A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning on Low-Cost Capacitive Sensor Arrays

Repeated, consistent, and precise gesture performance is a key part of recovery for stroke and other motor-impaired patients. Close professional supervision to these exercises is also essential to ensure proper neuromotor repair, which consumes a large amount of medical resources. Gesture recognition systems are emerging as stay-at-home solutions to this problem, but the best solutions are expensive, and the inexpensive solutions are not universal enough to tackle patient-to-patient variability. While many methods have been studied and implemented, the gesture recognition system designer does not have a strategy to effectively predict the right method to fit the needs of a patient. This thesis establishes such a strategy by outlining the strengths and weaknesses of several spatiotemporal learning architectures combined with deep learning, specifically when low-cost, low-resolution capacitive sensor arrays are used. This is done by testing the immunity and robustness of those architectures to the type of variability that is common among stroke patients, investigating select hyperparameters and their impact on the architectures’ training progressions, and comparing test performance in different applications and scenarios. The models analyzed here are trained on a mixture of high-quality, healthy gestures and personalized, imperfectly performed gestures using a low-cost recognition system

An investigation into the drug release mechanisms of polymeric solid dispersions

Personalised polypills, which includes multiple drugs in a single pill tailored for individual patients, has gained a lot of research interests with the emergence of pharmaceutical 3D printing. A distinct feature of polypill is to be able to release each drug in a controlled manner. However, currently, there are limited tools to aid the design of such solid dosage forms with desired drug release kinetics. In this work, the drug release mechanisms of a wide range of solid dispersions formed using polymers and model drugs covering a wide range of physicochemical properties were investigated to generate a large dataset with an attempt to develop a simulation strategy for achieving a desired drug release profile. Building a dataset and using the dataset toward simulation building the data to be reproducible and reliable. The sources of errors throughout the manufacturing and the performance measurements of 3D printed example solid dosage forms were first investigated to assess the reproducibility and reliability of the experimental data generated to build the dataset. This was the focus of chapter 3. Thereafter, chapter 4 systematically investigated the behaviour of a wide range of pure polymers to enable the prediction of the behaviour of polymer blends. The polymer behaviour studied include hydration, swelling, and erosion. Addition of the drug and investigating the effect on formulation behaviour was the focus of chapter 5. Chapter 6 used statistical approaches such as principal component analysis as a factor reduction technique and K-means clustering to classify the behaviour of the polymer-drug dispersions. These statistical approaches successfully demonstrated that correlating polymer behaviours and drug release profiles can be used to predict the selection of polymer(s) for a given drug to achieve a desired drug release profile. Further upscaling of the dataset is crucial to enhance analysis