This course provides students exposure to the breadth and depth of research activities in biomedical engineering, assists in the establishment of a biomedical engineering identity within the student population and externally to the University and to funding agencies, and provides students with the opportunity to present their work in a formal setting and receive feedback (on both presentation style and content) prior to their final defense. The primary goal of this course is to provide practical experience and guidance in the clear, concise oral communication of research results to an audience of educated, although not specialist, peers. This is an essential skill for anyone intending to seek a career in scientific research. The emphasis is different from a group-meeting or conference style talk to a specialist audience, but rather on the skills that are important ultimately for job talks or teaching situations. Another important goal of the series is to provide a broad knowledge of all aspects of research undertaken by other students in BME.

Immunoengineering is an emergent field that has significantly impacted numerous areas in biomedical sciences. This course will provide brief overviews on fundamental immunological concepts that are recurrent in immunoengineering followed by more in depth coverage of vaccines, cancer immunotherapies, and advances in immunological understanding associated with these fields. The course material will draw from both textbook and scientific articles that will be delivered as lectures and group discussions.

This course provides a contemporary sampling of clinical technologies deployed in the continuum of health care. Recent topics include: MRI physics, guided therapeutics, hemodialysis, clinical information technology, human factors engineering in healthcare, infusion therapy and devices, physiological pressures, laser interaction, and medical device tracking. The course focuses on 1) the scientific principles underlying the clinical instrumentation, 2) the clinical applications of the technologies reviewed, and 3) merits and limitations of current technology. Lectures are given by faculty and clinical scientists who are experts in their respective areas. All lectures will take place in the teaching hospitals and may include tours of various instrumentation suites, laboratories, and patient care areas. Students are evaluated on the basis of a midterm and a final exam.

This is a unique course that consists of three components. In the first month, students attend a half dozen didactic lectures introducing the surgical environment, basic instruments, and principles of asepsis. These lectures are given by surgeons and instructors at the Surgical Skills Centre and will include a group observation of a live surgery with play by play commentary. In the second part of the course, students will attend a handful of observerships of live surgeries, at different participating hospitals. These observerships typically range from 4 to 8 hours. Students will be required to document a subset of surgeries, focusing particularly on technologies deployed, underlying scientific principles, their limitations, surgical workflow and ideas/designs for improvement. The final part of the course consists of a project where each student will write a short paper on an engineering topic related to surgery. At the end of the course, students present their projects before an interdisciplinary panel of academic clinical engineers and surgeons.

This course continues from BME1405H and provides a contemporary sampling of clinical technologies deployed in the continuum of health care. Recent topics include: electrosurgery, metabolic measurement technology, magnetoencephalography (MEG) imaging, patient safety, radiotherapy, CT imaging, whole blood analysis, anesthesia technology and rehabilitation technologies. The course focuses on 1) the scientific principles underlying the clinical instrumentation, 2) the clinical applications of the technologies reviewed, and 3) merits and limitations of current technology. Lectures are given by faculty and clinical scientists who are experts in their respective areas. All lectures will take place in the teaching hospitals and may include tours of various instrumentation suites, laboratories, or patient care areas. Students are evaluated based on a midterm and a final exam.

Soft materials like polymers are powerful tools for biomedical engineering and have applications in drug delivery, regenerative medicine, and biomedical devices. Polymer chemistry and structure can dictate material function and can be used to tune the way these materials interact with biological matter. This course introduces polymer material properties including rheology, gelation, solubility, and glassy phase transition in the context of designing materials for biomedical applications. Classes will alternate between lectures that focus on introducing these fundamental polymer concepts and discussion-based classes where we will explore the application of these concepts in innovative biomaterials in the literature.

This course will provide an overview of different research areas in the field of functional DNA nanotechnology and their intersections with the fields of genomics, biophysics, and healthcare. The course is organized into four modules: i) DNA in The Post-Genomics Era, ii) Nucleic Acids as Enzymes and Binders, iii) Molecular Programming with DNA, and iv) Structural DNA nanotechnology. Through these modules, students will familiarize with different biophysical and biochemical properties of our genetic material. Evaluations will be based on in-class participation, paper presentations, reports, and drafting of a mock grant.

This course integrates relevant aspects of physiology, pathology, developmental biology, disease treatment, tissue engineering, and biomedical devices. The first part of the course will stress basic principles in each of these disciplines. The second portion of the course will integrate these disciplines in the context of specific organ systems. For example, the physiology of the cardiovascular system, the development of the system, cardiovascular disease, the relationship between developmental defects and adult disease, current disease treatment, cardiovascular devices, and the current progress in cardiovascular tissue engineering will be presented. The teaching material will be gathered from various textbooks and scientific journals. This course will be delivered in a group discussion format. Whenever possible, experts in the relevant field will teach guest lectures. This integrative approach will be reflected by a problem-based learning approach to testing and a written report.

In this course, the integration of nanotechnology with biomedical research will be discussed. The course is broken up into four sections: 1) properties of materials in the nanometer-scale and their integration with biological systems; 2) fundamental mechanisms of nanostructure assembly for the build-up of biomedical devices; 3) tools and systems for the analysis and characterization of nanoscale materials; and 4) current biomedical applications of nanomaterials.

Protein engineering has advanced significantly with the emergence of new chemical and genetic approaches. These approaches have allowed the modification and recombination of existing proteins to produce novel enzymes with industrial applications and furthermore, they have revealed the mechanisms of protein function. In this course, we will describe the fundamental concepts of engineering proteins with biological applications.

Fluorescence microscopy and associated biophysical methods are integral to many areas of biological research including biomedical engineering, cell biology, and molecular biology. This course covers the theory, mechanics, and application of fluorescent microscopy. Students will gain expertise in basic and advanced quantitative fluorescence microscopy in the context of working with living samples. The course topics include sample preparation (immunofluorescence-, dye-, and fluorescent protein-labeling), multidimensional imaging, confocal microscopy, two-photon microscopy, and other advanced imaging techniques. The course will also cover the associated biophysical methods used to probe live cell dynamics such as fluorescence recovery after photobleaching (FRAP), Förster resonance energy transfer (FRET), and fluorescence correlation spectroscopy (FCS). By centering on applications to living samples, students with gain the theoretical background and practical knowledge to design and implement live cell imaging experiments.

Image analysis has become a central tool in modern biology. While the human eye analyze images, its assessments are often qualitative. Computers provide quantitative, unbiased measurements, and enable the automation of the analysis, leading to a larger number of processed samples and a greater power of downstream statistical tests. In this course, we will discuss the main steps in the analysis of digital images, with an emphasis on different modalities of microscopy data, including confocal, TIRF, and super-resolution. Topics will include image display, filtering, segmentation, mathematical morphology, and measurements. Lectures will be complemented with examples from the current literature. Students will also have the opportunity to develop solutions to the analysis of images from their own research in a final project.

This course is intended to provide an in-depth coverage on the theory, practice, and applications of magnetic resonance imaging (MRI). Topics to be covered include cardiovascular imaging, neuroimaging, body imaging, musculoskeletal imaging, oncological imaging, and cellular and molecular imaging. MRI techniques, pulse sequences, and contrast agents appropriate to different applications will also be described to equip students with practical knowledge for hands-on work. The format will be largely based on lectures and literature readings, with invited speakers as appropriate to provide a clinical perspective.

Rehabilitation and biomedical engineering are closely linked in various aspects and need to be studied together. For example, electrical stimulation and robotics technologies have recently been proven to facilitate rehabilitation outcomes. Knowledge of the state-of-the-art engineering technologies is required for students in biomedical engineering research. Furthermore, developing new technologies that assist rehabilitation requires thorough knowledge of physiological systems and understanding how they link to those technologies. This course will introduce various state-of-the-art technologies in rehabilitation engineering. To cover diverse research topics in the field, expert guest lecturers in each field will be invited. The physiological basis of each technique will be emphasized, to encourage students to understand fundamental principles of each technique and to seek applications in their own areas of research.

Electrical neuromodulation can be defined as the use of electrical nerve stimulation to control the ongoing activity of one or more neural circuits. This course will cover the fundamental topics related to electrical neuromodulation devices, such as the mammalian nervous system, neural excitation predicted by cable theory, principles of neural recording, long-term performance of implanted devices, and advanced techniques for controlling nervous tissue activation. The class will also cover selected literature of important clinical applications of electrical neuromodulation, where each student will present and lead the discussion of assigned papers. Finally, there will be group projects (typically consisting of two students) in which students will be provided a choice of topics to investigate under the guidance of the instructor or graduate students. The project may involve the design and testing of novel methods of nerve stimulation/recording or it may involve the implementation of neural circuits using computer software (e.g., neuron).

Neural signals can be used to diagnose diseases, to investigate the mechanisms by which the nervous system operates, and to control assistive devices. This course will introduce students to state-of-the-art methods in measuring the electrical activity of the nervous system. The biophysical basis of bioelectric recordings will be described, after which data collection and signal processing methods will be discussed for a range of modalities, including: electroencephalography, intracranial recordings, electromyography, electroneurography, and evoked potentials. Applications and examples will be provided for each of the techniques studied, drawn from fields including neuroscience, neurorehabilitation, kinesiology, and neurosurgery. Students will have the opportunity to apply selected methods of the course to a problem in their own areas of research.

This course aims to provide students with practical research and academic skills by: 1) practicing fundamental research questioning, hypothesis generation, and research goals to define an individual research approach; 2) exploring project management and research planning to increase individual productivity; 3) comprehending the philosophy of research and ethical considerations pertaining to biomedical engineering in order to produce high quality research; and 4) disseminating individual work in written and oral formats to translate individual knowledge and to share multidisciplinary research in creative ways. To achieve these aims, aspects of academic communication will be practiced through interactive workshops that cover literature searching, proposal writing, peer review, the visual display of information, and knowledge mobilization/translation. Throughout the semester, independent study will be centred on the student’s own research topic with written, oral, and graphical communication; while team work will explore a multidisciplinary project that encourages the translation of scientific knowledge to broader audiences. Students will develop these skills while learning how to position themselves and their research for employment purposes.

Today's biomedical engineers will encounter many situations where an ability to perform basic computer programming is desirable if not essential. This is a hands-on course, teaching graduate students the basics of coding in the context of different biomedical engineering scenarios. Students will become familiar with the UNIX operating environment, Python scripting, and task automation, in addition to analyzing, modelling, and manipulating data. The class will involve working through practical examples and solving real problems through a mix of online and live workshops. This course requires no previous programming experience.

This course is intended to provide students interested in biomedical research with an introduction to core statistical concepts and methods, including experimental design. The course also provides a good foundation in the use of discovery tools provided by a data analysis and visualization software. The topics covered will include: i) Importance of being uncertain; ii) Error bars; iii) Significance, p-values and t-tests; iv) Power and sample size; v) Visualizing samples with box plots; vi) Comparing samples; vii) Non parametric tests; viii) Designing comparative experiments; ix) Analysis of variance and blocking; x) Replication; xi) Two-factor designs; xii) Association, correlation and causation; xvi) Simple and multiple linear regression; xvii) Regression diagnostics. The concepts will be illustrated with realistic examples that are commonly encountered by biomedical researchers. The main statistical software package used in this course is JMP (installed on your laptop).

In this course, students will learn to apply statistical approaches to efficiently design and analyse bioengineering experiments. The course first briefly reviews some fundamental statistical concepts related to the design and analysis of experiments (statistical distributions, the central limit theorem, linear functions of random variables and error propagation, ANOVA, multiple regression). The main topics covered include: screening designs, full factorial designs, blocking and replication, response surface methods, custom designs, sequential design strategies, non-normal responses and transformations. The students will learn to apply these statistical approaches to solve practical problems in bioengineering, in particular to examine and control the interactions of living systems with molecular and physical factors. They will also be expected to become proficient in the use of statistical software to design experiments and analyse them. Finally, the students will be expected to gain enough knowledge.

The field of Neuromodulation and its therapeutic application is experiencing unprecedented growth. Advanced therapies resulting from the convergence of machine learning, optical interfaces, electronics, mathematics, material sciences, image-guided surgery, neuroscience, and big data analyses are being rapidly developed and deployed. The primary goal of this course is to introduce students to various neuromodulation modalities, provide students with the knowledge to be prepared for research or industrial endeavors in neuromodulation as well as provide hands-on experience performing real brain data analyses. Topics covered include interventions that are non-invasive (i.e., transcranial stimulation, functional electrical stimulation, transcutaneous spinal cord stimulation), invasive (i.e., deep brain stimulation, subcutaneous spinal cord stimulation), emerging (i.e., focused ultrasound, laser interstitial thermal therapy), and pre-clinical (i.e., optogenetics, gene therapy), in the context of movement, psychiatric, pain, and memory disorders.

This course will introduce students to fundamentals of data science with a specific focus on the application of these methods to biological data. The topics covered will include: 1) structures for data representation; 2) data visualization; 3) methods for diagnosing quality of data; 4) detection, interpretation, and removal of outlier data; 5) preprocessing methods including variable transformations, feature selection, and feature detection; 6) methods for supervised learning, including classification and regression; 7) methods for unsupervised learning, including clustering.

This graduate level course will provide in-depth coverage on the state-of-the-art approaches for applying principles of universal design and high reliability organizations to preventing these injuries and illnesses. These principles include topics from the fields of biomechanics, ergonomics, and human factors. In particular, we will focus on the needs of individuals with disabilities and their caregivers, who are likely to experience the greatest negative impacts of injury and illness. You will learn how to prevent injury and illness for yourself, the people you care about and for society.

The course will consist of a combination of lectures from the instructor and student seminar presentations. The evaluation for the course will be project-based. Students will be asked to develop a preventative approach based on the topics discussed and develop a plan for how it could be implemented and/or evaluated (5-page proposal). Each student will present their proposal in an initial 20-minute oral presentation (in weeks 5 and 6) and a final 20-minute oral presentation followed by class discussion (in weeks 11, 12) on their chosen solution. All students will be asked to provide constructive feedback on their peers’ written and oral communication skills and will be evaluated on the quality of their feedback.

Through this course, engineering students will be prepared interacting with robots and develop future innovations in biomedical robotics. The course covers the foundations of robotics for biomedical engineering. Students will learn about applications that range from biomedical lab automation, robot-assisted surgery, mobile and service robots in hospitals, as well as further smart robot types for healthcare purposes. The practical component of the course will allow students to interact and program collaborative robots in UTM's Robot Teaching Lab.

Students will learn foundational concepts of robotics, i.e., forward and inverse kinematics, dynamics, trajectory generation, motion planning and execution for serial robots. Further on, they will learn to program robot motions in a preplanned, teleoperated and collaborative robot-style fashion. They will be familiarized with state-of-the-art methods like active constraints, admittance control, as well as coordinate system transformations through point-based and image-to-physical registration. In their course project, students have the chance to develop a robot application that is centered around their own research project, towards a lab automation task or hard- and software extensions ranging from designing dedicated endeffectors, integrating sensors, or developing AI-based control methods.

Rehabilitation helps people overcome limitations in functioning, activities and participation to promote well-being. Salient challenges in rehabilitation include: ensuring equitable access to services, promoting engagement and sustained behaviour change, providing opportunities for home practice and self-management. Gaming technologies and innovations have immense potential to tackle these challenges. The design of effective gaming technologies for rehabilitation requires integrated knowledge of clinical practice, psychology, and technology, creatively applied. The evaluation of gaming technologies in rehabilitation requires careful consideration of common limitations, for example: small and heterogeneous samples, understanding and integrating divergent data sources. This course will introduce the concept of gamification in the context of rehabilitation. We will discuss topics relevant to the design, testing and translation of gaming technologies for rehabilitation including: introduction to gamification, co-design, and stakeholder engagement, theoretical frameworks, implementation science, and prototype testing with a focus on single case experimental designs and mixed methods. This overview course will introduce students to key theoretical and methodological concepts to support research and development of gaming technologies for rehabilitation.

This seminar/reading and conference course is an interactive course designed to provide graduate students a basic understanding of latest developments in cellular, gene-modified cells and gene therapy, articulating basic science concepts and challenges in process development, scalability, quality control, safety, potency, and meeting evolving regulatory requirements. Each week, we will invite a leading guest speaker to present a seminar on their respective field of research related to cellular and gene therapy, science, manufacturing and regulation. The course is divided into 4 sub-categories: MSCs, iPSCs, Immunotherapy, GMP/Regulatory considerations with 3 lectures on each subcategory. An Invited Speaker will present a seminar on his/her research for 45 minutes followed by an in-depth discussion on two papers assigned by the Guest Speaker. In addition to discussing the papers, the speaker and students will discuss the 2 written questions that each student submitted prior to lecture time. A total of two hours will be dedicated to each session, which will include the seminar by the Guest Speaker and a detailed discussion of an assigned paper and submitted questions, led by the Guest Speaker.

This course provides an opportunity to graduate students with both the breadth and depth in the area of machine learning and deep learning applied to biomedical applications. The course firstly introduces basic machine learning algorithms, including supervised learning (e.g., logistic regression, naïve bayes, decision trees, etc.) and unsupervised learning (k-means, hierarchical clustering). It will be followed by describing other fundamental concepts, such as classifier evaluation and statistical testing to compare classifiers. The next part is the study of different deep learning models (in seminar style) for various biomedical applications that deals with multiple types of data, including and not limited to biosignals, physiological data, environmental data, speech, text, images, and videos. Different types of supervised and unsupervised frameworks for sequential and non-sequential data will be discussed, including Feed-forward neural network, Convolution neural networks, Autoencoders, Long Short-Term Memory, Temporal Convolution Network. In the last part, advanced deep learning architectures applied to biomedical application will be discussed, including Generative Adversarial Network and Contrastive Learning. The course will comprise of programming assignment (2), paper critiques, and a group project (team of 1 to 3 persons).

This course will introduce various aspects of data science in digital health applications in the form of state-of-the-art projects. Students will build a foundation for performing applied data science, machine learning, and artificial intelligence (AI). The course will employ a combination of high-level theory with practical experience. Each team will work on a unique topic in biomedical engineering and will present their results in the form of a journal publication through merging all assignments and presentations during the course work. Therefore, students will achieve hands-on experience in how to perform literature review, data visualization, AI analysis and results interpretation as well as how to prepare a manuscript to publish their results and get reviewers' feedback on their work. Expert guest lecturers from researchers and scientists will be invited to present sample previous or ongoing projects in digital health. Teams of four to five students will choose their projects from the provided themes. This course will provide insights to help students apply theory to real world health examples targeting vulnerable population such as patients and older adults. This course will also provide students with lots of opportunities to use recent innovative sensing and vision technology for projects on prevention, detection, and treatment. We will discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into healthcare safely and ethically.

Cardio-respiratory disorders are among the most common chronic disorders and causes of hospitalization in adults, especially older adults. This course will describe the application of digital technologies to improve access to care and management of chronic cardio-respiratory disorders. It will help students to get a deeper understanding of several conditions, including sleep problems, heart disease, lung disease, and opioid's effect on respiratory control. For each condition, this course includes physiological and clinical descriptions of the condition and the recent advances in digital technologies to manage these disorders such as diagnosis and treatment options. This course also includes the basics of user-centered design and health equity as the foundation of developing technologies to improve access to care.

The goal of this course is to be able to understand the fundamental theories behind the development of biomedical products from idea to commercial release. At the conclusion of this course, students should be able to: 1) understand the theory behind the development of biomedical products from idea to commercial release; 2) apply the theory to critically analyze the relevant processes; 3) integrate the above knowledge with real world examples and solve practical problems; 4) deliver projects in a team through interactions and group projects; and 5) appreciate the translational link between the fundamental concepts of biomedical engineering knowledge and its practical application in the development of commercial medical products, the processing of such products and the design considerations for clinical use of such products. The main themes of the course are: developing proper requirements; design control; regulatory requirements; IEC 60601 medical device standard; risk management (ISO 14971); verification and validation.

The course will emphasize fundamental engineering principles that will allow students the ability to become productive team members and give them the background necessary to assume leadership roles in product development. Guest experts, case studies, and real world examples augment the learning experience. Each theme incorporates fundamental engineering principles that will allow you to work effectively in a medical device company or to bring your own product to market.