8th International Conference on Body Area Networks

September 30–October 2, 2013 Boston, Massachusetts, United States


Expenditure Estimation using Smartphone Body Sensors

Amit Pande (University of California, Davis), Yunze Zeng (University of California, Davis), Aveek Kumar Das (University of California, Davis), Prasant Mohapatra (University of California, Davis), Sheridan Miyamoto (UC Davis School of Medicine), Edmund Seto (University of California, Berkeley), Erik K. Henricson (UC Davis School of Medicine), and Jay J. Han (UC Davis School of Medicine)

Energy Expenditure Estimation (EEE) is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EEE utilizing small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EEE for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of a common smartphone user. We used existing smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately detect EEE. Using Artificial Neural Networks, a machine learning technique, we build a generic regression model for EEE that yields upto 89% correlation with actual Energy Expenditure (EE). Using barometer data, in addition to accelerometry is found to significantly improve EEE performance (upto 15%). We compare our results against state-of-the-art Calorimetry Equations (CE) and consumer electronics devices (Fitbit and Nike+ Fuel Band). We were able to demonstrate the superior accuracy achieved by our algorithm. The results were calibrated against COSMED K4b2 calorimeter readings.


Towards a Framework for Safety Analysis of Body Sensor Networks

Philip Asare (University of Virginia), John Lach (University of Virginia), John A. Stankovic (University of Virginia), Yi Zhang (U.S. Food and Drug Administration), Paul L. Jones (U.S. Food and Drug Administration), and Sandy Weininger (U.S. Food and Drug Administration)

Body sensor networks (BSNs) are an emerging class of medical cyber-physical systems which have the potential to change the healthcare paradigm. However, they present many new challenges, chief of which (like any medical device) is assuring patient safety. This requires not only a precise definition of safety, but also techniques for assessing the safety of BSN designs. Although solutions are possible and important for specific BSNs used in specific applications, addressing this issue on a case-by-case basis usually results in an ad-hoc process, and more importantly, makes the translation of experiences and solutions between different applications more difficult. A generic and conceptual framework for guiding the safety analysis process would provide all stakeholders a common basis for communicating, discussing, and examining the safety of BSN designs, and provide manufacturers with an exemplary process that they can follow to improve and gain confidence in the safety of their devices. This paper presents our current efforts in developing such a framework. In particular, we present a theoretical foundation for modeling and analyzing BSNs, and identify the general class of hazards based on this foundation. These efforts explore critical issues that deserve attention in designing safe BSN systems, and more importantly, can help advance the understanding of BSNs and their safety.