Department Publications

Human gait analysis, the study of human locomotion, is possible with low-cost RGBD sensors such as the Kinect sensor. However, due to the inherent depth sensing accuracy limitations of these sensors as the distance from the sensor increases, the distance range of gait analysis too becomes small and inefficient for clinical use. We present a system that uses two independent Kinects in a data fusion framework that increases the distance range of gait analysis from 2.5 m to 4 m with three gait cycles. Our gait parameters are reasonably accurate and comparable with existing systems with 4% error in length measurements and 5° error in flexion measurements. The system is extensible to have several Kinects.

Fitness related wearables have become ubiquitous in the recent past. Nevertheless, short battery life of these devices is still a pressing issue. Limited battery capacity in small form factor and power hungry continuous monitoring of accelerometer have been significant concerns in this regard. To address these issues we propose a novel low-power step counting solution based on an Electromagnetic energy harvesting mechanism. Extremely simple nature of the step counter removes the requirement of any step detection algorithm, thereby reducing the power consumption, while the energy harvester generates a portion of energy requirement prolonging the battery life.

Pulse Transit Time (PTT)/Pulse Wave Velocity (PWV) measurement based non-invasive blood pressure estimation is of much interest at present, as it enables continuous monitoring of blood pressure in a more comfortable manner compared to traditional cuff based methods, without hindering natural user behavior. In this study we have investigated the possibility of estimating blood pressure using PTT without the need of external calibrations. The parametric model developed in our previous research is improved eliminating the need of initial calibration which requires external blood pressure measurement from a reference device. The results suggest that there is a potential of developing calibrationless blood pressure estimation methods based on PTT/PWV which may require further testing and validation.

Early detection of hypertension generally requires continuous monitoring of blood pressure levels which is not facilitated by traditional methods such as the cuff, which cannot be used in the normal environment for continuous monitoring due to the regular pressurization of certain body parts. Thus there is a need for non-invasive continuous pressure monitoring mechanism. In this paper we present a relationship between Pulse Transit Time (PTT) and Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) for continuous pressure monitoring and its experimental validation.

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 …

This paper presents a methodology to diagnose some heart diseases which can be identified using heart sounds. As the first stage, audio files in . wav format are acquired using an electronic stethoscope and entered to a database with patient symptoms. Prominent features and statistical parameters needed for disease detection are extracted from the heart sound samples using some algorithm. Patient's heart sound fde and symptoms are given as inputs to the developed software for analysis to find out the diseases

Monitoring the ECG (Electrocardiogram) of patients with cardiac disorder is of paramount importance since they may undergo cardiac arrhythmias even without noticing it. Majority of existing devices for this purpose are only capable of recording ECG of patients which are analyzed later on by cardiologists. This paper presents a new system developed to continuously monitor the ECG of patients and analyze them in real time to identify any abnormalities. The new system consists of two parts: a portable device worn by the patient and a server side component. The arrhythmia detection algorithm used in the portable device shows 99.78% sensitivity, 99.65% positive predictivity for QRS complex detection and 93.48% sensitivity, 96.45% positive predictivity for ventricular beat classification when evaluated on MIT-BIH arrhythmia database. When an arrhythmia is detected the device automatically uploads the ECG data in …