Lu, HuiHuiLuWenzel, MarvinMarvinWenzelSteigleder, TobiasTobiasSteiglederKlinger, IsabellIsabellKlingerOstgathe, ChristophChristophOstgatheKölpin, AlexanderAlexanderKölpin2023-11-292023-11-292023-0920th European Radar Conference (EuRAD 2023)https://hdl.handle.net/11420/44423Continuous wave (CW) radar has been used to detect motions in various scenarios. In this paper, we first present a data-driven method to classify in-bed movement from various scales with CW radar. Data augmentation techniques are used to address the small sample size problem, resulting in a significant improvement of over 10% in accuracy. Three machine learning classifiers, namely random forest, k-nearest neighbor (k-NN), and multilayer perceptron (MLP), are evaluated, with random forest demonstrating the highest accuracy of 81.94% and relative improvement of 22.5% compared to k-NN. The movement sitting up from the bed can be classified with 97.5% accuracy. Additionally, the method can classify two types of movements involving only arm and leg movements, which are not visible to the radar, by detecting small-scale joint movements from the back with an accuracy of 74.8%.encontinuous wave radardata augmentationfeature selectionmachine learningmovement classificationMLE@TUHHPhysicsConference Paper10.23919/EuRAD58043.2023.10289241Conference Paper