Faculty: Anind Dey, Deepak Ganesan
Post-docs: Jin-Hyuk Hong, Katarzyna Wac
The overall goal of the project is to develop a FieldStream system that enables obtaining scientifically valid physiological and behavioral data from human subjects in their natural environments in an accurate and timely manner. These data is to be obtained with use of Body Area Wireless Sensor Network (BAWSN) systems worn on the body of the subject. A BAWSN can sample physiological data as ECG, respiration, temperature, skin conductance and photoplethysmogram (PPG) with different frequency and resolution. The aim is that the BAWSN system automatically and accurately discriminates between the expected and unexpected behaviors, based on the models of behaviors learned from historical physiological data obtained from the subject in the past. Example behaviors include panic attack, stress or addiction withdrawal behavior.
As the data is obtained from the subjects in their natural environments, the BAWSN needs also capture subject’s context and this to enable the FieldStream to deal with noise, motion artifacts, and existence of other uncontrollable confounding factors influencing the quality of data obtained from the subject. For this purpose, subject' s activity, ambient temperature, light and humidity conditions conditions are sampled.
Our research addresses the BAWSN system
requirement for deriving accurate behavior models and for the system personalization to
account for wide between person differences in physiological measurements.
Secondly, our research aims at optimization of the sampling frequency of physiological data for an accurate of behavior recognition based on the learned behavior models. The
approach we take will be necessarily an experimental one. Namely, we will firstly
attempt to develop personalized models for subjects’ behavior based on the data
obtained in laboratory experiments. These models will not account for subject’s
Secondly, we will aim to obtain data for subjects being in particular sets of
contexts, such that models can be updated to account for these contexts. And finally we will derive optimization function for sampling frequency and resolution of physiological measurements based on the required speed and accuracy of subject's behavior recognition.
We structure our research along the following activities:
Currently we are focusing on state-of-the-art research on personalized modeling of behaviors based on physiological data and user context data. We have formulated of the following research questions.
Research questions related to the personalized behavior modeling:
Research questions related to behavior recognition:
The research is ongoing. Please return to this page for updates on our progress.
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 J. Healey and R. Picard, " Detecting stress during real-world driving tasks using physiological sensors," IEEE Trans. on Intelligent Transportation Systems, vol. 6, no. 2, pp. 156-166, 2006.