Arlene John and Ying Wang will give a lunch talk for Data Science Week entitled: "Advancing Digital Phenotyping: From Physiological Time Series Data to Real-Life Multimodal Health Monitoring".
This talk explores the journey from physiological time-series data to multimodal data analysis for digital phenotyping, emphasizing the transition from controlled semi-lab environments to real-life health monitoring. The challenges and some innovations in daily-life health monitoring required to sense information unobtrusively to enable the development of personalized phenotypes for continuous health tracking is discussed. Key topics include both wireless and wearable sensing techniques, multimodal feature extraction, identifying interrelationships amongst features, and connecting these insights to individual phenotypes.
Additionally, we examine methods for monitoring health trends over extended periods. Practical applications discussed will include energy expenditure monitoring during daily physical activity for people with risk of obesity, cardiac function monitoring for people with long term diabetes, psychophysiological condition monitoring for people with knee osteoarthritis, recovery tracking post-colorectal surgery using patch sensors, and smartphone-based digital phenotyping for breast cancer survivors.
More information and registration
The talk is open to anyone interested in the topic and affiliated with the UT. Registration is required. Visit the Data Science Week website to register, and for more information on the other activities during the Data Science Week.
