Residual network-based supervised learning of remotely sensed fall incidents using ultra-wideband radar
Detecting falls using radar has many applications in smart health care. In this paper, a novel method for fall detection in human daily activities using an ultra wideband radar technology is proposed. A time series derived from the radar scattering matrix is used as input to the the residual network for automatic feature extraction. In contrast to other existing methods, the proposed method relies on multi-level feature learning directly from the radar time series signals. In particular, the proposed method utilizes a deep residual neural network for automating feature learning and enhancing model discriminability. The performance of the proposed method is compared with that of the other methods such as support vector machine, K-nearest neighbors, multi-layer perceptron and dynamic time warping techniques. The results show that the proposed fall detection method outperforms the other methods in terms of accuracy and sensitivity values.
|Biomedical signal processing, Classification, Fall detection, Residual network, Smart home care, Ultra-wideband radar|
|2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019|
|Organisation||Department of Systems and Computer Engineering|
Sadreazami, H. (Hamidreza), Bolie, M. (Miodrag), & Rajan, S. (2019). Residual network-based supervised learning of remotely sensed fall incidents using ultra-wideband radar. In Proceedings - IEEE International Symposium on Circuits and Systems. doi:10.1109/ISCAS.2019.8702446