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X-WR-CALNAME:Institute of Biomedical Engineering (BME)
X-ORIGINAL-URL:https://bme.utoronto.ca
X-WR-CALDESC:Events for Institute of Biomedical Engineering (BME)
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TZID:America/Toronto
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TZOFFSETFROM:-0500
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DTSTART:20240310T070000
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251201T110000
DTEND;TZID=America/Toronto:20251201T140000
DTSTAMP:20251021T131247Z
CREATED:20251021T130834Z
LAST-MODIFIED:20251021T131247Z
UID:10000648-1764586800-1764597600@bme.utoronto.ca
SUMMARY:BME Holiday Party 2025
DESCRIPTION:Institute of Biomedical Engineering (BME) is proud to host this year’s Institute-wide holiday celebration! Get out of your labs and offices\, socialize\, get to know one another\, and help build our community.
URL:https://bme.utoronto.ca/event/bme-holiday-party-2025/
ATTACH;FMTTYPE=image/jpeg:https://bme.utoronto.ca/wp-content/uploads/2025/10/Holiday-Party-2-2.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251203T120000
DTEND;TZID=America/Toronto:20251203T130000
DTSTAMP:20251112T185145Z
CREATED:20251112T133546Z
LAST-MODIFIED:20251112T185145Z
UID:10000651-1764763200-1764766800@bme.utoronto.ca
SUMMARY:Open Defense - Development of Predictive Culture Models of Skeletal Muscle to Advance Muscle Stem Cell Therapeutics
DESCRIPTION:
URL:https://bme.utoronto.ca/event/open-defense-development-of-predictive-culture-models-of-skeletal-muscle-to-advance-muscle-stem-cell-therapeutics/
CATEGORIES:Events & Workshops
ATTACH;FMTTYPE=image/png:https://bme.utoronto.ca/wp-content/uploads/2025/09/Open-Defense-6.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251205T161000
DTEND;TZID=America/Toronto:20251205T162500
DTSTAMP:20251205T200737Z
CREATED:20251015T172231Z
LAST-MODIFIED:20251205T200737Z
UID:10000634-1764951000-1764951900@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Jennifer Kieda
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: POMaC particles for use in tissue engineering\nSupervisor Name: Milica Radisic\nYear of Study: 5\nProgram of Study: PhD\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-jennifer-kieda/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251205T162500
DTEND;TZID=America/Toronto:20251205T164000
DTSTAMP:20251205T200737Z
CREATED:20251015T172231Z
LAST-MODIFIED:20251205T200737Z
UID:10000635-1764951900-1764952800@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Richard jiang
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 Customizable Biomaterials that Promote Cardiac Tissue Growth Through Mechanical and Physical Cues\nSupervisor Name: Milica Radisic\nYear of Study: 3\nProgram of Study: PhD\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-richard-jiang/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251205T164000
DTEND;TZID=America/Toronto:20251205T165500
DTSTAMP:20251205T200738Z
CREATED:20251015T172231Z
LAST-MODIFIED:20251205T200738Z
UID:10000636-1764952800-1764953700@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Jathushan Kaetheeswaran
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: Consumer-grade Smartwatches for Cardiovascular Monitoring\nAbstract: Consumer-grade smartwatches offer a new personalized health monitoring option for general consumers globally as cardiovascular diseases continue to prevail as the leading cause of global mortality. The development and validation of reliable cardiovascular monitoring algorithms for these consumer-grade devices requires realistic biosignal data from diverse sets of participants. However\, the availability of public consumer-grade smartwatch datasets with synchronized cardiovascular biosignals is limited\, and existing datasets do not offer rich demographic diversity in their participant cohorts\, leading to potentially biased algorithm development. This paper presents HEART-Watch\, a multimodal physiological dataset collected from temporally synchronized wrist-worn Google Pixel Watch 2 electrocardiogram (ECG)\, photoplethysmography\, and accelerometer signals from a diverse cohort of 40 healthy adults across three physical states – sitting\, standing and walking with reference chest ECG. Intermittent upper arm blood pressure measurements and concurrent biosignals were collected as an additional biomarker for future research.\nSupervisor Name: Milad Lankarany\nYear of Study: 2\nProgram of Study: MASc\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-jathushan-kaetheeswaran/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251205T165500
DTEND;TZID=America/Toronto:20251205T171000
DTSTAMP:20251205T200737Z
CREATED:20251120T192237Z
LAST-MODIFIED:20251205T200737Z
UID:10000653-1764953700-1764954600@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Yinghe Sun
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: Identifying generalizable features in peripheral nerve recordings for improved neuroprosthetic control\nAbstract:\nPeripheral nerve interfaces can be used to create advanced assistive technologies. Neural networks associated with multicontact nerve cuff electrodes can selectively record and discriminate neural recordings and facilitate neuroprosthetic control. Due to variations in device positioning and anatomy\, neural networks trained on one subject currently cannot generalize to others. To take advantage of available data from other subjects\, the objective was to train a neural network whose encoder portion can extract representations that generalize effectively when using transfer learning to adapt the classification to new subjects.\nThe study applied neural networks to classify naturally evoked compound action potentials corresponding to three different sensory stimuli. The datasets were obtained from the sciatic nerves of 9 Long-Evans Rats through 7×8-channel cuff electrodes. To leverage data from multiple subjects\, we pre-trained the network on either one subject or merged data from multiple subjects\, then used cross-validation to retrain and evaluate it on a separate target subject. Layer freezing was applied to identify which part of the encoder would best generalize.\nPre-training with merged datasets led to a significant increase in mean macro-F1 score compared to subject-specific models trained from scratch (0.810±0.130 vs 0.733±0.121\, p < 0.05)\, regardless of the number of frozen layers. Pre-training on a single subject did not lead to a significant improvement.\nA pre-training approach combining data from multiple subjects shows significant improvement in classification performance. The study developed an encoder that benefits classification performance on unseen subjects despite anatomical variability and device positioning differences.\nSupervisor Name: José Zariffa\nYear of Study: 3\nProgram of Study: PhD\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-yinghe-sun/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20251205T165500
DTEND;TZID=America/Toronto:20251205T171000
DTSTAMP:20251120T192238Z
CREATED:20251120T192238Z
LAST-MODIFIED:20251120T192238Z
UID:10000654-1764953700-1764954600@bme.utoronto.ca
SUMMARY:Graduate Student Seminar Series - Yinghe Sun
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: Identifying generalizable features in peripheral nerve recordings for improved neuroprosthetic control\nAbstract:\nPeripheral nerve interfaces can be used to create advanced assistive technologies. Neural networks associated with multicontact nerve cuff electrodes can selectively record and discriminate neural recordings and facilitate neuroprosthetic control. Due to variations in device positioning and anatomy\, neural networks trained on one subject currently cannot generalize to others. To take advantage of available data from other subjects\, the objective was to train a neural network whose encoder portion can extract representations that generalize effectively when using transfer learning to adapt the classification to new subjects.\nThe study applied neural networks to classify naturally evoked compound action potentials corresponding to three different sensory stimuli. The datasets were obtained from the sciatic nerves of 9 Long-Evans Rats through 7×8-channel cuff electrodes. To leverage data from multiple subjects\, we pre-trained the network on either one subject or merged data from multiple subjects\, then used cross-validation to retrain and evaluate it on a separate target subject. Layer freezing was applied to identify which part of the encoder would best generalize.\nPre-training with merged datasets led to a significant increase in mean macro-F1 score compared to subject-specific models trained from scratch (0.810±0.130 vs 0.733±0.121\, p < 0.05)\, regardless of the number of frozen layers. Pre-training on a single subject did not lead to a significant improvement.\nA pre-training approach combining data from multiple subjects shows significant improvement in classification performance. The study developed an encoder that benefits classification performance on unseen subjects despite anatomical variability and device positioning differences.\nSupervisor Name: José Zariffa\nYear of Study: 3\nProgram of Study: PhD\nPowered by Calendly.com
URL:https://bme.utoronto.ca/event/graduate-student-seminar-series-yinghe-sun-2/
LOCATION:MS2158
CATEGORIES:Graduate Seminar Series
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