Design of bandage-sized wireless sensors and effect of ambient displays on social support and diabetes management

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004.Includes bibliographical references (p. 96-100).Health-Zero refers to health technology design that focus on interaction with an individual as a whole to promote well-being. This thesis presents two explorations in the discipline. The first exploration involves the design of a wireless bandage-sized ECG sensor to create a personal and interpersonal network of health sensors. The second exploration designs and evaluates an ambient blood glucose level visualization and feedback system for diabetes self-care and social support, where the latter is provided by a friend or family member of an individual with diabetes. This thesis provides insight into using technology to enable an individual to leverage his environment and inter-personal relationships for self-care and prevention.by Akshay Mohan.S.M

Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations

Vehicle light detection, association, and localization are required for important downstream safe autonomous driving tasks, such as predicting a vehicle's light state to determine if the vehicle is making a lane change or turning. Currently, many vehicle light detectors use single-stage detectors which predict bounding boxes to identify a vehicle light, in a manner decoupled from vehicle instances. In this paper, we present a method for detecting a vehicle light given an upstream vehicle detection and approximation of a visible light's center. Our method predicts four approximate corners associated with each vehicle light. We experiment with CNN architectures, data augmentation, and contextual preprocessing methods designed to reduce surrounding-vehicle confusion. We achieve an average distance error from the ground truth corner of 4.77 pixels, about 16.33% of the size of the vehicle light on average. We train and evaluate our model on the LISA Lights Dataset, allowing us to thoroughly evaluate our vehicle light corner detection model on a large variety of vehicle light shapes and lighting conditions. We propose that this model can be integrated into a pipeline with vehicle detection and vehicle light center detection to make a fully-formed vehicle light detection network, valuable to identifying trajectory-informative signals in driving scenes