The talk will cover physiological signal sensing, computational sensing (compressive sensing and sparsity), signal decomposition approaches for pre-processing (denoising, detrending and artifact removals), time-frequency and tensor signal processing for extraction of non-stationary and non-linear features, clustering approaches for pattern classification. Experimental results and suggestions on how these signal processing methods could be applied in the context of Internet of Things for “quantified” self and daily/frequency monitoring of activities such as cardiac, gait, sleep and other human motions.
Organizer I: Sri Krishnan, IEEE Senior Member and EMBS TC Member for Biomedical Signal Processing
Bio: Sri Krishnan is a Professor and a Canada Research Chair in Biomedical Signal Analysis at Ryerson University, Toronto, Ontario, Canada. He is also the founding Co-director for the Institute for Biomedical Engineering, Science and Technology (iBEST) a partnership between Ryerson University and St. Michael’s Hospital right at the heart of downtown Toronto. Dr. Krishnan is a Fellow of the Canadian Academy of Engineerin. More details are on the website: http://www.ee.ryerson.ca/~krishnan/