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X-WR-CALDESC:Events for Institute of Biomedical Engineering (BME)
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260918T161000
DTEND;TZID=America/Toronto:20260918T161500
DTSTAMP:20260910T163809Z
CREATED:20260909T143103Z
LAST-MODIFIED:20260910T163809Z
UID:10000739-1789747800-1789748100@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Kashfia Mahmood
DESCRIPTION:Graduate Student Seminar Series\nPlease ensure you invite your Principal Investigator by adding their email via the ‘Add Guest’ button and they will also be notified of your presentation.\nLocation: MS2158 – 1 King’s College Circle\nPresentation Title: Development of a Flexible Piezoelectric Osteotome\nSupervisor Name: Dale Podolsky\nYear of Study: 2\nProgram of Study: MASc\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-kashfia-mahmood/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260918T161500
DTEND;TZID=America/Toronto:20260918T162000
DTSTAMP:20260910T163809Z
CREATED:20260909T143103Z
LAST-MODIFIED:20260910T163809Z
UID:10000738-1789748100-1789748400@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Spandan Sengupta
DESCRIPTION:Graduate Student Seminar Series\nPlease ensure you invite your Principal Investigator by adding their email via the ‘Add Guest’ button and they will also be notified of your presentation.\nLocation: MS2158 – 1 King’s College Circle\nPresentation Title: State-Dependent Effects of Neurostimulation in an Recurrent E-I Network\nAbstract:\nDeep Brain Stimulation (DBS) is an established clinical treatment for a variety of neurological dis-\norders\, including Parkinson’s Disease where it has been shown to reduce motor symptoms as well\nas disrupt pathological beta oscillations in the basal ganglia. The mechanisms of action of DBS on\nthe collective activity of neuronal circuits is not fully understood. We use a recurrently-connected\nexcitatory-inhbitory network based on the Brunel network architecture that can produce activity in\na variety of states. Using a model of DBS that can reproduce observed effects such as antidromic\nactivation\, local somatic suppression\, and axonal activation\, we characterize the effect of stimulation\nacross the entire parameter space of the network. We show that the effects of stimulation are de-\npendent on the baseline state of the network\, with the level of beta suppression dependent on the\nlevel of inhibition and the external drive. Specifically\, networks with higher inhibition and lower drive\nshow greater disruption of beta oscillations. We further show that networks in different states are\npreferentially sensitive to different frequencies of stimulation\, suggesting that alternative protocols to\nthe clinically standard high-frequency stimulation may have therapeutic efficacy.\nSupervisor Name: Milad Lankarany\nYear of Study: 2\nProgram of Study: PhD\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-spandan-sengupta/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260918T162000
DTEND;TZID=America/Toronto:20260918T162500
DTSTAMP:20260910T163809Z
CREATED:20260909T143103Z
LAST-MODIFIED:20260910T163809Z
UID:10000741-1789748400-1789748700@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Seyed Pourya Moghadam Kouhi
DESCRIPTION:Graduate Student Seminar Series\nPlease ensure you invite your Principal Investigator by adding their email via the ‘Add Guest’ button and they will also be notified of your presentation.\nLocation: MS2158 – 1 King’s College Circle\nPresentation Title: Multimodal AI Systems for Non-Visual Activity of Daily Living Monitoring in Smart Home\nAbstract:\nMonitoring Activities of Daily Living (ADLs) is fundamental to the development of intelligent healthcare technologies that support aging in place and independent living by characterizing behavioral patterns\, functional ability\, and early indicators of health decline. Continuous monitoring of daily routines can further support the longitudinal assessment of functional ability and early signs of cognitive decline in older adults living independently. Despite significant progress\, ADL monitoring systems still face a trade-off between accuracy\, computational complexity\, and the need for continuous video recording.\nTo address this gap\, this thesis proposes approaches to enable accurate\, non-visual\, and lightweight multimodal ADL monitoring systems suitable for edge deployment. The contributions are organized around three interconnected research components.\nFirst\, we propose a novel lightweight fusion-based architecture\, called EFACT (Attention-Based Cross-Modal Temporal Modeling for Fine-Grained Activity Recognition Using RGB-D Data)\, designed for fine-grained human activity recognition using RGB and depth modalities. Using the Toyota Smarthome Dataset\, we demonstrate that EFACT outperforms state-of-the-art approaches under the established benchmark protocols for held-out participants and camera viewpoints. Additionally\, we show that the integration of quantization techniques (PTQ and QAT) allows EFACT to operate efficiently on resource-constrained platforms.\nThe second contribution of this thesis is the exploration of Non-Intrusive Load Monitoring (NILM)\, or estimating appliance-specific consumption from aggregate smart meter signals\, as a building block for non-visual ADL recognition. We propose EdgeTCNformer\, a lightweight causal framework for appliance-level power and state estimation that supports efficient local inference on residential edge gateways. Evaluation on four diverse datasets demonstrates a favorable accuracy–computational-efficiency trade-off under repeated sample-level and leave-one-house-out protocols\, while zero-shot cross-dataset evaluation exposes substantial limitations under regional and acquisition-domain shift.\nThe third contribution of this thesis is E2R-FuseNet\, an ADL recognition system for residential settings that combines NILM with millimeter-wave (mmWave) radar. We demonstrate that E2R-FuseNet outperforms radar-only systems in terms of accuracy and achieves performance comparable to some state-of-the-art vision-based systems\, while offering computational advantages and eliminating the need for continuous video recording during inference.\nOverall\, the contributions of this thesis collectively advance the development of next-generation intelligent home monitoring technologies\, supporting independent living and enabling scalable\, non-visual healthcare solutions.\nSupervisor Name: Dr. Azadeh Kushki\nYear of Study: 3\nProgram of Study: PhD\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-seyed-pourya-moghadam-kouhi-3/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20260918T162500
DTEND;TZID=America/Toronto:20260918T163000
DTSTAMP:20260910T163809Z
CREATED:20260909T143103Z
LAST-MODIFIED:20260910T163809Z
UID:10000740-1789748700-1789749000@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Rubaina Farin
DESCRIPTION:Graduate Student Seminar Series\nPlease ensure you invite your Principal Investigator by adding their email via the ‘Add Guest’ button and they will also be notified of your presentation.\nLocation: MS2158 – 1 King’s College Circle\nPresentation Title: Contactless In-Bed Posture Detection Using Load Cell Sensors and AI\nSupervisor Name: Dr. Dinesh Kumbhare\nYear of Study: 2\nProgram of Study: MASc\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-rubaina-farin/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
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