Key result
A deep neural network algorithm using frequency-modulated continuous wave radar successfully classified four persons based on their respiration patterns with 100% accuracy.
FMCW radar combined with a DNN algorithm can accurately identify individuals based on their unique respiration patterns.
This letter proposes a method of human identification that measures respiration patterns using frequency-modulated continuous wave (FMCW) radar. We exploit the fact that respiration signal patterns are unique to each individual, and FMCW radar is employed to obtain the respiration information. Based on the strengths of FMCW radar, the proposed algorithm compensates for the inability to distinguish the respiration signals of multiple users, which are difficult for continuous wave radar to measure. The proposed algorithm also employs a deep neural network algorithm instead of the K-nearest neighbor algorithm that was used in a previous study. The proposed algorithm further improves the performance by using a least mean square filter in the input signal of the DNN. The experimental results show that the proposed human identification method successfully classified four persons.
No takes yet. Share an insight, caveat, or question.
Kim et al. (2020) studied Human identification (n=4). Frequency-modulated continuous wave (FMCW) radar with Deep Neural Network (DNN) algorithm vs. K-nearest neighbor (KNN) algorithm was evaluated on Classification accuracy of human identification. A deep neural network algorithm using frequency-modulated continuous wave radar successfully classified four persons based on their respiration patterns with 100% accuracy.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: