Human activity classification with deep learning using FMCW radar
Human Activity Recognition (HAR) has recently attracted academic research attention and is used for purposes such as healthcare systems, surveillance-based security, sports activities, and entertainment. Deep Learning is also frequently used in Human Activity Recognition, as it shows superior performance in subjects such as Computer Vision and Natural Language Processing. FMCW radar data is a good choice for Human Activity Recognition as it works better than cameras under challenging situations such as rainy and foggy conditions. However, the work in this field does not progress as dynamically as in the camera-based area. This can be attributed to radar-based models that do not perform as well as camera-based models. This thesis proposes four new models to improve HAR performance using FMCW radar data. These models are CNN-based, LSTM-based, LSTM- and GRU-based, and Siamese-based. For feature extraction, the CNN-based model uses CNN blocks, the LSTM-based model uses LSTM blocks, and the LSTM-and GRU-based model uses LSTM and GRU blocks in parallel. Furthermore, the Siamese-based model is fed in parallel from three different radars (multi-input). Due to the Siamese Network nature, parallel paths will have the same weight. On the other hand, after feature extraction, all models use dense layers to classify human motion. To our best knowledge, the Siamese-based model is used for the first time in multi-input data for the classification of human movement. This model outper-forms the state-of-the-art models by using various features of radars operating at different frequencies in terms of classification accuracy. All codes and their esults can be found at "https://github.com/mertege/Thesis Experiments".