Engineering Science - Theses, Dissertations, and other Required Graduate Degree Essays

Receive updates for this collection

Exploratory study to use K-means clustering for gesture selection of force myography upper limb data in participants with cerebral palsy

Author: 
Date created: 
2019-12-04
Abstract: 

Many with Cerebral Palsy (CP) use assistive devices to perform daily activities. A gesture recognition based wearable device can be implemented using force myography (FMG). However, little research has been done regarding gestures to use with populations that exhibit physical disturbances associated with CP. The research presented in this Thesis lays the groundwork for implementing k-means clustering to conduct gesture selection for a FMG wearable device in a clinical setting. The concept was tested with ten healthy participants and then validated in a pilot study with a CP participant. The results from both population studies showed that the k-means clustering is able to determine the ideal gesture subset in a shorter computation time than testing machine learning models with all the possible combinations of gestures. A finally study explored online testing with three healthy participants controlling a line-following robot with the FMG band. Though this work provides the foundation for using the FMG technology to interact with individuals with cerebral palsy, additional studies are required to determine its full potential.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Antenna designs for modern applications and smart radar for fall detection

Date created: 
2019-12-12
Abstract: 

The antenna is the most critical single component of a communication and sensing system. It not only provides the basic transformation between the electrical signals and electromagnetic waves, but also governs the signal-to-noise ratio which limits the performance of the signal processing and the entire system. The antenna design - both construction and pattern - must account for different system requirements. In diversity/MIMO applications, where the goal is to mitigate multipath-induced signal fading (or improve capacity, error rate, range, coverage, and a host of other performance metrics), it is normally beneficial to have broad patterns for matching to the broad angular range of the multipath, and polarization purity is not a priority. In a polarimetric radar, the target is usually within a narrow angular range, and different combinations of polarizations in the transmit and receive provide different information. These connections between the antenna and the signal processing, for different applications, motivate the new designs presented in this dissertation. The first part concerns multi-element designs for diversity and MIMO, used for portable terminals in broadcast, Wi-Fi and cellular systems. Performance evaluation using the patterns and statistical models for the multipath propagation is the key design tool. The von-Mises Fisher distribution is introduced for evaluating the impact of directivity in the multipath. The antenna construction is typically PCB-based since the products must be very low cost and compact. The second part strives for higher directivity using new designs of fixed arrays. These designs include dual-polarization, multiple frequency bands, and circular polarization. The construction is slotted metallic cavities because of the low loss in both the elements and the feed (distribution of power over the aperture), and the potential simplicity of manufacture, given the higher directivity of polarized illumination. The final part discusses new radar signal processing for indoor fall detection. A radar system was developed and tested, and demonstrates the detection of falls, breathing, and other movements, even when a person has fallen and is on the floor. Deep learning algorithms are used with new experiments providing the training data for distinguishing a person from other moving entities such as pets, reducing the false-alarm rate of the fall detection.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Rodney Vaughan
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Design of a wearable to improve single-device motion classification of arm motions

Author: 
Date created: 
2019-11-26
Abstract: 

Inertial Measurement Unit (IMU) based wearable sensors have found common use to track arm activity in daily life. However, classifying a high number of arm motions with single IMU-based systems remains a challenging task. In this study, we propose a single-device wearable which incorporates a thermal sensor and an inertial sensor. The system was evaluated in a study incorporating 11 healthy participants, where 24 different arm motions were recorded and predicted with a machine learning classifier. This study found that 24 arm motions could be classified with 93.55% accuracy. Further, the passive infrared thermal sensor significantly increased classification accuracy from 75% to 93.55% , p=<0.05. The performance of the generalized classifier indicates that the device could classify arm motions on a user without prior training.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Brain vital signs: Towards next generation neurotechnologies for rapid brain function assessments at point-of-care

Author: 
Date created: 
2019-11-18
Abstract: 

Vital signs such as heart rate, blood pressure and body temperature have revolutionized medical care by providing rapidly assessed, physiology-based, non-invasive and easy-to-understand standardized metrics of different body functions. However, no such vital sign exists for the brain; instead, assessments of the brain are largely reliant on surrogate measures such as observations of behaviour or questionnaire-based measurements, which have been shown to be subjective and unreliable. This research aims to fill this key scientific, clinical, and technological gap by developing a brainwave-based technology platform to evaluate ‘vital sign’ metrics for the brain. A series of studies were undertaken to create and demonstrate a ‘brain vital signs’ platform that is capable of assessing a broad spectrum of functions ranging from the lower-level functions (i.e. sensation) to the highest-level cognition domains (i.e. contextual orientation). In particular, the first study focused on development and initial demonstration of the methods and apparatus for the brain vital signs technology; the next study focused on characterizing the brain vital sign responses to ensure scientific validity; the third study focused on creating a previously non-existant neurophysiology-based neural marker capable of capturing contextual orientation – which is the highest level cognitive domain known to be crucial to frontline clinical assessments; and finally, the last study focused on developing an advanced data analytic technique for maximizing signal capture under noisy environments typical of point-of-care evaluation settings. This research represents the first time that a ‘vital sign’-like metric has been developed for the brain that embodies the key characteristics of existing vital signs, enabling brain function measures that are rapid (~5 minute testing time), easy to use, portable, non-invasive, and standardized with automated analysis. Crucially, these vital sign metrics directly measure the brain’s electrical activity and do not depend on any responses from the test participant, thus providing much more objective information about brain function. The development of portable and objective ‘vital sign’-like metrics for the brain not only advances the scientific understanding of brain function through novel metrics like orientation, but also creates significant opportunities for enhancing clinical diagnosis through improved brain function assessments at the point-of-care.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ryan D'Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Blink-related oscillations: Neurotechnology advances that open a new window into brain function

Author: 
Date created: 
2019-11-22
Abstract: 

Although spontaneous blinking has traditionally not been considered to have much importance in cognition, increasing behavioural evidence suggests that blinking is modulated by changes in attentional demand and cognitive load. BROs (BROs) are neurophysiological responses occurring after blinking, and have been postulated to originate in the precuneus region known to be involved in environmental monitoring and awareness. Given the importance of the precuneus in supporting consciousness and awareness, BRO responses represent a potential avenue for evaluating consciousness in brain-injured patients. However, BRO studies to date have been hindered by major limitations that compromised the reliability of the findings, leading to this response having been largely dismissed by the scientific community. The current doctoral research aims to address this by investigating the potential of utilizing BRO-based measures to evaluate brain function. The first two studies in this research investigated the temporal, spectral, and neuroanatomical features of the BRO response in healthy adults using high-temporal- and high-spatial-resolution MEG, in controlled sensory environments and utilizing multiple task conditions including both resting and cognitive loading via mental calculation. The third study developed a novel signal analysis technique for extraction of BRO responses using only few sensors to enable the development of a point-of-care platform for BRO assessment. Results showed that BRO responses strongly activate the bilateral precuneus and other regions including the dorsal and ventral visual processing pathways as well as regions of the ventral attention network. There are also concomitant spectral effects consistent with sensory, episodic memory, and information processing following blinking. Crucially, results show that BROs are cognitively-driven brain responses, and that spontaneous blink instances actually represent innate ‘stimulus events’ that are actively processed by the brain, with the effects being modulated by cognitive loading. Together, these findings suggest that BRO responses engage key neural processes and activate important cortical hubs, and represent a novel and intriguing new window into brain function.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ryan D'Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Automatic analysis of human embryo's early development

Author: 
Date created: 
2019-08-30
Abstract: 

In Vitro Fertilization (IVF) is the most common fertility treatment. In an IVF treatment, embryologists inspect embryos for subjective quality assessment on a daily basis to select one for implantation. There are several biological studies that confirm the correlations between morphological properties of an embryo’s internal structure and its potential in leading to successful implantation. Automated assessment of embryo’s quality enables a more in-depth understanding of such characteristics and their impact on a positive pregnancy outcome. Moreover, automated quality assessment eliminates subjectivity by selecting embryos with the highest implantation potentials. Automatic monitoring and objective quality assessment of human embryo can potentially improve the outcome of IVF process. This can be achieved through unbiased computer-based approaches that can automatically identify various components of an embryo at different growth stages and quantify their characteristics. This dissertation aims to design and develop tools and methodologies for automatic analysis of temporal and morphological aspects of the human embryo’s in vitro development process. Some of these features and components include the number, centroid locations, boundaries of blastomeres, and segmenting regions corresponding to Zona Pellucida, Trophectoderm and Inner Cell Mass. This dissertation takes a crucial step toward achieving automatic embryo quality assessment. The major contributions of this dissertation include the proposal of a novel cell counting and localization method for blastomeres, the development of the first semantic segmentation for blastocyst components outperforming state-of-the-art methods, and the design of the first system to predict implantation outcome from a single blastocyst image outperforming expert embryologists. Experiments are carried out using various criteria to verify the performance of the proposed methods. Furthermore, the methods developed in this Ph.D. dissertation can be utilized to validate various theoretical assumptions about the relationship between morphological and temporal features of the main components of an embryo and the implantation outcome.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Parvaneh Saeedi
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Brain Vital Signs: Towards Next Generation Neurotechnologies for Rapid Brain Function Assessments at Point-of-Care

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2019-11-18
Abstract: 

Vital signs such as heart rate, blood pressure and body temperature have revolutionized medical care by providing rapidly assessed, physiology-based, non-invasive and easy-to-understand standardized metrics of different body functions. However, no such vital sign exists for the brain; instead, assessments of the brain are largely reliant on surrogate measures such as observations of behaviour or questionnaire-based measurements, which have been shown to be subjective and unreliable. This research aims to fill this key scientific, clinical, and technological gap by developing a brainwave-based technology platform to evaluate ‘vital sign’ metrics for the brain. A series of studies were undertaken to create and demonstrate a ‘brain vital signs’ platform that is capable of assessing a broad spectrum of functions ranging from the lower-level functions (i.e. sensation) to the highest-level cognition domains (i.e. contextual orientation). In particular, the first study focused on development and initial demonstration of the methods and apparatus for the brain vital signs technology; the next study focused on characterizing the brain vital sign responses to ensure scientific validity; the third study focused on creating a previously non-existant neurophysiology-based neural marker capable of capturing contextual orientation – which is the highest level cognitive domain known to be crucial to frontline clinical assessments; and finally, the last study focused on developing an advanced data analytic technique for maximizing signal capture under noisy environments typical of point-of-care evaluation settings. This research represents the first time that a ‘vital sign’-like metric has been developed for the brain that embodies the key characteristics of existing vital signs, enabling brain function measures that are rapid (~5 minute testing time), easy to use, portable, non-invasive, and standardized with automated analysis. Crucially, these vital sign metrics directly measure the brain’s electrical activity and do not depend on any responses from the test participant, thus providing much more objective information about brain function. The development of portable and objective ‘vital sign’-like metrics for the brain not only advances the scientific understanding of brain function through novel metrics like orientation, but also creates significant opportunities for enhancing clinical diagnosis through improved brain function assessments at the point-of-care.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ryan D’Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Abstracting OpenCL for Multi-Application Workloads on CPU-FPGA Clusters

Author: 
Date created: 
2019-12-03
Abstract: 

Field-programmable gate arrays (FPGAs) continue to see integration in data centres, where customized hardware accelerators provide improved performance for cloud workloads. However, existing programming models for such environments typically require a manual assignment of application tasks between CPUs and FPGA-based accelerators. Furthermore, coordinating the execution of tasks from multiple applications necessitates the use of a higher-level cluster management system. In this thesis, we present an abstraction model named CFUSE (Cluster Front-end USEr framework), which abstracts the execution target within a heterogeneous cluster. CFUSE allows tasks from multiple applications from unknown workloads to be mapped dynamically to the available CPU and FPGA resources and allows accelerator sharing among applications. We demonstrate CFUSE with an OpenCL-based prototype implementation for a small cluster of Xilinx FPGA development boards. Using this cluster, we execute a variety of multi-application workloads to evaluate three scheduling policies and to determine the relevant scheduling factors for the system.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Lesley Shannon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Practical electroencephalography (EEG) applications in stroke rehabilitation: Towards brain-computer interface (BCI) setup and motor function assessment

Author: 
Date created: 
2019-11-04
Abstract: 

Electroencephalography (EEG) records electrical brain activity typically in a non-invasive manner. Recent literature has shown its potential in stroke rehabilitation, to actively engage stroke survivors in rehabilitation. In Chapter 3 of this thesis, the problems of EEG applications in stroke rehabilitation were firstly identified with a pilot study. Two main challenges were identified, hindering further application of EEG in stroke rehabilitation training. One of the challenges is that the BCI involved rehabilitation process is unsatisfying. Three objectives were derived from this challenge. Firstly, at the beginning of all EEG related stroke rehabilitation training, it is both time and effort consuming to go through data collection and model training for every rehabilitation task. Therefore, in Chapter 4 of the thesis, the possibility of using an EEG model from one type of motor imagery (e.g.: elbow extension and flexion) to classify EEG from other types of motor imagery activities (e.g.: open a drawer) was investigated. Secondly, a novel training method was introduced together with a rehabilitation platform in Chapter 5. The results suggested that the proposed methods in this thesis are feasible and potentially effective. Thirdly, the transition of the offline analysis method to an online control method is one of the major factors that affect BCI performance. However, research particularly focused on the method of filtering the prediction of an online classification is scarce. In Chapter 6, two methods of filtering online classification predictions were proposed and evaluated in a pseudo-online classification paradigm, with the EEG data collected from Chapter 5. The other challenge is related to motor function assessments in rehabilitation. Motor function is generally assessed with standard questionnaire-based assessments. In these assessments, the rater requires the ratee to perform pre-defined movements and gives scores based on the quality of the movements. Therefore, those motor function assessments have inevitable subjective influences on the functional scores. In Chapter 7 of the thesis, the author investigated the possibility of using EEG data to assess motor function. As a preliminary investigation, EEG-based motor function assessments were only investigated for upper-extremity among participants with stroke. The results suggested that EEG data can be used to assess motor function accurately.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

The design of soft fluid filled actuators driven by conductive nylon

Author: 
Date created: 
2019-09-17
Abstract: 

Soft robots have become increasingly prevalent due to their distinct advantages over traditional rigid robots such as high deformability and good impact resistance. However, soft robotics are currently limited by bulky, non-portable methods of actuation. In this study, we propose a soft actuator driven by conductive nylon artificial muscles which is able to produce forces up to 1.2N. By utilizing nylon artificial muscles, the system does not require sizable pumps or compressors for actuation. The proposed actuator is made up of two main components, a sealed bladder filled with air and an arrangement of nylon artificial muscles. The quasi static behavior of the actuator is characterized using established hyper elastic models and validated against experimental results (maximum error of 5.3%). Using these models, a set of design considerations are formulated which outline the achievable torques for various actuator dimensions.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.