Auckland Bioengineering Institute

Facial expressions are a natural behavior, which is already being considered in the area of affective computing. We can use these for implicit interaction to adjust a system’s behavior. Moreover, using facial gestures for an explicit control enables hands-free interaction, which doesn’t interrupt the primary task and thus potentially enables for a peripheral interaction. This type of interaction can, in particular, help people with motor-control difficulties to interact with a computational system. In this research, we would like to investigate the recognition of facial and head related gestures with a glasses prototype that is based on the technology of Capacitive Sensing (CapSense). Our previous studies show that once CapSense is correctly set up, it is a powerful technology to recognize facial activity. A wearable prototype in form of a pair of glasses is supposed to be developed. 

In this project, we are going to explore the vast potential of vibrotactile illusions in touch interaction.  The idea is that with vibrations synchronized to the touch interactions, we can create illusions such as surface friction and deformation.  This would enable Graphical User Interface (GUI) elements such as virtual buttons to have distinct physical touch and feel, allowing for more physically realistic and intuitive interactive experiences with a touch interface.

To facilitate this research, I have already created the first prototype in the form of finger-worn vibrotactile actuators, in collaboration with University of South Australia, with whom I am also seeking to further the collaboration through this project.  Unlike conventional approach that integrates actuators into the touch devices themselves, my prototype attaches the actuators on the fingers, allowing for more mobile and versatile usage scenarios, without the need to physically modify any existing touch devices.  I envision that the outcomes of this summer research project would be easily deployed to existing touch-enabled computing devices, such as smartphones, smart watches, tablets, and computer touchpads.

In the future, we will also look into the potential applications in virtual reality (VR) and augmented reality (AR), where we move from the 2D touch interaction addressed in this SRS research to 3D gestural interactions.

An ideal candidate would need to have strong expertise in designing and building graphical user interfaces, developing web apps; have a curiosity to learn a bit about psychological research methods; and can occasionally tinker with electronics.  Background in computer engineering, computer science, or software engineering is preferred.  Prior experience with the PureData software would also be a great match.

We have developed human thermoregulation models that can predict the temperature profiles at various body segments and parameterisation techniques to adapt the thermoregulation model for different body shapes and sizes.

The make human project enables us to generate human body surface geometries with the same underlying mesh topology. In this project we wish to develop a tool based on the makehuman project to enable users to describe a human surface geometry and to simulate the thermoregulation process under various conditions (for instance, clothing type). We will also recast the thermoregulation system using the bond graph formalism to enable us to couple with other systems (such as circulation, lymph drainage etc.) in the future.

Skilled in solving ODEs, Analytical geometry, QT and python.

Atrial fibrillation (AF) is the most common heart rhythm disturbance in industrialized countries. It is known that atrial anatomical structure is one of the key factors behind electrical propagation patterns during sinus rhythm and arrhythmias. Furthermore, recent clinical studies in AF patients using gadolinium enhanced (GE-MRI) suggest an important role of atrial fibrosis as a structural substrate behind AF and reliable predicator of clinical treatment outcomes. Estimating the structural characteristics across the two atrial chambers is a challenging task in the past due to extremely complex and thin atrial wall and low resolution of human atria acquired clinically. 

This work is made possible by the collaboration with our overseas collaborators for accessing intact human hearts with a history of AF and high resolution GE-MRI. In this project, the student will systematically analyse the most recent human heart imaged using GE-MRI: 1) 3D human atrial myofiber architecture; 2) fibrosis distribution; and 3) Wall thickness variation across the two atrial chambers. Furthermore, the student needs to compare the results from this heart to the other heart we have studied recently. 

This project will be suitable for students interested in imaging, programming and analysis using a computer software, particularly Matlab.

Atrial fibrillation (AF) leads to an irregular and rapid heart rate, and is the most common sustained heart rhythm disturbance. At present, ~25% of New Zealanders who are 40 years old or more will experience AF in their lifetime. Current clinical studies using gadolinium enhanced magnetic resonance imaging (GE-MRI) in patients with AF suggest that atrial fibrosis acts as the substrate behind AF and can be used to predict clinical treatment outcomes.

In this study, we will study ten sets of GE-MRI from patients with AF imaged using a 3T whole-body MRI by our international collaborators. We aim to systematically analyze fibrosis distribution spatially in each patient, and link the performed ablation strategy to individual fibrosis pattern. These cohort clinical GE-MRI data and associated clinical ablation strategies potentially provide a direct approach for understanding the role of fibrosis in AF.

This project will be suitable for students interested in imaging, programming and analysis using a computer software, particularly Matlab.

The aim of this project is to integrate wearable sensors data into a musculoskeletal model to measure lower limb joint motion. Wearable sensors are an inexpensive and easy to use technology that integrate Inertial Measurement Units (IMUs) and Electromyography (EMG). These wearable motion sensors are placed on the lower body segments (Feet, shanks, tights and pelvis) and coupled to a rigid body model in free software OpenSim (simtk.org, Stanford CA) to estimate joint kinematics, dynamics and muscle forces. Predictions of joint motion will be validated against the clinical ‘gold standard’ optical motion capture.
You will be processing the IMU, EMG and optical motion capture data and feed it into a musculoskeletal model (in OpenSim). You will compare joint kinematics and joint moments determined from IMU sensors data and from the traditional optical motion capture.
You will need to have basic programming knowledge either in Matlab or Python. You will learn how to process and analyse IMU, EMG and optical motion capture data. You will learn how to build a model in OpenSim. If you want (and have enough time) you can participate in the gait data collection.

Long QT syndrome (LQTS) is a congenital heart disorder that can lead to sudden cardiac death. It is caused by defects in the genes responsible for the generation of the cardiac action potential and manifests as a prolongation of the QT interval on an electrocardiogram (ECG). Patients with LQTS have a higher risk of cardiac arrhythmias. The most common form of LQTS is caused by a mutation of the KCNQ1 gene (LQTS1) which reduces the function of the ion channel responsible for the slow repolarising K+ current, leading to a longer action potential duration. As a result, patients with LQT1 mutations are particularly susceptible to arrhythmias induced by sympathetic nerve activity (i.e. an increase in heart rate). In this project, patient-specific computational models of cardiac electrophysiology will be developed using data derived from LQTS1 human-induced pluripotent stem cells (iPSC). The models will be used to predict the efficacy of a range of beta-blocker drugs on patients with LQTS1.

Pre-requisites: Good understanding of cardiac physiology; programming skills in Python and/or Matlab.

The movement of our bodies is controlled by coordinated electrical signals. Disruptions in the electrical conduction system can lead to a variety of disorders such as cardiac arrhythmias, locomotion problems, and disordered motility in the gut.

The ABI has an established experimental framework for measuring bioelectric activity in high-resolution (up to 256 electrode locations at 5 mm spacings). Such measurements are able to detect abnormalities in the conduction patterns, frequencies and velocities of the underlying electrical waves.

To augment our existing framework, we seek to develop new methods to: (i) measure electrical activity at different depths through the wall of the gut, (ii) measure the pressure distributions over a distributed surface (for example underneath an electrode array), (iii) simultaneously measure the pressure and electrical patterns from a region of the gut. Our ultimate aim is to simultaneously measure from multiple sensing techniques to help understand the underlying bio-electric activity patterns in the human.

This project would suit a student with interests or skills in:

• “Hands-on” design and practical implementation to medical devices
• Experimental electrophysiology
• Signal processing
• Interests in post-graduate research.
  

The ABI has an established experimental framework for measuring the manoemtry (pressure) patterns in the human colon. These measurements are currently being used to help diagnose and develop treatment strategies for debilitating colonic disorders such as faecal incontinence.

We seek to supplement our understanding of the pressure waves in the colon by also measuring and quantifying the underlying electrical patterns in the colon.

This project will: (i) acquire new electrical recordings from the colon, (ii) adapt signal processing techniques for analysis and (iii) develop 3d visualisation techniques to integrate the display of electrical and manometry recordings.

The project would suit a student with interests or skills in:

• Clinical translation and electrophysiology
• Signal processing (matlab)
• Numerical computation and visualisation
• Postgraduate studies.
  

Simulating the mechanical behaviour of skin using biomechanical models is useful for a wide range of applications from simulating surgical interventions to helping to create more realistic models of the face for the animation industry. Existing biomechanical models of skin require many parameters to describe the complex mechanical behaviour observed in real skin. Attempting to identify these parameters using ad-hoc experimentation protocols often results in non-unique parameter estimates. This situation arises because of difficulties in predicting whether the experimental protocols being applied can provide sufficiently rich information for robustly identifying the model parameters. A model-based design of experiments framework has recently been developed at ABI to help address these issue by determining experimental protocols that maximise the identifiability of the mechanical parameters.

The main aim of this project is apply our model-based design of experiments framework to computational models of skin. The framework will be applied to determine optimal indentation protocols for identifying mechanical properties of skin using existing computational models and a novel micro-robot indenter developed at ABI for applying controlled deformation to skin (Figure 1).

The student would ideally be keen on computer modelling and will develop skills in finite element modelling, design of experiments techniques, nonlinear parameter optimisation, and code development using python and Matlab.

Project Aims

1. Apply the design of experiments framework to existing biomechanical models of skin to determine the optimal indentation trajectories that maximise the identifiability of the mechanical parameters in the model.
2. Perform the optimal indentation protocol on the forearm skin of an individual using the micro-robot (as shown in Figure 1) and identify their individual-specific mechanical parameters using an existing numerical optimisation algorithm.

In an age of instant access to information it can be surprising that most critical medical information is inaccessible on demand. For example, cardiac output, a measure of blood flow from the heart, is critical for the treatment of heart failure patients. Current methods provide expensive and low-rate data as they require catheterisation or otherwise rely on care-centre based medical instrumentation and expertise. A wirelessly implantable flow measurement system could enable low-cost, high-rate data and lead to improved care.

This project will assess the feasibility of using a wireless implant for cardiac output monitoring. Work would begin with a literature review of cardiac output and flow sensing techniques followed by feasibility modelling towards a fully implanted flow sensing system.

This project would suit someone interested in instrumentation, and modelling.

The mechanical contractions of the stomach during digestion are initiated and coordinated by an underlying rhythmic electrical activity, termed ‘slow waves’. Severe symptomatic motility disorders, like gastroparesis, chronic unexplained nausea and vomiting, and functional dyspepsia affect up to 30% of the general population, imparting major clinical, economic, and societal burdens. However, diagnosis and therapy options remain limited, necessitating the development of new therapeutic strategies. These disorders have all been associated with bioelectrical abnormalities (‘dysrhythmias’), yet there is no proven therapy for correcting gastric dysrhythmias. Comparatively, similar electrical dysrhythmias exist in cardiac disease, but are treated by a broad range of proven therapies, including cardiac ablation, where focused energy is delivered to essentially burn the tissue and induce targeted electrical blocks to disrupt dysrhythmias. Initial gastric ablation studies have been performed showing feasibility of this technique.

This project now seeks to develop methods and/or devices to monitor the temperature of gastric tissue during ablation for real-time assessment and optimisation of ablation lesion formation. Methods should be developed for continual temperature measurement from both the serosal (outside) and mucosal (inside) surfaces of the stomach, as well as transmurally through the thickness of the gastric wall.

Specific Aims

1. Review the literature, established methodologies, and state-of-art technology for surface and transmural temperature measurement from physiological tissues.
2. Design and construct a device capable of real-time temperature measurement of the serosal and mucosal surfaces, and transmural gastric wall thickness, during gastric ablation.
3. Develop a software interface to control the device, view real-time data, and save the data for further post-processing and analysis.
4. Perform bench-top testing and validation of the device to validate functionality and efficacy.
5. If time allows, perform in-vivo validation of the device during gastric ablation to quantify the regional and temporal temperature changes of the gastric wall.

Preferred Skills

The student should ideally have a strong interest and/or background in bioinstrumentation, device development, software development, and translational/applied bioengineering.

Offered by Exercise Sciences, Faculty of Science

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Offered by Anatomy and Medical Imaging, Faculty of Medical and Health Sciences

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