Ege, MertMorgül, Ömer2024-03-122024-03-122023-06-0397830313550042367-3370https://hdl.handle.net/11693/114568Human Activity Recognition (HAR) is an attractive task in academic researchers. Furthermore, HAR is used in many areas such as security, sports activities, health, and entertainment. Frequency Modulated Continuous Wave (FMCW) radar data is a suitable option to classify human activities since it operates more robustly than a camera in difficult weather conditions such as fog and rain. Additionally, FMCW radars cost less than cameras. However, FMCW radars are less popular than camera-based HAR systems. This is mainly because the accuracy performance of FMCW radar data is lower than that of the camera when classifying human activation This article proposes the SiameseHAR model for the classification of human movement with FMCW radar data. In this model, we use LSTM and GRU blocks in parallel. In addition, we feed radar data operating at different frequencies (10 GHz, 24 GHz, 77 GHz) to the SiameseHAR model in parallel with the Siamese architecture. Therefore, the weights of the paths that use different radar data as inputs are tied. As far as we know, it is the first time that the multi-input Siamese architecture has been used for human activity classification. The SiameseHAR model we proposed is superior to most of the state-of-the-art models.enCC BY 4.0 DEED (Attribution 4.0 International)https://creativecommons.org/licenses/by/4.0/Human activity recognitionSiamese networkFMCW radar datamicro-doppler signaturedeep learningConference Paper10.1007/978-3-031-35501-1_2997830313550112367-3389