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Tremor affects millions of people and many patients desire alternative treatment options to medication or neural surgery. Peripheral suppression techniques are gaining greater use, but are currently applied in a trial-and-error method. To optimize these techniques, the muscles most responsible for an individual patient's tremor need to be identified. In this dissertation, I explored two parallel paths that both could aid in identifying muscles responsible for tremor. The first method utilizies measured data and a technique (coherence) that quantifies the frequency dependent correlation between two signals. Using coherence to identify muscles contributing to tremor requires at least two parts: an analysis of how tremor content is shared between muscles, and an analyis between muscle activity and joint/hand motion. The interpretation of the second analysis depends on the results of the first. The second method of identifying responsible muscles uses a mathematical model of the upper limb. With a validated model established techniques can be used to quantify the contribution to the output from each input. However, the accuracy of the model that has been previously used in the Neuromechanics Research Group had not been quantified. To evaluate the accuracy of this model, I used measured muscle activity as the input to generate simulated tremor and compared that to the measured tremor. From the first method, I found that synergistic muscles tend to share tremor content and do so in phase with each other. Therefore, tremor is likely due to a group of muscles rather than a single muscle. Additionally, I observed that the elbow flexor and wrist extensor muscles tended to be most correlated with tremor and should therefore be considered in peripheral suppression techniques. The second method revealed that while this upper-limb model shows potential to predict cases of severe tremor, improved model parameters must be identified through measurement or estimation techniques before the model should be used as it currently over-predicts the tremor.

The number of children with complex medical needs has risen in recent years, due to the increase in medical technology and subsequent increased survival rate of premature infants. This has led to an increasing number of children with complex neurological conditions, such as Cerebral Palsy, being seen by speech-language therapists to address their complex feeding and communication needs in schools (Arvedson 2008). Surface electromyography (sEMG) has been successfully used as a tool to facilitate therapy in adult dysphagia rehabilitation (Huckabee & Cannito 1999), and has been used in studies of dysarthric speech in children with Cerebral Palsy (Marchant, Mc Auliffe & Huckabee 2007). This case series report examines the effect of oral motor control therapy with sEMG biofeedback to increase motor control and inhibit increased muscle tone. Three participants aged 6, 16, and 18 were selected from the population of Kimi Ora Special School. Each of the three participants were offered sixty, twice daily treatment sessions of 30 minutes each focusing on active relaxation, and reducing duration of return to reset after recruitment of the masseter and submental muscles using sEMG biofeedback. After each session each participant was fed a prescribed amount of thin fluid and a range of food textures to encourage generalization of increased control of the submental and masseter muscles during eating and drinking. One participant was withdrawn after 42 sessions, and two participants completed all 60 sessions. Results showed variable improvement in feeding skills, with a notable improvement in anterior food loss. All participants were able to participate fully in the treatment and made significant gains in their ability to control their muscles during treatment sessions which was reflected in the reduction of sEMG amplitudes. This study demonstrated that oral motor control therapy with sEMG is a viable treatment tool, which warrants further larger scale research into its effectiveness.

<div>The aim of this study was to compare a newly developed ultrathin wearable surface electromyography (sEMG) sensors patch (patent pending, inventors: Lee & Malandraki) (i.e., experimental sensors) to commercially available and widely-used sEMG sensors (i.e., conventional sensors) in monitoring submental muscle activity during swallowing in healthy older adults. A randomized crossover design was employed to compare the performance of the experimental sensors with the performance of conventional snap-on sensors. Forty healthy older adults participated (24F; age range 53-85). Participants completed the same experimental protocol with both sensor types in a counterbalanced order. Swallow trials completed with both types of sensors included 5 trials of 5ml and 10ml water swallows. Comparisons were made on: a) signal related factors (i.e., signal-to-noise ratio, baseline amplitude, normalized amplitude of the swallow trials, and duration of sEMG burst during swallow trials); and b) safety and preclinical factors (safety/adverse effects, efficiency, and satisfaction/comfort).</div><div><br></div><div><div>In terms of signal related factors (Aim 1), we hypothesized that the signal-to-noise ratio and baseline amplitude values acquired using the experimental sensors will not be inferior to the ones acquired using the conventional sensors. These hypotheses were tested using non-inferiority tests. Moreover, we hypothesized that the normalized amplitude values and the sEMG burst duration during swallow trials will be comparable/equivalent between the two sensor types. These hypotheses were tested using equivalency tests. In terms of safety and pre-clinical factors</div><div>(Aim 2), we predicted that no adverse effects will be reported after using either type of sensors. We also hypothesized that sensor placement will be more efficient, and satisfaction/comfort level will be higher with the experimental sensors. These hypotheses were tested using paired t-tests.</div></div><div><br></div><div><div>Overall, the findings supported our hypotheses for Aim 1. Results showed that the experimental sensors did not perform inferiorly to the conventional sensors based on signal-tonoise ratio (left sensors: t(39) = 3.95, p <0.0002; right sensors: t(39) = 2.66, <i>p <0.0056</i>) and baseline amplitude values (left sensors: t(39) = -7.72, p <<i>0.0001</i>; right sensors: t(39) = -7.43, <i>p</i><<i>0.0001</i>). The normalized amplitude values were deemed equivalent for all swallow trials (5ml left: t_u = 4.25, t_l = -6.22; overall <i>p-value <0.0001</i>; 5ml right: t_u = 2.07, t_l = -4.06; overall <i>p-value <0.0224</i>; 10ml left: t_u = 5.49, t_l = -7.20; overall <i>p-value <0.0001</i>; 10ml right: t_u = 3.36 t_l = -5.28; overall <i>p-value <0.0012</i>).The duration of sEMG burst was also deemed equivalent for all variables (5ml left: t_u = 9.48, t_l = -7.25; overall <i>p-value <0.0001</i>; 5ml right: t_u = 9.03, t_l = -6.35; overall <i>p-value <0.0001</i>; 10ml left: t_u = 6.75, t_l = -6.11; <i>p-value <0.0001</i>; 10ml right: t_u = 6.58, t_l = -6.23; overall <i>p-value < 0.0001</i>).</div></div><div><br></div><div><div>In terms of safety and adverse effects (Aim 2, hypothesis #1), mild redness and itchiness occurred with the conventional sensors in six participants, whereas only one participant reported itchiness with the experimental sensors. No redness or skin irritation was observed or reported by any of the participants after the removal of the experimental sensors. In terms of time efficiency of electrode placement (Aim 2, hypothesis #2), our hypothesis was not proven, as there were no statistically significant differences in the time it took to place both sensor types; (t(39) = 1.87, <i>p= 0.9657</i>). However, as hypothesized (Aim 2, hypothesis #3) satisfaction/comfort level was significantly higher with the experimental sensors than the conventional ones, albeit with a relatively small effect size, t(39) = 1.71, <i>p = 0.0476</i>, <i>d = 0.226</i>.</div></div><div><br></div><div><div>Taken together, these findings indicate that the newly developed ultrathin wearable sEMG sensors obtain comparable signal quality and signal parameters to conventional and widely used sEMG snap-on electrodes; have fewer adverse effects associated with them compared to the conventional sensors, and healthy older adults are highly satisfied and comfortable using them. Future research is warranted to optimize the wearable sEMG sensors, before clinical trials examining the effectiveness of these sensors in the treatment of dysphagia can be initiated.</div></div>

The relationship between sEMG signals and muscle force, and associated joint torque, is an object of study for clinical applications such as rehabilitation robotics and commercial applications as wearable motion control devices. The information type and quality obtained by sEMG can impact the classification and prediction accuracy of ankle joint torque. In this thesis project, HD-sEMG based data was collected together with ankle joint torque measurements from 5 subjects during MVIC of plantarflexors and dorsiflexors. Machine learning approaches ideally suited for nonlinear regression tasks, such as MLP and LSTM, have been implemented and evaluated to best predict joint torque profiles given extracted features from sEMG data. An evaluation of machine learning performances using HD-sEMG data over bipolar sEMG data has been conducted in intra-session, inter-subjective and intra-subjective study cases.

Les hallucinations auditives verbales (HAVs) sont des perceptions langagières en l'absence de stimuli externes appropriés. Elles sont un des symptômes les plus invalidants dans la schizophrénie. Parmi les grands types de modèles explicatifs, deux sont particulièrement intéressants : les modèles à origine perceptive, selon lesquels les voix entendues seraient dues à une imagerie mentale et des représentations auditives trop vives et les modèles à origine productive, selon lesquels la parole intérieure est perturbée de telle sorte que les propres pensées verbales du patient sont attribuées à un agent externe. Pour tester le versant moteur des modèles productifs, une expérience de recueil de traces oro-faciales lors des HAVs à l'aide de l'électromyographie de surface a été conduite auprès de 11 patients schizophrènes. Les résultats montrent une tendance à l'augmentation de l'activité musculaire de l'orbiculaire inférieur lors des HAVs par rapport à une condition de repos. Pour tester le versant cérébral des modèles, une expérience en IRMf de génération de pensée verbale et de perception auditive a été menée auprès de 19 sujets schizophrènes et 24 sujets contrôles et a montré une hyper-activation d'un réseau impliquant le cortex temporal et le cortex cingulaire antérieur. La caractérisation phénoménologique des HAVs vécues par les patients a montré que les HAVs diffèrent de la pensée intérieure typique en ce que les voix entendues peuvent être nombreuses et ne sont pas celle du patient lui-même. Ainsi aucun des deux types de modèles considérés isolément n'est satisfaisant pour expliquer les HAVs. Un modèle intégratif multidimensionnel permettrait de mieux rendre compte de la complexité des HAVs. Il existerait, chez les patients schizophrènes une prédisposition perceptive hyper-active couplée à un système de prédiction défaillant. Les deux dysfonctionnements seraient de plus modulés par des facteurs top-down, de stress et un biais cognitif d'externalisation. / Les hallucinations auditives verbales (HAVs) sont des perceptions langagières en l'absence de stimuli externes appropriés. Elles sont un des symptômes les plus invalidants dans la schizophrénie. Parmi les grands types de modèles explicatifs, deux sont particulièrement intéressants : les modèles à origine perceptive, selon lesquels les voix entendues seraient dues à une imagerie mentale et des représentations auditives trop vives et les modèles à origine productive, selon lesquels la parole intérieure est perturbée de telle sorte que les propres pensées verbales du patient sont attribuées à un agent externe. Pour tester le versant moteur des modèles productifs, une expérience de recueil de traces oro-faciales lors des HAVs à l'aide de l'électromyographie de surface a été conduite auprès de 11 patients schizophrènes. Les résultats montrent une tendance à l'augmentation de l'activité musculaire de l'orbiculaire inférieur lors des HAVs par rapport à une condition de repos. Pour tester le versant cérébral des modèles, une expérience en IRMf de génération de pensée verbale et de perception auditive a été menée auprès de 19 sujets schizophrènes et 24 sujets contrôles et a montré une hyper-activation d'un réseau impliquant le cortex temporal et le cortex cingulaire antérieur. La caractérisation phénoménologique des HAVs vécues par les patients a montré que les HAVs diffèrent de la pensée intérieure typique en ce que les voix entendues peuvent être nombreuses et ne sont pas celle du patient lui-même. Ainsi aucun des deux types de modèles considérés isolément n'est satisfaisant pour expliquer les HAVs. Un modèle intégratif multidimensionnel permettrait de mieux rendre compte de la complexité des HAVs. Il existerait, chez les patients schizophrènes une prédisposition perceptive hyper-active couplée à un système de prédiction défaillant. Les deux dysfonctionnements seraient de plus modulés par des facteurs top-down, de stress et un biais cognitif d'externalisation.

Robotic-powered exoskeletons are increasingly used to assist patients with movement disorders in daily life and rehabilitation. Accurately estimating joint torque, especially for dynamic movement conditions using EMG, is crucial for effective assistance. Machine learning and deep learning have been employed for EMG-based force/torque estimation, but their precision and robustness have been limited, particularly for dynamic movements. This thesis aims at comparing and analyze the results using MLP, CNN, and CNN-LSTM methods to estimate ankle joint torque in dynamic movements based on HD-EMG. Meanwhile, this thesis designed and tested different data augmentation to enhance the performance using HD-EMG data augmentation techniques. The CNN-LSTM model demonstrated superior performance among the machine learning models. Additionally, the combination of spatial and signal augmentation methods showed notable improvements in the inter-subject case performance of the prediction. No augmentation methods have shown notable improvements in the intra-subject case or inter-session case. / Robotdrivna exoskelett används i allt större utsträckning för att hjälpa patienter med rörelsestörningar i det dagliga livet och rehabiliteringen. Att noggrant uppskatta ledmomentet, särskilt för dynamiska rörelseförhållanden med EMG, är avgörande för effektiv assistans. Maskininlärning och djupinlärning har använts för EMG-baserad kraft/vridmomentuppskattning, men deras precision och robusthet har varit begränsad, särskilt för dynamiska rörelser. Denna avhandling syftar till att jämföra och analysera resultaten med MLP-, CNN- och CNN-LSTM-metoder för att uppskatta fotledsvridmoment i dynamiska rörelser med hjälp av HD-EMG datadrivna modeller. Samtidigt designade och testade denna avhandling olika dataförstärkningar för att förbättra prestandan med hjälp av HD-EMG dataförstärkningstekniker. CNN-LSTM-modellen visade överlägsen prestanda bland maskininlärningsmodellerna. Dessutom visade kombinationen av rumsliga och signalförstärkningsmetoder anmärkningsvärda förbättringar i prediktionens inter-subject case performance. Inga förstärkningsmetoder har visat märkbara förbättringar i fallet inom ämnet eller fallet mellan sessioner.

Modelling of human motion is used in a wide range of applications. An important aspect of accurate representation of human movement is the ability to customize models to account for individual differences. The following work proposes a methodology using Hill-based candidate functions in the Fast Orthogonal Search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force estimation framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface EMG data from three muscles of the upper arm (biceps brachii, brachioradialis and triceps brachii) were recorded from 10 subjects as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint kinematic data (joint angle and angular velocity) were utilized as inputs to the FOS model. The resulting wrist force estimations were found to be more accurate for models utilizing Hill-based candidate functions, than models utilizing candidate functions that were not physiologically relevant. Subject-specific estimates of optimal joint angle were determined via frequency analysis of the selected FOS candidate functions. Subject-specific optimal joint angle estimates demonstrated low variability and fell within the range of angles presented in the literature. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-10-30 01:32:01.606