BreathTrack: Tracking Indoor Human Breath Status via Commodity WiFi

Zhang, Dongheng; Hu, Yang; Chen, Yan; Zeng, Bing

doi:10.1109/jiot.2019.2893330

Cited by 142 publications

(66 citation statements)

References 33 publications

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“…Previous equation is the result of Bayes rule and the conditional independence of the observation for the current state. The θ k which maximizes the following equation is selected as MAP parameter estimate in (15),…”

Section: A Analysis Of Modjukfmentioning

confidence: 99%

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

2020

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It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the timevarying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method. INDEX TERMS Unscented Kalman filter, joint unscented Kalman filter, respiratory rate tracking, devicefree, vital signs, health monitoring, radio signals.

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Section: A Analysis Of Modjukfmentioning

confidence: 99%

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

2020

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“…Fresnel Diffraction Model proposed by Daqing Zhang has been employed in fine-grained human respiration with commodity Wi-Fi devices [44]. A breath tracking system is investigated by utilizing both the hardware and software correction methods [45]. Respiration and breath monitoring are also combined detected when people is sleeping [46]- [48].…”

Section: ) Vital Signs Monitoringmentioning

confidence: 99%

Leveraging TPDV to Direction Estimation With a Single Pair of Commodity Devices

et al. 2020

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In this paper, we propose an indoor passive human tracking system equipped with only one pair of commodity Wi-Fi devices, namely WiBlinker. The key insight is that the motion of moving target not only introduce Doppler frequency shift, but also changes the value of phase difference between two adjacent antennas of the receiver. First of all, a lightweight interpolation scheme is presented to remove the random phase noise in channel state information for improving the accuracy of the trajectory tracing. Instead of using AoA to obtain the moving direction, WiBlinker employs the trend of phase difference variation in direction estimation which is no need multiple Wi-Fi devices and easier to be achieved. Experimental results show that WiBlinker outperforms the conventional multiple Wi-Fi links methods, especially in the case of a sharp turn. INDEX TERMS Indoor human tracking, commodity devices, Angle-of-Arrival, Doppler frequency shift, channel state information.

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“…In order to acquire accurate angle estimation, the MUSIC (multiple signal classification) algorithm has been applied, which adopts two or more AoA measurements from known points and utilizes triangulation to calculate the position of the signal source. All in all, the different behaviors can be tracked by analyzing the signal phase difference, such as handwriting recognition [130], human detection [126], [107], respiratory monitoring [136], and activity recognition [176].…”

Section: ) Angle Of Arrivalmentioning

confidence: 99%

“…The reason may be that building an appropriate model is challenging because many factors affect the precision of the model. The typical model-based applications include daily behavior recognition [124], [125], human detection [129], walking direction estimation [126], and respiration detection [24], [131]- [136]. We interpret the key element of these applications as follows.…”

Section: B Model-based Human Behavior Recognition Applicationsmentioning

confidence: 99%

A Survey on Human Behavior Recognition Using Channel State Information

et al. 2019

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Recently, device-free human behavior recognition has become a hot research topic and has achieved significant progress in the field of ubiquitous computing. Among various implementation, behavior recognition based on WiFi CSI (channel state information) has drawn wide attention due to its major advantages. This paper investigates more than 100 latest CSI based behavior recognition applications within the last 6 years and presents a comprehensive survey from every aspect of human behavior recognition. Firstly, this paper reviews general behavior recognition applications using the WiFi signal and presents the basic concept of CSI and the fundamental principle of CSI-based behavior recognition. This paper analyzes the key components and core characteristics of the system architecture of human behavior recognition using CSI. Afterward, we divide the sensing procedures into many steps and summarize the typical studies from these steps, including base signal selection, signal preprocessing, and identification approaches. Next, based on the recognition technique, we classify the applications into three groups, including patternbased, model-based, and deep learning-based approach. In every group, we categorize the state-of-the-art applications into three groups, including coarse-grained specific behavior recognition, fine-grained specific behavior recognition, and activity inference. It elaborates the typical behavior recognition applications from five aspects, including experimental equipment, experimental scenario, behavior, classifier, and system performance. Then, this paper presents comprehensive discussions about representative applications from the implementation view and outlines the major consideration when developing a recognition system. Finally, this article concludes by analyzing the open issues of CSI-based behavior recognition applications and pointing out future research directions. INDEX TERMS Channel state information (CSI), deep learning, human behavior recognition, model, pattern, WiFi.

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BreathTrack: Tracking Indoor Human Breath Status via Commodity WiFi

Cited by 142 publications

References 33 publications

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

Leveraging TPDV to Direction Estimation With a Single Pair of Commodity Devices

A Survey on Human Behavior Recognition Using Channel State Information

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