Lu H, Wenzel M, Steigleder T, Klinger I, Ostgathe C, Koelpin A (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 306-309
Conference Proceedings Title: 20th European Radar Conference, EuRAD 2023
ISBN: 9782874870743
DOI: 10.23919/EuRAD58043.2023.10289241
Continuous 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%.
APA:
Lu, H., Wenzel, M., Steigleder, T., Klinger, I., Ostgathe, C., & Koelpin, A. (2023). Contactless In-Bed Movement in Various Scales Classification with CW Radar. In 20th European Radar Conference, EuRAD 2023 (pp. 306-309). Berlin, DE: Institute of Electrical and Electronics Engineers Inc..
MLA:
Lu, Hui, et al. "Contactless In-Bed Movement in Various Scales Classification with CW Radar." Proceedings of the 20th European Radar Conference, EuRAD 2023, Berlin Institute of Electrical and Electronics Engineers Inc., 2023. 306-309.
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