Prabhav Vanguri
student at Westwood High School. He has conducted scientific research for the last three years, including research at University of Texas at Austin and at the Southern Methodist University in Dallas.
Title: Fall-iN-TELL: Smart Electronic Framework for Personalized Accurate Fall Detection
Biography
Biography: Prabhav Vanguri
Abstract
Millions of older adults experience falls each year. Early response after a fall is vital because it has been established that the earlier the fall is reported, the lower the morbidity-mortality rate is. Gait stability is an important fall risk indicator for older adults. The goal of this project is to develop a reliable fall detection device that takes individual gait traits into consideration in order to accurately detect fall. In older adults, the risk of falling increases during normal daily activities that involve movement. In this research, movement data analysis of eight daily life activities during which falls likely occur are measured and analyzed. Based on this analysis, a quantifiable gait stability scale was developed to improve fall detection accuracy. A fall detection device was developed using a microcontroller with an inertial measurement unit. This device was programmed to detect falls by recording movement thresholds to distinguish between normal movements and falls. These thresholds are personalized based on the individual gait traits that are closely associated with falls. A warning system was also implemented in the device that is capable of alerting emergency services automatically, in case the user is incapacitated. The accuracy of this fall detection device was tested with the same group of adult volunteers whose gait traits were studied for analysis. Falls were simulated by dropping the prototype during various normal daily life activities. These tests were highly successful in predicting falls and generating alerts to provide timely intervention.