Booth Id:
ENBM016
Category:
Biomedical Engineering
Year:
2019
Finalist Names:
Nie, Melissa (School: Saint Paul Academy and Summit School)
Abstract:
With advancements in science and medicine, the population of older adults has grown around
the world in recent years. As a result, fall detection systems are necessary for the wellbeing of
older adults, but there are no existing solutions that are simultaneously accurate, accessible, and
able to maintain privacy. In this project, a hardware and software platform has been developed to
capture thermal images with a thermopile array sensor, remove interference caused by items
other than a human body that radiate thermal energy, identify a human body from low-resolution
images and track its movement, extract valid features from the identified body to reduce
computational complexity, and employ neural network algorithms to detect falls from organized
features. Performance evaluations show that the neural network algorithms can detect falls with
high accuracy. Therefore, fall detection systems that use thermopile array sensors and machine
learning algorithms are a promising approach to maintaining the health of older adults.
Awards Won:
Third Award of $1,000