Investigation of Few-Shot Learning for Fall Detection

Abstract

Falls affect seniors' quality of life, and therefore fall detection and prevention are paramount for the health and safety of aging seniors. Current deep learning-based fall detection methods perform well when a large amount of training data is available. As obtaining fall data from seniors is extremely difficult, training deep learning models is a challenge, and therefore, a few-shot Siamese network is considered in this thesis. A shallow 1 × 1 convolutional neural network for Siamese and Triplet networks is proposed in this work. A deeper architecture-based on the Inception and Densenet networks is also considered to improve the fall detection performance. The performances of the proposed few-shot Siamese architectures and Triplet networks are investigated using signals obtained from a wearable sensor. The proposed learning models outperform the traditional deep learning networks, while Siamese architectures also demonstrate generalizability by classifying unseen classes of falls and falls from different sensing modalities. First and foremost I am extremely grateful to my supervisor, Dr. Sreeraman Rajan, for his awesome advice, continuous support, and patience during the course of my masters program. His plentiful experience have encouraged me through out the time of my research and daily life. I thank the examination committee members for their time and efforts to evaluate my work and provide valuable feedback. I am extremely grateful to Dr. Jila Hosseinkhani for meticulously proof-reading my thesis and providing appropriate feedback. I would like to thank the Department of Systems and Computer Engineering, Carleton University, for providing me the opportunity and on-demand support for the completion of my program. I also thank the Natural Science and Engineering Council of Canada (NSERC) for partially funding the work. I also acknowledge the financial support that I got for a term from Department of National Defence's (DND)-IDEAS-Micronet initiative. These financial supports have made my study and life in Canada a smooth experience through out the ups and downs of this pandemic-ridden world. Finally, I would like to express my gratitude to my parents. Without their financial support, tremendous understanding and encouragement in the past few months, it would be impossible for me to complete my program.