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Activities of daily living (ADL) such as writing, eating, and object manipulation are challenging for patients suffering from pathological tremors. Pathological tremors are involuntary, rhythmic, and oscillatory movements that manifest in limbs, the head, and other body parts. Among the existing treatments, mechanical loading through wearable rehabilitation devices is popular for being non-invasive and innocuous to the human body. In particular, a few exoskeletons are developed to actively mitigate pathological tremors in the forearm. While these forearm exoskeletons can effectively suppress tremors, they still require significant improvements in ergonomics to be implemented for ADL applications. The ergonomics of the exoskeleton can be improved via design and motion control pertaining to human biomechanics, which leads to better efficiency, comfort, and safety for the user. The wrist is a complicated biomechanical joint with two coupled degrees of freedom (DOF) pivotal to human manipulation capabilities. Existing exoskeletons either do not provide tremor suppression in all wrist DOFs, or can be restrictive to the natural wrist movement. This motivates us to explore a better exoskeleton solution for wrist tremor suppression. We propose TAWE - a wearable exoskeleton that provides alleviation of pathological tremors in all wrist DOFs. The design adopts a 6-DOF rigid linkage mechanism to ensure unconstrained natural wrist movements, and wearability features without extreme tight-binding or precise positioning for convenient ADL applications. When TAWE is equipped by the user, a closed-kinematic chain is formed between the exoskeleton and the forearm. We analyze the coupled multibody dynamics of the human-exoskeleton system, which reveals a few robotic control problems - (i) The first problem is the identification of the unknown wrist kinematics within the closed kinematic chain. We realize the real-time wrist kinematic identification (WKI) based on a novel ellipsoidal joint model that describes the coupled wrist kinematics, and a sparsity-promoting Extended Kalman Filter for the efficient real-time regression; (ii) The second problem is the exoskeleton motion control for tremor suppression. We design a robust adaptive controller (IO-RAC) based on model reference adaptive control and inverse optimal robust control theories, which can identify the unknown model inertia and load, and provide stable tracking control under disturbance; (iii) The third problem is the estimation of voluntary movement from tremorous motion data for the motion planning of exoskeleton. We develop a lightweight and data-driven voluntary movement estimator (SVR-VME) based on least square support vector regression, which can estimate voluntary movements with real-time signal adaptability and significantly reduced time delay. Simulations and experiments are carried out to test the individual performance of robotic control algorithms proposed in this study, and their combined real-time performance when integrated into the full exoskeleton control system. We also manufacture the prototype of TAWE, which helps us validate the proposed solutions in tremor alleviation exoskeletons. Overall, the design of TAWE meets the expectations in its compliance with natural wrist movement and simple wearability. The exoskeleton control system can execute stably in real-time, identify unknown system kinematics and dynamics, estimate voluntary movements, and suppress tremors in the wrist. The results also indicate a few limitations in the current approaches, which require further investigations and improvements. Finally, the proposed exoskeleton control solutions are developed based on generic formulations, which can be applied to not only TAWE, but also other rehabilitation exoskeletons. / Doctor of Philosophy / Activities of daily living (ADL) such as writing, eating, and object manipulation are challenging for patients suffering from pathological tremors, which affect millions of people worldwide. Tremors are involuntary, rhythmic, and oscillatory movements. In recent years, rehabilitation exoskeletons are developed as non-invasive solutions to pathological tremor alleviation. The wrist is pivotal to human manipulation capabilities. Existing exoskeletons either do not provide tremor suppression in all wrist movements, or can be restrictive to natural wrist movements. To explore a better solution with improved performance and ergonomics, we propose TAWE - a wearable exoskeleton that provides tremor alleviation in full wrist motions. TAWE adopts a high-degree-of-freedom mechanism to ensure unconstrained natural wrist movements, and wearability features for convenient ADL applications. The coupled dynamics between the forearm and TAWE leads to a few robotic control problems. We propose novel real-time robotic control solutions in the identification of unknown wrist kinematics, robust adaptive exoskeleton control for tremor suppression, and voluntary movement estimation for motion planning. Later, simulations and experiments validate the TAWE prototype and its exoskeleton control framework for tremor alleviation, and reveal limitations in the current approaches that require further investigations and improvements. Finally, the proposed exoskeleton control solutions are developed based on generic formulations, which can be applied to not only TAWE, but also other rehabilitation exoskeletons.

Pathological tremor is a common neuromuscular disorder that significantly affects the quality of life for patients worldwide. With recent developments in robotics, rehabilitation exoskeletons serve as one of the solutions to alleviate these tremors. For improved performance of such devices, we need to solve a few problems, which include developing a model for pathological tremors, and a safe control system that can conveniently incorporate constraints on the wrist's range of motion and it's input force/torque. Accurate predictive modeling of tremor signals can be used to provide alleviation from these tremors via various currently available solutions like adaptive deep brain stimulation, electrical stimulation and rehabilitation orthoses. Existing methods are either too general or too simplistic to accurately predict these tremors in the long term, motivating us to explore better modeling of tremors for long-term predictions and analysis. We explore towards the prediction of tremors using artificial neural networks using EMG signals, leveraging the 20- 100 ms of Electromechanical Delay. The kinematics and EMG data of a publicly available Parkinson's tremor dataset is first analyzed, which confirms that the underlying EMGs have similar frequency composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. This is then further extended by adding constraints-based losses in an attempt to further improve the predictions. Then, we explore the application of model-based predictive control (MPC) for the full wrist exoskeleton designed in our lab for the alleviation of tremors. The main motivation for using MPC here relies on its ability to incorporate state and input constraints, which are crucial for the user's safety. We employ a linear MPC methodology, in which the forearm-exoskeleton model is successively linearized at each time sample to obtain a linear state space model, which is then used to obtain the optimal input by minimizing a convex quadratic cost function. This is then integrated with the tremor model developed via BMFLC and neural networks to provide tremor suppression. Simulation studies are provided to demonstrate the effectiveness of the control schemes. The numerical simulations suggest that the MPC framework is capable of accurate trajectory tracking while providing better tremor suppression than a PD controller without using any tremor model, while the neural network model outperforms the frequency-based BMFLC model. The findings could set up for devising physics-based Neural networks for pathological tremor modeling and experimentally evaluate the performance of the developed framework. / Master of Science / Pathological tremors are involuntary, rhythmic, and oscillatory movements of the limbs that affect millions of people worldwide, and daily life activities like writing, eating, and object manipulation are challenging for them. In recent years, rehabilitation exoskeletons have been developed as non-invasive solutions to pathological tremor alleviation. The wrist is pivotal to human manipulation capabilities, and thus, a wrist exoskeleton (TAWE) has been developed in our lab to provide tremor alleviation. For improved performance of such devices, we need to solve a few problems, which include developing a model for pathological tremors, and a safe control system that can conveniently incorporate constraints on the wrist's range of motion and its input force/torque. We propose a deep-learning-based method for accurate modeling of tremors, along with a model predictive control framework for tremor suppression. Simulations and analyses are done to validate the tremor-modeling framework and the control framework of an exoskeleton for the tremor alleviation, and highlight shortcomings in the current methods that call for more research and advancements.

Le tremblement, défini comme un mouvement rythmique involontaire, est un des mouvements anormaux les plus fréquents. Le tremblement n'est pas une pathologie mortelle, mais elle diminue souvent considérablement la qualité de vie de la personne. Les traitements efficaces ne sont pas encore disponibles, puisque les solutions pharmacologiques et chirurgicales souffrent encore de limitations en termes d'efficacité, de risques et de coûts. Une alternative consiste à utiliser des technologies d'assistance, tels que les exosquelettes ou la Stimulation Électrique Fonctionnelle (SEF).Néanmoins, la conception de systèmes actifs de compensation des tremblements présente plusieurs défis. Un tel système doit être capable, par exemple, d'atténuer les tremblements tout en minimisant la fatigue, la douleur et l'inconfort induit. Il doit aussi distinguer entre le tremblement et le mouvement volontaire, afin de réduire les interférences sur les mouvements intentionnels.Cette thèse se concentre donc sur l'évaluation de l'usage de la SEF pour atténuer le tremblement. Une première contribution concerne le développement des modèles neuromusculosquelettiques pour étudier l'influence des boucles réflexes sur la dynamique du tremblement, ainsi que la modulation de l'impédance de l'articulation via la co-contraction induite par la SEF. Un algorithme pour estimer en ligne le tremblement et ses caractéristiques tout en filtrant le mouvement volontaire a été proposé et validé sur patients. Enfin, un système SEF pour atténuer le tremblement basé sur le contrôle d'impédance a été conçu et évalué sur patients, alors qu'une deuxième stratégie en boucle fermée a été testée sur des sujets sains. / Tremor, defined as an involuntary, approximately rhythmic and roughly sinusoidal movement, is one of the most common movement disorders. It is not a life-threatening pathology, but it often decreases significantly the person's quality of life. Today, effective treatments for pathological tremor are not yet available, since current pharmacological and surgical alternatives still present limitations with respect to effectiveness, risks, and costs. A different approach is the use of assistive technologies, such as upper limb exoskeletons or Functional Electrical Stimulation (FES).Nevertheless, the design of active tremor compensation systems based on these technologies presents several challenges. Such a system must be able, for instance, to attenuate tremor while minimizing the induced fatigue, pain, and discomfort. Also, it must be able to distinguish between pathological tremor and voluntary motion, in order to reduce interference on intentional movements.This thesis is focused then in evaluating the use of FES to attenuate the effects of tremor. A first contribution concerns the use of neuromusculoskeletal models to study the effects reflex pathways may produce on tremor dynamics, as well as how FES-induced co-contraction may modulate joint impedance. Also, an online algorithm to estimate tremor and its features while simultaneously filtering voluntary motion has been proposed and validated with tremor patients. Finally, a FES system to attenuate tremor based on impedance control has been designed and evaluated on tremor patients, while a second strategy using closed-loop FES control has been tested on healthy subjects.