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Start date: 2022Subject: search.filters.subject.400709 Medical robotics

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    Squeezing through the gut : micro-manufacturing of smart capsule : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in the School of Food and Advanced Technology at Massey University, Palmerston North, New Zealand
    (Massey University, 2025) Allen, Martin Christopher
    Capsule robotics has been an important part of medical evaluation of conditions within the Gastro-intestinal (GI) tract since their inception in 1957. As technology improved, their functionality expanded to explore the entirety of the GI tract such as; taking images; measuring chemical and mechanical properties; delivering drugs; performing biopsies; and retrieving samples of microbiome. This has led to a significant increase in adoption with over 130,000 procedures conducted annually; however, approximately 1,400 of these procedures result in capsule retention, which requires surgery to remove the capsule. This risk is significantly increased, with patients suffering from inflammatory bowel diseases such as Crohn’s disease being approximately 8 times more likely to retain capsules. This thesis investigates how to improve the safety and motility of microrobotic capsules, especially for those with intestinal complications, to ensure equal access to this technology and improve patient outcomes, increasing access to the information needed to better treat these ailments. This project covers multi-disciplinary subject areas ranging from biomedical technology, mechanical characterisation, robotics, and electronics. Including the design and manufacture of capsule exteriors down to the microscale and the development of new testing equipment for them, such as a synthetic intestine tensile testing platform and robotic intestine testing system for quantifying capsule performance inside an intestinal-like environment. In addition, a precise testing procedure is provided with the created equipment so that experiments can be easily replicated and accurate data are collected. The best capsule design determined is a three-dimensional resin printed capsule using surgical guide resin with a six-ridged segmented design. Determined using force response data from pulling capsule designs and measuring the excess power draw to push them through a synthetic intestinal con striction. This also demonstrated the functionality of the testing equipment developed during this research project. In the future, these capsule design considerations are expected to be used to increase the adoption of this field of technology and improve patient outcomes. Also, it is hoped the testing equipment is used and developed further by my research group to improve their respective project outcomes; and by any external group looking to test their capsule prototypes.
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    The development of a steerable needle robot with biomaterials for the application of 3D printing in situ towards in vivo artificial muscles : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Palmerston North, New Zealand
    (Massey University, 2023) Odendaal, Jean Henri
    Additive manufacturing is an emerging and continually growing field of research with great potential in the development of new technologies through which to change the world as we know it. This thesis offers a look from a 3D printing perspective towards the diverse fields of artificial muscle fabrication, bioprinting, polymer chemistry which can effect shape change or other responses under stimuli, as well an in detail investigation into steerable needle robotics and its potential as a mechanism for additive manufacturing. Since the 3D printing of (bio)polymers is generally reserved for the fabrication of structures on beds which are far away from where their intended use is intended, this thesis proposes an approach to 3D printing exactly the polymer that is of interest in the location in which it is intended. This thesis presents the research and development of a flexible steerable needle robot for the application of 3D printing (bio)polymers which could take the form of artificial muscles, bone, nerves, etc. in vivo. This is extremely challenging, however, and the research undertaken is intended towards building the capability to one day in future achieving this goal. Several experiments are presented which explore the characteristics of a custom developed steerable needle robot in application for 3D printing which include: its mechanisms, its control systems, its algorithms to accurately reach a target goal within a presented body, as well as it visualization system. Furthermore, this developed robot is then utilized to ”3D print” a (bio)polymer inside of a prepared phantom body (e.g., gelatine) to fabricate a bio-fiber. While the bio-fibers presented by this thesis are simple and do not react under any stimulus to act as an artificial muscle, there is a further future opportunity identified which could utilize advanced polymer chemistry to in fact achieve this end result. This thesis contributes towards the synthesis of multiple fields of research towards the goal of one day realizing the imagination of science fiction. Namely, the ability to quickly regenerate human tissue without the need for complex surgeries as well as the fabrication of fibers which could form part of artificial limbs or bodies.
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    Pressure sensor array infused mattress with neural network posture classification : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Mechatronics at Massey University, Albany, New Zealand
    (Massey University, 2022) Krull, Matthew
    Sleep is an essential physiological process and is vital for human health and well-being. There is an undesirable trend of poor sleep hygiene, especially insufficient sleep. Sleep monitors can help bring to light this essential nighttime activity. The Comfort Group® (TCG), Australasia’s largest mattress manufacturer seeks to embed a sleep posture monitor into a mattress as this is a platform used in almost every home. A full body posture monitor can provide richer information about the body than most movement-based sleep monitors on the market. There is particular interest in embedding a Pressure Sensor Array into a mattress because of the amount of sleep information that can be extracted from a pressure image. The renewed interest in this already-proven technology comes with the growth of computing power and machine learning algorithms. Today, features such as body shape, limb position, joint angles and even breathing rate can be inferred from the sleeper. However, they are not cost-effective for the consumer market. Here, we demonstrate a cost-effective pressure sensor array that is embedded into a consumer mattress. Basic posture detection of left-lateral, right-lateral and supine is shown using an artificial neural network. An accuracy rate of 99.1% is achieved. Having a cost-effective mattress-infused platform for the consumer will increase sleep hygiene in society and open the doors to a larger dataset for further analyse. For the scientific community, this larger dataset has the potential to produce a higher fidelity of insights into societal sleep.
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    Hand gesture recognition through capacitive sensing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics & Computer Engineering at Massey University, School of Food and Advanced Technology (SF&AT), Auckland, New Zealand
    (Massey University, 2022) Xu, Muqing
    This thesis investigated capacitive sensing-based hand gesture recognition by developing and validating through custom built hardware. We attempted to discover if massed arrays of capacitance sensors can produce a robust system capable of simple hand gesture detection and recognition. The first stage of this research was to build the hardware that performed capacitance sensing. This hardware needs to be sensitive enough to capture minor variations in capacitance values, while also reducing stray capacitance to their minimum. The hardware designed in this stage formed the basis of all the data captured and utilised for subsequent training and testing of machine learning based classifiers. The second stage of this system used mass arrays of capacitance sensor pads to capture frames of hand gestures in the form of low-resolution 2D images. The raw data was then processed to account for random variations and noise present naturally in the surrounding environment. Five different gestures were captured from several test participants and used to train, validate and test the classifiers. Different methods were explored in the recognition and classification stage: initially, simple probabilistic classifiers were used; afterwards, neural networks were used. Two types of neural networks are explored, namely Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN), which are capable of achieving upwards of 92.34 % classification accuracy.