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 x 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.