Smart Application for Fall Detection Using Wearable ECG & Accelerometer
Sensors
- URL: http://arxiv.org/abs/2207.00008v1
- Date: Tue, 28 Jun 2022 12:49:25 GMT
- Title: Smart Application for Fall Detection Using Wearable ECG & Accelerometer
Sensors
- Authors: Harry Wixley
- Abstract summary: Timely and reliable detection of falls is a large and rapidly growing field of research.
Sensors are beneficial for fall detection, and allow for the discrimination between falls and other activities.
This study leaves a significant amount of room for improvement due to weaknesses identified in the resultant dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Timely and reliable detection of falls is a large and rapidly growing field
of research due to the medical and financial demand of caring for a constantly
growing elderly population. Within the past 2 decades, the availability of
high-quality hardware (high-quality sensors and AI microchips) and software
(machine learning algorithms) technologies has served as a catalyst for this
research by giving developers the capabilities to develop such systems. This
study developed multiple application components in order to investigate the
development challenges and choices for fall detection systems, and provide
materials for future research. The smart application developed using this
methodology was validated by the results from fall detection modelling
experiments and model mobile deployment. The best performing model overall was
the ResNet152 on a standardised, and shuffled dataset with a 2s window size
which achieved 92.8% AUC, 7.28% sensitivity, and 98.33% specificity. Given
these results it is evident that accelerometer and ECG sensors are beneficial
for fall detection, and allow for the discrimination between falls and other
activities. This study leaves a significant amount of room for improvement due
to weaknesses identified in the resultant dataset. These improvements include
using a labelling protocol for the critical phase of a fall, increasing the
number of dataset samples, improving the test subject representation, and
experimenting with frequency domain preprocessing.
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