Doctor of Philosophy
Trejos, Ana Luisa
Price, Aaron D.
Parkinson’s Disease (PD) is a progressive neurological disease that presents as unintentional and oscillatory muscle movements of the body by alternating antagonistic muscle contractions. Recently, wearable exoskeleton-type therapy devices have shown potential as an alternative tremor management approach, which work by exerting a controllable force on the target joints. Although the presented Wearable Tremor Suppression Devices (WTSDs) have been able to reduce tremor amplitude by up to 90%, they are unsuitable for everyday use due to their size, weight, and power consumption. Therefore, the available WTSDs need to be improved in their design, size, and weight by using artificial muscles instead of traditional actuators, such as electric motors, pneumatic actuators, or hydraulic actuators. Twisted Coiled Actuators (TCAs) made from nylon are promising artificial muscles that can produce all of the desirable properties of human muscles with higher output power than any other smart material. However, to increase the effectiveness of these actuators for use in a lightweight and compact WTSD, there is a need to develop a control system capable of handling their substantially nonlinear behaviours.
In this thesis, new mathematical models are presented to improve the actuation and control systems of tendon-driven WTSDs operated by TCAs. First, a comprehensive kinematic model was derived that considers the configuration of the tendons and the sheaths to compute the tendon length during hand motions. Second, an improved friction model is proposed to calculate the friction force in tendon-driven mechanisms by considering the physical properties of the tendons and the sheaths, and calculating the contact area and the adhesion force between them. Finally, the efficacy of two linear and nonlinear models of TCAs was investigated to quantify the relationship between temperature, force, and displacement in these actuators. The results show that using the proposed models for modelling and characterization of an available WTSD reduces the overall absolute errors by almost 80%. In general, the availability of the proposed models can be helpful in improving the kinetic models, the control system, and the design of soft robotic exoskeletons.
Summary for Lay Audience
Parkinsonian tremor is a symptom of a neurological disorder that often starts with rhythmic shaking of the hands. Traditional medicines do not always work, and surgery is risky. A new approach to managing tremor includes using devices that work by applying forces on the wrist and fingers. Unfortunately, the devices presented in the literature are too heavy and bulky for daily use. None of them are commercially available. The main reason that the available devices are heavy and bulky is that they use traditional motors (actuators) to apply forces. Research recently showed that new actuators made from smart materials could be used to design these devices. Due to the limited space surrounding the human hand, the best way to connect these new actuators to the wrist and fingers is by using cables. However, using the cables makes the control system more complex.
This work aims to develop mathematical models to describe the motion of the new actuators within the device structure. First, a model was created to calculate the cable length during the hand motion. Second, another model was developed to calculate the friction along the cables. Finally, an intelligent model was formulated for the actuators. The obtained results showed that using the proposed models decreases the errors when controlling these wearable devices. Therefore, the results of this thesis will be greatly beneficial to patients who are disabled by tremor of the wrist and fingers. The resulting devices are expected to significantly improve their ability to perform activities of daily living.
Daemi, Parisa, "Modelling of a TCA-driven Wearable Tremor Suppression Device for People with Parkinson’s Disease" (2023). Electronic Thesis and Dissertation Repository. 9109.
Available for download on Friday, December 20, 2024