Faculty Supervisor
Dr Khaled Mahmud
Date of Defense
Fall 12-1-2020
Program Name
Honours Bachelor of Computer Science (Mobile Computing)
School
Applied Computing
Keywords
fall detection, machine learning, neural network, SoC, edge processing, IoT
Department
Faculty of Applied Science & Technology (FAST)
Description
Falls inside of the home is a major concern facing the aging population. Monitoring the home environment to detect a fall can prevent profound consequences due to delayed emergency response. One option to monitor a home environment is to use a camera-based fall detection system. Conceptual designs vary from 3D positional monitoring (multi-camera monitoring) to body position and limb speed classification. Research shows varying degree of success with such concepts when designed with multi-camera setup. However, camera-based systems are inherently intrusive and costly to implement. In this research, we use a sound-based system to detect fall events. Acoustic sensors are used to monitor various sound events and feed a trained machine learning model that makes predictions of a fall events. Audio samples from the sensors are converted to frequency domain images using Mel-Frequency Cepstral Coefficients method. These images are used by a trained convolution neural network to predict a fall. A publicly available dataset of household sounds is used to train the model. Varying the model's complexity, we found an optimal architecture that achieves high performance while being computationally less extensive compared to the other models with similar performance. We deployed this model in a NVIDIA Jetson Nano Developer Kit.
Abstract
Falls inside of the home is a major concern facing the aging population. Monitoring the home environment to detect a fall can prevent profound consequences due to delayed emergency response. One option to monitor a home environment is to use a camera-based fall detection system. Conceptual designs vary from 3D positional monitoring (multi-camera monitoring) to body position and limb speed classification. Research shows varying degree of success with such concepts when designed with multi-camera setup. However, camera-based systems are inherently intrusive and costly to implement. In this research, we use a sound-based system to detect fall events. Acoustic sensors are used to monitor various sound events and feed a trained machine learning model that makes predictions of a fall events. Audio samples from the sensors are converted to frequency domain images using Mel-Frequency Cepstral Coefficients method. These images are used by a trained convolution neural network to predict a fall. A publicly available dataset of household sounds is used to train the model. Varying the model's complexity, we found an optimal architecture that achieves high performance while being computationally less extensive compared to the other models with similar performance. We deployed this model in a NVIDIA Jetson Nano Developer Kit.
Copyright
© Warren Zajac
Terms of Use
Terms of Use for Works posted in SOURCE.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Zajac, Warren, "Fall Detection Using Neural Networks" (2020). Student Theses. 3.
https://source.sheridancollege.ca/fast_sw_mobile_computing_theses/3