RF-ECG:Heart Rate Variability Assessment Based on COTS RFID Tag Array.85:3 (e.g.,USRP [10]and RFID with conductive fabric electrodes [11]),and thus are expensive compared with the commercial devices.Additionally,some professional skills are required to operate the dedicated devices,which is not suitable for applications in a ubiquitous environment.Moreover,when multiple people are within the sensing range,the device-free property prevents them from effectively distinguishing multiple human subjects and performing HRV monitoring simultaneously.Therefore,a new solution based on Commercial-off-the-Shelf (COTS)devices is desirable to the above limitations in a lightweight and distinguishable approach. 1.3 Our Approach The emerging RFID technology [15,25,27,34,37,38,40]has brought new opportunities for HRV monitoring in a more convenient and accurate approach,as the RFID tag can be regarded as an extremely lightweight sensor and its nature of identification can be used to effectively and easily distinguish different human subjects.In this paper,we propose RF-ECG,an RFID-based approach to perform HRV monitoring on human subjects,which aims to recover the IBI information with a similar accuracy as the traditional ECG based on the COTS RFID devices. Specifically,instead of designing a dedicated platform [11],we attach a set of COTS RFID tags on the chest area in the clothes of the human subject,which forms a tag array for comprehensive sensing.We deploy a COTS RFID reader to continuously interrogate these tags by issuing a continuous wave,and collect their backscattered RF-signals within the effective scanning range(1 ~3m).Similarly to the traditional ECG-based approaches,we focus on the HRV monitoring with a relatively quite environment,where the users are supposed to keep still during the measurement.Figure 1(a)illustrates the deployment of tag array of RF-ECG,where 6 tags form a 2 x 3 tag array.Through the RF-signal from the tag array,RF-ECG is able to facilitate two main applications for HRV monitoring in daily life.Firstly,by deploying the antenna above the bed,RF-ECG is able to monitor the user's heart status during sleeping.which can further enable the treatment of sleep apnea [1]and sleep stage detection [21]. Such mode can be extended to infant monitoring,because infants spend most of the time sleeping in the cribs. Secondly,since elderly people are usually less active and spend lots of time in performing more stationary activities(e.g,watching TVs),we can deploy the antenna in the living room and bedroom for efficient HRV monitoring.Such HRV monitoring can offer rapid and effective diagnostic clues for the general health evaluation. Toward these monitoring applications,Figure 1(b)further presents the diagram of our HRV monitoring approach. As the heart beats behind the human chest,the continuous wave reflected from the heart movement could be captured by the tag array,and then backscattered to the RFID reader via the RF-signals [41],which we call the reflection effect of the tag array.Meanwhile,the RF-signals are also affected by the chest movements due to human respiration,which we call the moving effect of the tag array.Hence,the RF-signals received by the RFID reader consist of heartbeat signal,respiration signal and the ambient noise from the environment.In this paper,we investigate the possibility of extracting such tiny reflection signals corresponding to the heartbeat, while eliminating other signal interferences from the human respiration and the ambient noise in the multi-path environment.In particular,according to the RF-signals received from the tag array,we develop a mechanism to capture the chest movement,aiming to cancel the respiration signal corresponding to the moving effect.Then,by using the wavelet-based signal denoising,we further extract the heartbeat signals corresponding to the reflection effect.Finally,we build a model to depict the reflection effect on RF-signals of different tags from the tag array, and extract the Inter-Beat Interval(IBI)for HRV monitoring. 1.4 Challenges There are three main challenges in performing the HRV assessment via the RFID based approach.The first challenge is to detect and extract weak heartbeat signals from RFID tags among multiple interferences caused by human respiration and ambient noises.In particular,the signal variation captured by the reflection effect from heartbeat is much weaker than the signal variation caused by the moving effect from respiration.To address Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.Vol.2.No.2.Article 85.Publication date:June 2018.RF-ECG: Heart Rate Variability Assessment Based on COTS RFID Tag Array • 85:3 (e.g., USRP [10] and RFID with conductive fabric electrodes [11]), and thus are expensive compared with the commercial devices. Additionally, some professional skills are required to operate the dedicated devices, which is not suitable for applications in a ubiquitous environment. Moreover, when multiple people are within the sensing range, the device-free property prevents them from effectively distinguishing multiple human subjects and performing HRV monitoring simultaneously. Therefore, a new solution based on Commercial-off-the-Shelf (COTS) devices is desirable to the above limitations in a lightweight and distinguishable approach. 1.3 Our Approach The emerging RFID technology [15, 25, 27, 34, 37, 38, 40] has brought new opportunities for HRV monitoring in a more convenient and accurate approach, as the RFID tag can be regarded as an extremely lightweight sensor and its nature of identification can be used to effectively and easily distinguish different human subjects. In this paper, we propose RF-ECG, an RFID-based approach to perform HRV monitoring on human subjects, which aims to recover the IBI information with a similar accuracy as the traditional ECG based on the COTS RFID devices. Specifically, instead of designing a dedicated platform [11], we attach a set of COTS RFID tags on the chest area in the clothes of the human subject, which forms a tag array for comprehensive sensing. We deploy a COTS RFID reader to continuously interrogate these tags by issuing a continuous wave, and collect their backscattered RF-signals within the effective scanning range (1 ∼ 3m). Similarly to the traditional ECG-based approaches, we focus on the HRV monitoring with a relatively quite environment, where the users are supposed to keep still during the measurement. Figure 1(a) illustrates the deployment of tag array of RF-ECG, where 6 tags form a 2 × 3 tag array. Through the RF-signal from the tag array, RF-ECG is able to facilitate two main applications for HRV monitoring in daily life. Firstly, by deploying the antenna above the bed, RF-ECG is able to monitor the user’s heart status during sleeping, which can further enable the treatment of sleep apnea [1] and sleep stage detection [21]. Such mode can be extended to infant monitoring, because infants spend most of the time sleeping in the cribs. Secondly, since elderly people are usually less active and spend lots of time in performing more stationary activities (e.g., watching TVs), we can deploy the antenna in the living room and bedroom for efficient HRV monitoring. Such HRV monitoring can offer rapid and effective diagnostic clues for the general health evaluation. Toward these monitoring applications, Figure 1(b) further presents the diagram of our HRV monitoring approach. As the heart beats behind the human chest, the continuous wave reflected from the heart movement could be captured by the tag array, and then backscattered to the RFID reader via the RF-signals [41], which we call the reflection effect of the tag array. Meanwhile, the RF-signals are also affected by the chest movements due to human respiration, which we call the moving effect of the tag array. Hence, the RF-signals received by the RFID reader consist of heartbeat signal, respiration signal and the ambient noise from the environment. In this paper, we investigate the possibility of extracting such tiny reflection signals corresponding to the heartbeat, while eliminating other signal interferences from the human respiration and the ambient noise in the multi-path environment. In particular, according to the RF-signals received from the tag array, we develop a mechanism to capture the chest movement, aiming to cancel the respiration signal corresponding to the moving effect. Then, by using the wavelet-based signal denoising, we further extract the heartbeat signals corresponding to the reflection effect. Finally, we build a model to depict the reflection effect on RF-signals of different tags from the tag array, and extract the Inter-Beat Interval (IBI) for HRV monitoring. 1.4 Challenges There are three main challenges in performing the HRV assessment via the RFID based approach. The first challenge is to detect and extract weak heartbeat signals from RFID tags among multiple interferences caused by human respiration and ambient noises. In particular, the signal variation captured by the reflection effect from heartbeat is much weaker than the signal variation caused by the moving effect from respiration. To address Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 2, Article 85. Publication date: June 2018