Options
Contactless in-bed movement in various scales classification with CW radar
Publikationstyp
Conference Paper
Publikationsdatum
2023-09
Sprache
English
Author
Lu, Hui
Klinger, Isabell
Start Page
306
End Page
309
Citation
20th European Radar Conference (EuRAD 2023)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Institute of Electrical and Electronics Engineers Inc.
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%.
Schlagworte
continuous wave radar
data augmentation
feature selection
machine learning
movement classification
MLE@TUHH
DDC Class
530: Physics