This study seeks to combine the collection of wireless accelerometry data (using TEMPO) with in-clinic wired electroencephalography (EEG) monitoring for epileptic seizures, to determine the efficacy of accelerometer-based detection of such seizures.
Various sensing systems have been exploited to monitor social activities, which are one of the most important indicators of physical and mental health. However, existing solutions either need to deploy significant ambient infrastructure or cannot provide enough detailed insight regardig the quality of social interactions which is of great interest to psychiatrists and caregivers. In this reserach, we use Body Sensor Networks (BSNs) to accurately detect various social activities and provides insights absent in existing systems.
All BSN applications have strict requirements regarding different metrics (battery life, low latency etc.). However there are not widely accepted metrics regarding the application fidelity. This project focuses on the development of a general framework whose task is to define appropriate application fidelity metrics that meet the requirements of different BSN applications.