Towards the Development of a Wearable Tremor Suppression Glove

Patients diagnosed with Parkinson’s disease (PD) often associate with tremor. Among other symptoms of PD, tremor is the most aggressive symptom and it is difficult to control with traditional treatments. This thesis presents the assessment of Parkinsonian hand tremor in both the time domain and the frequency domain, the performance of a tremor estimator using different tremor models, and the development of a novel mechatronic transmission system for a wearable tremor suppression device. This transmission system functions as a mechatronic splitter that allows a single power source to support multiple independent applications. Unique features of this transmission system include low power consumption and adjustability in size and weight. Tremor assessment results showed that the hand tremor signal often presents a multi-harmonics pattern. The use of a multi-harmonics tremor model produced a better estimation result than using a monoharmonic tremor model

A Wearable Mechatronic Device for Hand Tremor Monitoring and Suppression: Development and Evaluation

Tremor, one of the most disabling symptoms of Parkinson\u27s disease (PD), significantly affects the quality of life of the individuals who suffer from it. These people live with difficulties with fine motor tasks, such as eating and writing, and suffer from social embarrassment. Traditional medicines are often ineffective, and surgery is highly invasive and risky. The emergence of wearable technology facilitates an externally worn mechatronic tremor suppression device as a potential alternative approach for tremor management. However, no device has been developed for the suppression of finger tremor that has been validated on a human. It has been reported in the literature that tremor can be selectively suppressed by mechanical loading. Therefore, the objectives of this thesis were to develop a wearable tremor suppression device that can suppress tremor at the wrist and the fingers, and to evaluate it on individuals with PD in a pre-clinical trial. To address these objectives, several experiments were performed to quantify hand tremor; an enhanced high-order tremor estimator was developed and evaluated for tremor estimation; and a wearable tremor suppression glove (WTSG) was developed to suppress tremor in the index finger metacarpophalangeal (MCP) joint, the thumb MCP joint, and the wrist. A total of 18 individuals with PD were recruited for characterizing tremor. The frequencies and magnitudes of the linear acceleration, angular velocity, and angular displacement of tremor in the index finger MCP joint, the thumb MCP joint, and the wrist were quantified. The results showed that parkinsonian tremor consists of multiple harmonics, and that the second and third harmonics cannot be ignored. With the knowledge of the tremor characteristics, an enhanced high-order tremor estimator was developed to acquire better tremor estimation accuracy than its lower-order counterpart. In addition, the evaluation of the WTSG was conducted on both a physical tremor simulator and on one individual with PD. The results of the simulation study proved the feasibility of using the WTSG to suppress tremor; and the results of the evaluation on a human subject showed that the WTSG can suppress tremor motion while allowing the user to perform voluntary motions. The WTSG developed as a result of this work has demonstrated the feasibility of managing hand tremor with a mechatronic device, and its validation on a human subject has provided useful insights from the user\u27s perspectives, which facilitate the transition of the WTSG from the lab to the clinic, and eventually to commercial use. Lastly, an evaluation studying the impact of suppressed tremor on unrestricted joints was conducted on 14 individuals with PD. The results showed a significant increase in tremor magnitude in the unrestricted distal joints when the motions of the proximal joints were restricted. The average increase of the tremor magnitude of the index finger MCP joint, the thumb MCP joint, the wrist and the elbow are 54%, 96%, 124%, and 98% for resting tremor, and 50%, 102%, 49%, and 107% for postural tremor, respectively. Such a result provided additional clinical justification for the significance of the development of a wearable mechatronic device for hand tremor management. Although the focus of this thesis is on hand tremor management, the development and evaluation of a full upper-limb tremor suppression device is required as a future step, in order to advance the use of wearable mechatronic devices as one of the valid tremor treatment approaches

Desenvolvimento de sistema de simulação de tremores nas mãos causados pela Doença de Parkinson, visando a substituição de pacientes em testes com dispositivos vestíveis de supressão

A Doença de Parkinson (DP) é uma enfermidade que afeta milhares de pessoas em todo o mundo e caracterizada por ser neurodegenerativa progressiva que afeta o sistema nervoso central. Frequentemente, pessoas com DP sofrem com sintomas relacionados a apatia, depressão, constipação, desordem no sono, perda do olfato e problemas de cognição. No entanto, a doença é mais conhecida pelas implicações ao sistema motor, resultando em movimentos involuntários conhecidos como tremores. A existência destes tremores afeta profundamente os portadores da doença, causando dificuldades motoras e muitas vezes, até as inibem do convívio social. Buscando uma solução não intrusiva, existem trabalhos que realizam a aquisição de dados característicos dos tremores utilizando MEMS, câmeras de vídeo, tecnologias vestíveis, os quais se propõem a identificar padrões nos tremores ou ainda, desenvolver sistemas que façam a supressão dos movimentos involuntários por meio de atuadores posicionados em juntas específicas como o punho e o cotovelo. Assim, com base nos dados já existentes em artigos e na literatura, iniciou-se o desenvolvimento de um sistema mecatrônico baseado na plataforma Stewart, que simule de forma satisfatória os movimentos de tremor de repouso nas mãos causados pela doença de Parkinson. Neste trabalho, primeiramente foi implementado um simulador em ambiente computacional baseado no modelo cinemático e dinâmico da plataforma Stewart, para realizar testes e avaliar se o mecanismo robótico é capaz de replicar tais movimentos. A estrutura da plataforma foi desenhada em ambiente CAD no software Solidworks® e posteriormente, o modelo foi exportado para o software Matlab® utilizando o ambiente do Simulink e a Biblioteca Simscape Multibody, possibilitando estudos do espaço de trabalho, resposta dos motores ao estímulo em uma faixa de frequência de tremores, bem como o torque fornecido pelos motores. Este estudo antecede a construção de uma plataforma real, sendo importante avaliar como a cinemática e dinâmica dos movimentos funcionam durante a execução. O sistema tem como objetivo realizar testes preliminares com tremor sem a necessidade da presença do paciente, possibilitando a simulação da evolução da doença de Parkinson, variações nos movimentos do tremor, tanto em sua forma e amplitude, quanto no espectro da frequência. Durante a realização dos testes, considerando a configuração do simulador utilizada, a plataforma executou os tremores senoidais de 1Hz a 9Hz com coeficiente de correlação de Pearson acima de 0,9. Os resultados demonstram que a plataforma simulada possui capacidade de representação dos tremores de repouso da DP em todos os movimentos do punho. Essa implementação pode ajudar na comparação entre técnicas de supressão, substituir ou reduzir a participação de pacientes em testes de dispositivos supressores e auxiliar no desenvolvimento de novas tecnologias menos invasivas aos pacientes

Real-time Parkinsonian Tremor Signal Identifier Based on Internal Model Principle

Parkinsonian tremor is one of the clinical hallmarks of Parkinson\u27s disease. Since the traditional medical treatments are not effective, many wearable devices are developed to help suppress the tremor. In order to suppress the tremor, a well-designed tremor estimator is needed. Previous tremor estimators treat a 3-D tremor signal as three independent 1-D signals. Moreover, they did not consider the real-life characteristics of tremor signals. For instance, the tremor does not always exist in the postural tremor signal, and the patient\u27s voluntary motion can be included in the kinetic tremor signal. This paper presents a real-time adaptive parkinsonian tremor signal identifier based on the internal model principle and instantaneous Fourier decomposition and tests on tremor signals collected by a special glove from 18 patients. The result showed that our proposed identifier could identify a 3-D tremor signal and have the ability to recognize the presence of tremor and separate the voluntary motion from the tremor signal. We also showed that our proposed identifier could achieve 80%+ in signal identification accuracy and 90%+ in power estimation accuracy in different tremor signals. Finally, we achieved real-time tremor identification in a bench-top tremor simulator

Pathological Tremor as a Mechanical System: Modeling and Control of Artificial Muscle-Based Tremor Suppression

Central nervous system disorders produce the undesired, approximately rhythmic movement of body parts known as pathological tremor. This undesired motion inhibits the patient\u27s ability to perform tasks of daily living and participate in society. Typical treatments are medications and deep brain stimulation surgery, both of which include risks, side effects, and varying efficacy. Since the pathophysiology of tremor is not well understood, empirical investigation drives tremor treatment development. This dissertation explores tremor from a mechanical systems perspective to work towards theory-driven treatment design. The primary negative outcome of pathological tremor is the undesired movement of body parts: mechanically suppressing this motion provides effective tremor treatment by restoring limb function. Unlike typical treatments, the mechanisms for mechanical tremor suppression are well understood: applying joint torques that oppose tremor-producing muscular torques will reduce tremor irrespective of central nervous system pathophysiology. However, a tremor suppression system must also consider voluntary movements. For example, mechanically constraining the arm in a rigid cast eliminates tremor motion, but also eliminates the ability to produce voluntary motions. Indeed, passive mechanical systems typically reduce tremor and voluntary motions equally due to the close proximity of their frequency content. Thus, mechanical tremor suppression requires active actuation to reduce tremor with minimal influence on voluntary motion. However, typical engineering actuators are rigid and bulky, preventing clinical implementations. This dissertation explores dielectric elastomers as tremor suppression actuators to improve clinical implementation potential of mechanical tremor suppression. Dielectric elastomers are often called artificial muscles due to their similar mechanical properties as human muscle; these similarities may enable relatively soft, low-profile implementations. The primary drawback of dielectric elastomers is their relatively low actuation levels compared to typical actuators. This research develops a tremor-active approach to dielectric elastomer-based tremor suppression. In a tremor-active approach, the actuators only actuate to oppose tremor, while the human motor system must overcome the passive actuator dynamics. This approach leverages the low mechanical impedance of dielectric elastomers to overcome their low actuation levels. Simulations with recorded tremor datasets demonstrate excellent and robust tremor suppression performance. Benchtop experiments validate the control approach on a scaled system. Since dielectric elastomers are not yet commercially available, this research quantifies the necessary dielectric elastomer parameters to enable clinical implementations and evaluates the potential of manufacturing approaches in the literature to achieve these parameters. Overall, tremor-active control using dielectric elastomers represents a promising alternative to medications and surgery. Such a system may achieve comparable tremor reduction as medications and deep brain stimulation with minimal risks and greater efficacy, but at the cost of increased patient effort to produce voluntary motions. Parallel advances in scaled dielectric elastomer manufacturing processes and high-voltage power electronics will enable consumer implementations. In addition to tremor suppression, this dissertation investigates the mechanisms of central nervous system tremor generation from a control systems perspective. This research investigates a delay-based model for parkinsonian tremor. Besides tremor, Parkinson\u27s disease generally inhibits movement, with typical symptoms including rigidity, bradykinesia, and increased reaction times. This fact raises the question as to how the same disease produces excessive movement (tremor) despite characteristically inhibiting movement. One possible answer is that excessive central nervous system inhibition produces unaccounted feedback delays that cause instability. This dissertation develops an optimal control model of human motor control with an unaccounted delay between the state estimator and controller. This delay represents the increased inhibition projected from the basal ganglia to the thalamus, delaying signals traveling from the cerebellum (estimator) to the primary motor cortex (controller). Model simulations show increased delays decrease tremor frequency and increase tremor amplitude, consistent with the evolution of tremor as the disease progresses. Simulations that incorporate tremor resetting and random variation in control saturation produce simulated tremor with similar characteristics as recorded tremor. Delay-induced tremor explains the effectiveness of deep brain stimulation in both the thalamus and basal ganglia since both regions contribute to the presence of feedback delay. Clinical evaluation of mechanical tremor suppression may provide clinical evidence for delay-induced tremor: unlike state-independent tremor, suppression of delay-induced tremor increases tremor frequency. Altogether, establishing the mechanisms for tremor generation will facilitate pathways towards improved treatments and cure development

Development of a Wearable Tremor Suppression Glove

Current treatments for parkinsonian tremor, such as medication and brain surgery, have shown varying levels of effectiveness and carry the risk of significant side effects and complications. Studies on wearable tremor suppression devices have shown positive results in the use of mechanical and electrical suppression on tremor management of the upper limbs. Wearable technology for tremor suppression is a promising solution for patients who do not respond to medication and do not present severe enough symptoms to undergo surgery. Available tremor suppression devices are mainly for elbow and wrist tremor. Devices for finger tremor suppression have not been developed despite the fact that finger tremor is also present. In this study, a wearable tremor suppression glove prototype was designed and validated with recorded tremor data from patients with Parkinson\u27s disease. Two validation experiments were conducted to assess the performance of the proposed device when suppressing tremor motion and following voluntary motion. The tremor suppression assessment showed an overall tremor amplitude reduction of 85.0% ±8.1%, and the power reductions for the Ist, 2nd, and 3rd harmonics are 87.9%±.6%, 92.0%±.4%, and 81.7% ±13.0%, respectively. Following voluntary motion was possible with a RMSE of 14.2%±.5% and a correlation coefficient of 0.97±0.01. Both assessments have shown positive results for the validation of the proposed device; however, further work is needed to improve the performance of the proposed device prior to human trials