Department Publications

Listening to the heart sounds is a common practice in identifying cardiac malfunctions. Since this method has many limitations, tools that aid physicians in their diagnosis of heart diseases are very useful. This paper presents a software tool to predict cardiac abnormalities which can be identified using heart sounds. Both heart sound information and symptoms are used in disease prediction. First audio inputs at four clinically important locations on the chest are acquired using an electronic stethoscope and entered to a database with symptoms for each patient. After de-noising, prominent features and statistical parameters needed for disease detection are extracted from the heart sound samples using several algorithms. Then the disease classification is performed to find out possible disease and murmur types. The software tool reported in this paper is capable of identifying normal heart sounds and abnormal heart …

The paper presents a wireless body area sensor network (BASN) for evaluating the effectiveness of fitness exercising. The goal of our work is to provide a mobile and an accurate way of quantifying the energy expenditure during a fitness exercise session. While energy expenditure estimates are available in modern exercise machines, no accurate method exists for outdoor exercising. The prototype system implemented includes an IEEE 802.15.4(Zigbee) based wireless body area sensor network which measures physiological and kinetic features related to the activity being monitored, and a mobile application for visualization of the estimated energy expenditure. The paper outlines the sensor hardware used, the body area network which collects the measurements from the sensor nodes, the algorithm for energy computation and experimental results. The system, once trained can be used with any form of outdoor …