Aravinda S. Rao

Motor recovery monitoring using acceleration measurements in post acute stroke patients.

BackgroundStroke is one of the major causes of morbidity and mortality. Its recovery and treatment depends on close clinical monitoring by a clinician especially during the first few hours after the onset of stroke. Patients who do not exhibit early motor recovery post thrombolysis may benefit from more aggressive treatment.MethodA novel approach for monitoring stroke during the first few hours after the onset of stroke using a wireless accelerometer based motor activity monitoring system is developed. It monitors the motor activity by measuring the acceleration of the arms in three axes. In the presented proof of concept study, the measured acceleration data is transferred wirelessly using iMote2 platform to the base station that is equipped with an online algorithm capable of calculating an index equivalent to the National Institute of Health Stroke Score (NIHSS) motor index. The system is developed by collecting data from 15 patients.ResultsWe have successfully demonstrated an end-to-end stroke monitoring system reporting an accuracy of calculating stroke index of more than 80%, highest Cohen’s overall agreement of 0.91 (with excellent κ coefficient of 0.76).ConclusionA wireless accelerometer based ‘hot stroke’ monitoring system is developed to monitor the motor recovery in acute-stroke patients. It has been shown to monitor stroke patients continuously, which has not been possible so far with high reliability.

Design of low-cost autonomous water quality monitoring system

Good water quality is essential for the health of our aquatic ecosystems. Continuous water quality monitoring is an important tool for catchment management authorities, providing real-time data for environmental protection and tracking pollution sources; however, continuous water quality monitoring at high temporal and spatial resolution remains prohibitively expensive. An affordable wireless aquatic monitoring system will enable cost-effective water quality data collection, assisting catchment managers to maintain the health of aquatic ecosystems. In this paper, a low-cost wireless water physiochemistry sensing system is presented. The results indicate that with appropriate calibration, a reliable monitoring system can be established. This will allow catchment managers to continuously monitoring the quality of the water at higher spatial resolution than has previously been feasible, and to maintain this surveillance over an extended period of time. In addition, it helps to understand the behavior of aquatic animals relative to water pollution using data analysis.

Estimation of crowd density by clustering motion cues

Understanding crowd behavior using automated video analytics is a relevant research problem in recent times due to complex challenges in monitoring large gatherings. From an automated video surveillance perspective, estimation of crowd density in particular regions of the video scene is an indispensable tool in understanding crowd behavior. Crowd density estimation provides the measure of number of people in a given region at a specified time. While most of the existing computer vision methods use supervised training to arrive at density estimates, we propose an approach to estimate crowd density using motion cues and hierarchical clustering. The proposed method incorporates optical flow for motion estimation, contour analysis for crowd silhouette detection, and clustering to derive the crowd density. The proposed approach has been tested on a dataset collected at the Melbourne Cricket Ground (MCG) and two publicly available crowd datasets—Performance Evaluation of Tracking and Surveillance (PETS) 2009 and University of California, San Diego (UCSD) Pedestrian Traffic Database—with different crowd densities (medium- to high-density crowds) and in varied environmental conditions (in the presence of partial occlusions). We show that the proposed approach results in accurate estimates of crowd density. While the maximum mean error of

Crowd density estimation based on optical flow and hierarchical clustering

3.62

Detection of Anomalous Crowd Behaviour Using Hyperspherical Clustering

3.62 was received for MCG and PETS datasets, it was

Real-Time Urban Microclimate Analysis Using Internet of Things

2.66