| Authors: | |
| Kohei Yamamoto | |
| Kentaroh Toyoda | |
| Tomoaki Ohtsuki |
One Sentence Summary:
propose a novel CNN (Convolutional Neural Network)-based respiration rate estimation method
Abstract:
- Non-contact respiration rate estimation technique in indoor environments is receiving more and more attention in various fields, e.g., health care and smart home, since respiration is known to reflect our health condition. Hence, various radarbased respiration rate estimation methods have been proposed so far. However, these conventional methods do not work, when a subject is not right in front of the radar. In this paper, we propose a novel CNN (Convolutional Neural Network)-based respiration rate estimation method in indoor environments via a MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. A MIMO FMCW radar can estimate DoA (Direction of Arrival) and the distance between a MIMO FMCW radar and an object. Thus, respiration can be captured based on the phase variation at a subject's location. However, even when the advanced signal processing, e.g., MUSIC (MUltiple SIgnal Classification) algorithm, is used, it is difficult to estimate DoA and the distance in indoor environments due to the large effect of multipath. To deal with this problem, the proposed method calculates spectrograms from phase variations against various locations, and then estimates the respiration rate by inputting each spectrogram into CNN that outputs the respiration rates, e.g., 0.1 Hz, 0.2 Hz, and non-respiration, i.e., a spectrogram without the effect of respiration. We observed respiration in three situations where a subject was lying on his (i) back, (ii) face, and (iii) side at various indoor locations. We confirmed that except for when microwaves were not transmitted directly toward a subject's chest, our method accurately estimated the respiration rate, regardless of the situation.