Expertise: Vital Sign Acquisition
SAM3 Expertise Areas: Vital Sign Acquisition
SAM3 researchers have explored a number of methods to measure vital signs within the smart home through passive and non-contact means. These provide an option for the measurement of these vital signs without the need for the use of a device that is worn or directly used by the subject.
Application Areas
- Video Magnification for the assessment of heart rate, respiration rate and blood pressure
- Pressure mats for the assessment of respiration rate and other respiratory conditions including cough.
- Acoustics analysis to assess cough and respiration.
- Applications of vital sign measurement to assess driver attention
Bruce Wallace, Phillippe Forster, Rafik Goubran, Heidi Sveistrup, William Jones, Thomas Fletcher “Small ECG Device Dry Electrode Lead Analysis to Ensure ECG Signal Quality’, AGEWELL AGM, 2025
B. Laska, P. Xi, J. Valdés, B. Wallace, J. Green, R. Goubran, F. Knoefel, “Coughprint: Distilled Cough Representations from Speech Foundation Model Embeddings”, in IEEE Transactions on Instrumentation and Measurement, 2025
P. Forster, B. Laska, R. Goubran, B. Wallace, P. Liu, H. Sveistrup, “Method for the Measurement of the Dicrotic Notch in the Photoplethysmogram Signal” accepted at 2025 IEEE International Instrumentation Measurement Technology Conference (I2MTC), 2025
Elhajjar, Diane “A Confidence Framework for Heart Rate Estimation in Video Magnification.” MASc diss., Carleton University, 2023.
Link to Thesis
Stewart, Matthew, Caitlin Higginson, Julien Lariviere-Chartier, Elliott Lee, James Green, Rafik Goubran, Frank Knoefel, and Rebecca Robillard. “Classification of Central and Obstructive Sleep Apnea Using Respiratory Inductance Plethysmography and Convolutional Neural Networks.” In 2024 IEEE Sensors Applications Symposium (SAS), pp. 1-6. IEEE, 2024.
Link to Paper
Saiful Huq, “Differentiation of Dry and Wet Cough Sounds using A Deep Learning Model and Data Augmentation”, M.A.Sc. Thesis Carleton University, 2023.
Link to Thesis