The need for remote human presence detection is growing. Home security systems use cameras and passive infra-red (PIR) sensors to determine if a person approaching or is inside the house. Passive infra-red sensors suffers from many false alarm, but more importantly, they cannot sense minute movements like a still, sleeping person and cameras are not suitable for applications in which privacy is needed like monitoring an elderly in the restroom. Radars have become a readily available solution for the consumer, so in this paper we will focus on the usage of a radar to detect the micro-Doppler effect of breathing, and thereby determine if a person is there or not. The problem of presence sensing is basically an decision problem: is there someone in the radar search volume or not? If the person walks or moves inside the room, a simple moving target indicator (MTI) followed by a constant false alarm rate (CFAR) detector can be employed to detect the walking or movement. However, this method will not work properly on a person who is immobile and only breathing, such as the situation when a person is asleep. Another critical example is baby monitoring applications: inside the car (in-cabin) elimination of the forgotten baby syndrome, in-crib detection of sudden infant death syndrome (SIDS), both relies on accurate, true presence detection.
The topic of human presence detection using radars had been studied before both in the context of moving and stationary subjects. In [1
], the authors showed the possibility of presence detection with a FMCW 24 GHz radar and compared it to passive-infrared (PIR) measurements. They utilized an energy detector on the range-Doppler map to decide whether a target is present or not, and did not deal with complete stationary or sleeping targets. The algorithm proposed in [2
] is based on calculation of the Doppler power to identify doorway crossing and thus infer on the occupancy of a given room or space. This work also dealt with the problem of walking humans only. WiFi signals were used in [3
] for passive-occupancy detection of humans. Though they did not deal with stationary targets, they reported promising results on people counting and presence detection of walking humans. Fourier processing with constant energy threshold detector was utilized in [4
], however, stationary human presence accuracy was not reported.
The feasibility of using breathing to detect presence was proven in [5
], where they showed that a mechanical target which simulates a breathing human can be detected in a room with 93% accuracy by using a Doppler radar with a threshold on the root mean square (RMS) of the received signal, while in [7
] a non-adaptive energy threshold detector calculated from pre-recorded noise data was employed to determine if there is one person or two in the radar search volume, both in movement and stationary settings. However, high energy does not mean breathing, or presence. Finally, the usage of convolutional neural networks (CNN) were investigated in [8
], where a Doppler radar and and infrared imaging device were jointly employed for presence detection of one human in a specific room. They reported an accuracy of
. The usage of CNNs bares a complex implementation for real-time purposes, as well as it relies heavily on visual information from the infrared imaging device.
Another application in which sensing the vital signs of a human subject is critical is through-wall and through-debris life sensing. In [9
], the detection of vital signs through walls was investigated. They showed the feasibility of detecting the breathing and heart-rate of a human subject standing behind a wall, while in [10
] they proposed a continuous wave radar architecture for the purpose of detecting vital signs through highly dense construction materials of about
m thick. In [11
], they used empirical mode decomposition to prove feasible the detection of breathing, hand waving and body bending behind an obstacle emulating debris. Through-debris breathing detection was also shown feasible in [12
], where they experiment with a debris setup and a live person lying and breathing under it. They showed visual results that the human can be detected, though they did not show how to detect the human in an automated manner. The underlying assumption of the above papers is that the subject is there. The accurate estimation of the breathing frequency is also of importance for many applications, including baby monitoring, elderly care, sleep monitoring and more.
Breathing rate extraction with a pulse-Doppler architecture was presented in [13
], where they visually showed feasibility of extracting the breathing rate of a stationary and moving human using Fourier analysis. They employed range-Doppler processing, but there was no outline of how to detect the ’breathing targets’ and verify that it is in fact breathing, also the accuracy was not evaluated. The authors in [14
] used the wavelet transform to overcome the discrete Fourier transform (DFT) resolution insufficiency, and for the same reason the chirp Z transform was used in [16
] to estimate breathing rate. The chirp Z transform was also used in [17
] coupled with an analytical model for the remote estimation of both breathing and heart-rate. The accuracy of these methods and a comparison against a known bound was not analyzed. More recent work on remote breathing extraction can be found in [18
], in which breathing was extracted with a radar and a verification that the peak is falling within the breathing band of frequencies was done. While [18
] reported accuracy results and evaluation of proposed methods, as we show in this paper, we achieve better results by using a maximum likelihood estimator. Furthermore, the verification presented there is not optimal, and does not test adherence to a breathing model, as the detector we present in this paper does.
The need to rely on the breathing movement for human presence detection stems from the fact that the use of a moving target indicator (MTI) [20
], often fails to detect stationary humans. MTI is essentially a high-pass filter (HPF) that filters out close to zero-Doppler targets. This HPF caveat is that it most often filters out the breathing, being that breathing frequency is very close to the zero frequency (DC), hence, will fail to detect a sleeping person.
We propose an algorithm that detects presence using the minute movements of the abdomen and torso due to breathing, while at the same time, estimates the breathing frequency. Since detecting moving targets, even targets that slightly shift in their chair, can be done using MTI, we intentionally deal only with strictly stationary subjects.
Thus, this paper’s contribution is three fold. First, we present a framework of detecting presence using only the breathing movement. We develop a GLRT detector which, as an input, takes the suspected breathing pattern and as an output decides whether its breathing (presence) or not. Second, a maximum likelihood (ML) estimator of the breathing rate is developed, and shown to asymptotically achieve the Cramer–Rao lower bound (CRB), and lastly, we show that the GLRT detector and ML estimator are the same mathematical expression so we inherently get both with one evaluation. We tested this algorithmic framework performance on various scenarios such as sleeping babies and stationary adults, and results are reported.
In Section 2
, we explain the measurement setup, and immediately move to derive both the GLRT detector as well as the ML estimator in Section 3
. The estimator is also compared to the CRB. In Section 4
, we explain the experiments we have done to verify our algorithms as well as report the results. We discuss future work in Section 5
and we conclude the paper in Section 6