Abstract: Your model is only as good as the data that goes into it, so removing the maximum amount of noise while retaining signal is vital. This talk will introduce basic signal processing and motion artefact removal techniques, such as low-pass filtering, Kalman filtering, and Savitzky-Golay filtering. The session will take you through practical examples that you can apply straight away to your biological data. I will also make recommendations for data collection to consider when designing a sensor so that you can get the best possible data (because prevention is better than treatment!). Those interested in machine learning and signal analysis for biomedical processing, electrical and optical signals, and wearables technology will enjoy this talk!
Bio: Mathias Ciliberto is a data scientist at Hydrostasis, Inc. He started working on wearable sensors at ST Microelectronics, before completing his PhD in Engineering at the University of Sussex with a thesis on movement recognition with template matching methods for power-aware applications. At Hydrostasis Inc., he works on methods for hydration assessment using near-infrared signals. His expertise includes machine learning, deep learning and template matching methods for physiological sensing and assessment, with a focus on health-care and low power applications. His research interests also include signal processing, embedded systems and novel wearable applications.