Brett C. Hannigan

Biomedical and Mobile Health Technology Lab

ETH Zürich

Lengghalde 5

8008 Zürich

Switzerland

Hello, I’m Brett! My research interests are applied control theory, electronics, systems and modelling, wearable technology, and biomedical engineering.

My current position is as a Scientific Assistant at ETH Zürich’s BMHT Lab. The group primarily works on textile sensor technologies with the goals to enable personalized health, guide rehabilitation, and improve athletic performance. Recently, we have developed a new type of textile strain sensor that uses an auxetic structure to exceed the theoretical maximum sensitivity for capacitive strain sensors. We used this sensor in a prototype garment combined with custom readout electronics and a machine learning algorithm to demonstrate distributed sensing in textiles—the ability to monitor shoulder, elbow, and wrist movements along one continuous fibre sensor with a single connection. This is important because it reduces the number of unreliable connections in “smart textiles” and allows them to be manufactured at scale using conventional methods. We hope that this can further the adoption of unobtrusive wearable electronics in athletics, rehabilitation, occupational health, consumer electronics, and other fields.

I completed my master’s degree at the University of British Columbia in the Electrical and Computer Engineering department. My thesis research was on applying control theory to improve performance and stability of sigma-delta modulators as part of the Digital Health Innovation Lab team. At the same time, I did a Mitacs Accelerate Fellowship to translate this research with an industry partner, ESS Technology.

I previously worked as a software developer at Lungpacer Medical, while it was still a spin-off from Simon Fraser University in Burnaby, B.C., Canada. Lungpacer is an innovative medical device start-up commercializing minimally invasive diaphragm pacing to improve weaning in mechanically ventilated patients. There I wrote software for experimental data collection and designed algorithms for the control of diaphragm stimulation. I also do some contract work, primarily in signal processing, data analysis, and software development applied to biomedical engineering. Feel free to contact me for any potential collaborations or consultation requests.

Please enjoy some of my project posts, code, and publications. I also enjoy many outdoors sports such as skiing, mountaineering, fishing, and cycling. I will try to include some of these activities here as well as my other assorted fun projects. I am in the process of writing up more of these, hopefully they can be helpful!

Latest Posts

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26 January 2024	Mountain Photos of 2023
14 October 2023	Anodizing Aluminum Parts
09 September 2023	Mountain Photos of 2021-2022

Selected Publications

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2024

Distributed Sensing Along Fibres for Smart Clothing

Brett C. Hannigan, Tyler J. Cuthbert, Chakaveh Ahmadizadeh, and 1 more author

Science Advances, 2024

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Textile sensors transform our everyday clothing into a means to track movement and bio-signals in a completely unobtrusive way. One major hindrance to the adoption of “smart” clothing is the difficulty encountered with connections and space when scaling up the number of sensors. There is a lack of research addressing a key limitation in wearable electronics: connections between rigid and textile elements are often unreliable and they require interfacing sensors in a way incompatible with textile mass production methods. We introduce a prototype garment, compact readout circuit, and algorithm to measure localized strain along multiple regions of a fibre. We employ a helical auxetic yarn sensor with tunable sensitivity along its length to selectively respond to strain signals. We demonstrate distributed sensing in clothing, monitoring arm joint angles from a single continuous fibre. Compared to optical motion capture, we achieve around 5° error in reconstructing shoulder, elbow, and wrist joint angles.

2023

Fast, Analytical Method for Structured Identification of SISO RC-Ladder-Type Systems

Brett C. Hannigan, and Carlo Menon

IEEE Transactions on Circuits and Systems II: Express Briefs, 2023

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Structured system identification often requires solving optimization problems that are not deterministic in computation time and may converge to a local optimum. For the case of systems that can be represented as a SISO “RC- ladder” impedance network, this paper presents a closed-form algorithm that can determine the structured state-space matrices and extract the parameters from an arbitrarily transformed system, such as that produced by subspace system identification. This algorithm relies on a modified version of the Lanczos tridiagonalization process and the solving of a least squares problem with dimension equal to the system order. It is fast, deterministic, and successful for systems of low to moderate order. Practical applications include network synthesis, thermal model identification, and distributed sensing reconstruction, where exact RC parameter values must be identified from data.
HACS: Helical Auxetic Yarn Capacitive Sensors that Go Beyond the Theoretical Sensitivity Limit

Tyler J. Cuthbert, Brett C. Hannigan, Alexander Shokurov, and 1 more author

Advanced Materials, 2023

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The development of flexible strain sensors over the past decade has focused on accessing high strain percentages and high sensitivity (i.e. gauge factors). Strain sensors that employ capacitance as the electrical signal to correlate to strain are typically restricted in sensitivity because of the Poisson effect. By employing auxetic structures, the limits of sensitivity for capacitive sensors have been exceeded, which improves the competitiveness of this modality of sensing. We present the employment of helical auxetic yarns, which have yet to be used as capacitive sensors, likely because there is not a straightforward correlation of how the auxetic character would impact sensor sensitivity. We complete the modeling of helical auxetic yarns to provide insight into ideal sensor variables that would enable us to fabricate sensors that can span a wide range of strains, while also possessing auxetic character. We hypothesized that possessing auxetic character would result in higher sensitivity, which did turn out to be partially correct. We found that the heli-cal auxetic yarn capacitive sensors (which we term HACS) response was dependent on the two variables of i) the ratio of diameters between the two conductive fiber elements, and ii) the initial helical lengths. Depending on these variables, we could obtain sensors that responded to strain with increasing or decreasing capacitances. We found a greater auxetic character resulted in larger sensitivities accessible at smaller strains-a characteristic that is not commonly found when accessing high gauge factors. In addition, we obtained the highest sensitivity for auxetic capacitive sensors reported thus far. We propose a mechanism-that is unique to these sensors-of sensor response that explains both the variable capacitance response and the high gauge factors obtained experimentally .

2021

Understanding the Impact of Machine Learning Models on the Performance of Different Flexible Strain Sensor Modalities

Brett C. Hannigan, Tyler J. Cuthbert, Wanhaoyi Geng, and 1 more author

Frontiers in Materials, 2021

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Fibre strain sensors commonly use three major modalities to transduce strain—piezoresistance, capacitance, and inductance. The electrical signal in response to strain differs between these sensing technologies, having varying sensitivity, maximum measurable strain rate, and susceptibility to deleterious effects like hysteresis and drift. The wide variety of sensor materials and strain tracking applications makes it difficult to choose the best sensor modality for a wearable device when considering signal quality, cost, and difficulty of manufacture. Fibre strain sensor samples employing the three sensing mechanisms are fabricated and subjected to strain using a tensile tester. Their mechanical and electrical properties are measured in response to strain profiles designed to exhibit particular shortcomings of sensor behaviour. Using these data, the sensors are compared to identify materials and sensing technologies well suited for different textile motion tracking applications. Several regression models are trained and validated on random strain pattern data, providing guidance for pairing each sensor with a model architecture that compensates for non-ideal effects. A thermoplastic elastomer-core piezoresistive sensor had the highest sensitivity (average gauge factor: 12.6) and a piezoreistive sensor of similar construction with a polyether urethane-urea core had the largest bandwidth, capable of resolving strain rates above 300%/s with 36% signal amplitude attenuation. However, both piezoresistve sensors suffered from larger hysteresis and drift than a coaxial polymer sensor using the capacitive strain sensing mechanism. Machine learning improved the piezoresistive sensors’ root-mean-squared error when tracking a random strain signal by up to 58% while maintaining their high sensitivity, bandwidth, and ease of interfacing electronically.