro för sekundär skada med avseende på funktionell rörelsekontroll efter ACL-skada som behandlats med rekonstruktion (ACLR) och rehabilitering. Metoder: Avhandlingen består av fyra tvärsnittsstudier (delarbeten I-IV), som härrör från två datainsamlingar utförda i ett rörelseanalyslaboratorium. Delarbete I introducerar ett nytt test där man kliver över ett hinder med så litet avstånd som möjligt och utan information från nedre synfältet. Testet syftar till att funktionellt bedöma proprioception och sensorimotorisk kontroll. Individer med ACLR och rehabilitering jämfördes med två kontrollgrupper av icke-skadade individer; moderat aktiva personer (CTRL) samt elitidrottare (ATH). En kinematisk analys, med 3D motion capture, inkluderade rörelsenoggrannhet, variabilitet och symmetri i nedre extremiteterna. D

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique.

Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. Nevertheless, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN (Adaptive Domain Adversarial Neural Network), which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, this work provides the first topological data analysis of EMG-based gesture recognition for the characterization of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. In the later layers, the learned features are inclined to instead adopt a one-vs.-all strategy for a given class. Furthermore, by using convolutional network visualization techniques, it is revealed that learned features actually tend to ignore the most activated channel during contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and han

Usability challenges and social acceptance of textual input in a context of extended realities (XR) motivate the research of novel input modalities. We investigate the fusion of inertial measurement unit (IMU) control and surface electromyography (sEMG) gesture recognition applied to text entry using a QWERTY-layout virtual keyboard. We design, implement and evaluate our proposed multi-modal solution, MyoBoard, which is an IMU-driven and EMG-based text entry system based on the QWERTY keyboard layout for XR headsets. The user can select characters with a combination of arm movements and hand gestures. MyoBoard employs a light-weight convolutional neural network classifier that can be deployed on a mobile device with insignificant inference time. We demonstrate the practicality of interruption-free text entry in mobile scenarios with MyoBoard, by recruiting 12 participants and by testing three sets of grasp micro-gestures in three scenarios - empty hand text input, input holding a pen, and a large object, such as an umbrella. With MyoBoard, users achieve an average text entry rate of 9.33 words per minute (WPM), 8.76 WPM, and 8.35 WPM for the freehand, pen, and umbrella conditions, respectively.

Múltiples lesiones neurológicas, como los Accidentes Cardíacos Vasculares (ACV), provocan una degeneración y/o atrofia de la movilidad en las extremidades. La terapia pasiva se suele usar en los procesos de recuperación tras sufrir este tipo de lesiones, pero los ejercicios de brazos y manos utilizados habitualmente se basan en la realización de actividades de alta intensidad y repetitivos, haciendo que la recuperación sea larga, necesite de mucho trabajo y de la ayuda de varios fisiotera- peutas. En este trabajo se realiza el diseño, montaje, implementación y testeo de un sistema hardware-software basado en exoesqueleto y sensores de captación de movimiento de brazo y manos para su utilización en la rehabilitación de los miembros superiores con el fin de reducir el tiempo de recuperación. El usuario dispondrá de una aplicación que le guiará en todo momento haciendo uso de técnicas activas de gamificación para aumentar la motivación.

Actions can convey information about the affective state of an actor. By the end of the first year, infants show sensitivity to such emotional information in actions. Here, we examined the mechanisms contributing to infants' developing sensitivity to emotional action kinematics. We hypothesized that this sensitivity might rely on two factors: a stable motor representation of the observed action to be able to detect deviations from how it would typically be performed and experience with emotional expressions. The sensitivity of 12-to 13-month-old infants to happy and angry emotional cues in a manual transport action was examined using facial EMG. Infants' own movements when performing an object transport task were assessed using optical motion capture. The infants' caregivers' emotional expressivity was measured using a questionnaire. Negative emotional expressivity of the primary caregiver was significantly related to infants' sensitivity to observed angry actions. There was no evidence for such an association with infants' own motor skill. Overall, our results show that infants' experience with emotions, measured as caregivers' emotional expressivity, may aid infants' discrimination of others' emotions expressed in action kinematics.

ObjectivesFacial paralysis is a debilitating condition with substantial functional and psychological consequences. This feline-model study evaluates whether facial muscles can be selectively activated in acute and chronic implantation of 16-channel multichannel cuff electrodes (MCE).MethodsTwo cats underwent acute terminal MCE implantation experiments, 2 underwent chronic MCE implantation in uninjured facial nerves (FN) and tested for 6 months, and 2 underwent chronic MCE implantation experiments after FN transection injury and tested for 3 months. The MCE were wrapped around the main trunk of the skeletonized FN, and data collection consisted of EMG thresholds, amplitudes, and selectivity of muscle activation.ResultsIn acute experimentation, activation of specific channels (ie, channels 1-3 and 6-8) resulted in selective activation of orbicularis oculi, whereas activation of other channels (ie, channels 4, 5, or 8) led to selective activation of levator auris longus with higher EMG amplitudes. MCE implantation yielded stable and selective facial muscle activation EMG thresholds and amplitudes up to a 5-month period. Modest selective muscle activation was furthermore obtained after a complete transection-reapproximating nerve injury after a 3-month recovery period and implantation reoperation. Chronic implantation of MCE did not lead to fibrosis on histology. Field steering was achieved to activate distinct facial muscles by sending simultaneous subthreshold currents to multiple channels, thus theoretically protecting against nerve damage from chronic electrical stimulation.ConclusionOur proof-of-concept results show the ability of an MCE, supplemented with field steering, to provide a degree of selective facial muscle stimulation in a feline model, even following nerve regeneration after FN injury.Level of evidenceN/A.

Non-traumatic shoulder injuries are common in team handball. However, many athletes continue to throw, despite pain in the shoulder. This study investigated upper body kinematics and muscle activation while throwing in female elite handball players with and without shoulder pain. Thirty female elite team handball players, 15 with pain (age 22.2 ± 2.9 yrs.) and 15 without pain (age 20.4 ± 2.6 yrs.) performed five standing throws in which joint kinematics and muscle activity were measured in the following muscles: pectoralis major, infraspinatus, serratus anterior, latissimus dorsi, and upper-, middle-, and lower trapezius. The main findings revealed that peak joint angles and angular velocities were not different between groups; however, group differences were observed in earlier timing of position and longer time spent in maximal shoulder extension and external shoulder rotation in the pain group compared with the no pain group. The pain group also revealed a significant lower muscle peak activity in the serratus anterior during the cocking phase compared to the no pain group. After the cocking phase and at ball release, the groups had similar activation. In conclusion, the present study showed group differences in appearance and time spent in maximal humerus extension and external rotation and a different serratus anterior muscle peak activity between elite handball players playing with and without shoulder pain, which are identified as possible mechanisms of adaptation to avoid pain.

Robotic exoskeleton technologies are one of the most active fields of robotics in recent years. Exoskeleton systems can give essential support for limb motions with enhanced strength and endurance, and they have a wide variety of therapeutic and supportive utility in life. These technologies have been extensively improved to be utilized for human power enhancement, worker injury prevention, human power assistance, and physical interface in augmented reality. Employees in the manufacturing and construction industries perform especially challenging duties, increasing their risk of health problems, disability, and medical leave, resulting in diminished job competitiveness and a shortage of qualified applicants. The usage of an exoskeleton might decrease muscular peak loads and lessen worker injury risks. This study includes a detailed analysis of employees wearing exoskeletons while doing various job-related duties. In this thesis, the tests assess the benefits of adopting exoskeletons in lowering human muscular activity and, as a result, weariness, and exhaustion. Unlike industrial robots, robotic exoskeleton technologies must be carefully built since they actually interact with actual users. The study used two widely available exoskeletons named Eksovest, an upper-body exoskeleton, and LegX, a lower-body exoskeleton. The study includes five applications: shoulder height weight-lifting, wall drilling, and roof drilling positions for the upper body Eksovest, and virtual chair and knee position for the lower body LegX. This application evaluated electromyography (EMG) signals which were collected using EMG sensors on the human body as supportive tools. Furthermore, the investigations compare the different volunteer’s body muscle data gathered by EMG sensors mounted on biceps, thigh, and calf muscles. The work also evaluates the accuracies of the data collecting procedures used in this study. Based on this study, it is discovered that by employing these exoskeletons may

Non-ergonomic execution of repetitive physical tasks represents a major cause of work-related musculoskeletal disorders (WMSD). This study was focused on the pushing and pulling (P&P) of an industrial handcart (which is a generic physical task present across many industries), with the aim to investigate the dependence of P&P execution on the operators’ psychological status and the presence of pain syndromes of the upper limbs and spine. The developed acquisition system integrated two three-axis force sensors (placed on the left and right arm) and six electromyography (EMG) electrodes (placed on the chest, back, and hand flexor muscles). The conducted experiment involved two groups of participants (with and without increased psychological scores and pain syndromes). Ten force parameters (for both left and right side), one EMG parameter (for three different muscles, both left and right side), and two time-domain parameters were extracted from the acquired signals. Data analysis showed intergroup differences in the examined parameters, especially in force integral values and EMG mean absolute values. To the best of our knowledge, this is the first study that evaluated the composite effects of pain syndromes, spine mobility, and psychological status of the participants on the execution of P&P tasks—concluding that they have a significant impact on the P&P task execution and potentially on the risk of WMSD. The future work will be directed towards the development of a personalized risk assessment system by considering more muscle groups, supplementary data derived from operators’ poses (extracted with computer vision algorithms), and cognitive parameters (extracted with EEG sensors).

En aquest document es presenta el disseny i la implementació d’un electromiògraf, és a dir, un aparell que permet mesurar l’estímul elèctric, o potencial creat en la contracció muscular, per tant, durant el moviment d’un individu. Es presenta el tema de l’electromiografia, l’estudi del potencial creat durant el moviment, explicant en què consisteix i les seves aplicacions, les diferents maneres a partir de les quals es pot obtenir el senyal EMG i, les característiques i l’origen d’aquest senyal. Es detallen les etapes necessàries per al disseny d’un sistema de mesura o d’adquisició de dades, concretament, d’un dedicat a mesurar senyals biomèdiques, les que tenen origen al cos humà. S’uneixen els temes en l’especificació del disseny d’un sistema d’adquisició de senyals EMG, dimensionant les diferents etapes necessàries. Finalment, s’introdueix el tema de Rsaspberry Pi, les seves aplicacions en diferents camps i l’especificació de l’ús a l’aplicació dissenyada., En este documento se presenta el diseño y la implementación de un electromiógrafo, es decir, un sistema que permite medir el estímulo eléctrico, o potencia creado en la contracción muscular, por lo tanto, durante el movimiento de un individuo. Se presenta el tema de la electromiografía, el estudio del potencial generado durante el movimiento, explicando en qué consiste y sus aplicaciones, las distintas maneras a partir de las que se puede obtener la señal EMG y, las características y origen de esta señal. Se detallan las etapas necesarias para el diseño de un sistema de medida o de adquisición de datos, concretamente de uno dedicado a medir señales biomédicas, las que tienen origen en el cuerpo humano. Se unen los temas en la especificación del diseño de un sistema de adquisición de señales EMG, dimensionando las diferentes etapas necesarias.. Por último, se introduce el tema de Raspberry Pi, sus aplicaciones en distintos campos y la especificación del uso en la aplicación diseñada., This document presents the design and implementation of an electromyograph, a system that allows the measuring of the electrical power created in muscle contraction, therefore, during the movement of an individual. It presents the topic of electromyography, the study of the potential created during movement, explaining what it consists of and its applications. Also the different ways an electromyography can be made and the origin and characteristics of its signal. The necessary steps for the design of a data acquisition system are detailed, specifically the one dedicated to measuring biomedical signals, the type of signals that originate in the human body. The concepts are joined specifying the design of an EMG signal acquisition system, with its different stages. Finally, Raspberry Pi is defined with its different implementations and the use in this project.