85:4·C.Wang et al. this challenge,we develop a mechanism to depict the RF-signal variation of the tag array caused by the chest movement,helping to eliminate the part of the respiration signals corresponding to the moving effect.We further build a model to depict the reflection effect on RF-signals of different tags from the tag array.By fusing the RF-signals from multiple tags,we are able to strengthen the heartbeat signals while suppressing the signal interferences from the others. The second challenge is to achieve a fine-grained heart beat estimation for the HRV assessment according to the reflection effect.Instead of simply calculating the heart rate,which could be estimated through the Fast Fourier Transform results,the fine-grained HRV assessment requires to estimate the beat-to-beat interval by performing accurate beat segmentation.To address this challenge,after eliminating the respiration signals,we apply the wavelet-based denoising method to further filter out the ambient noise signals outside the frequency band of heart rate.The filtered signals thus can show a clear periodic pattern to facilitate fine-grained IBI segmentation.Finally, we propose a PCA-based scheme to derive a template to depict the inter-beat signals and use it to iteratively perform the IBI segmentation. The third challenge is to understand the sensing mechanism of RFID tag array and leverage the RFID tag array to perform accurate sensing on HRV.Specifically,as multiple tags are deployed on the human body,these tags can be regarded as a non-rigid body array to perceive the moving effect,since the tags may have relative displacement during the process of respiration;they can also be regarded as a rigid body array to perceive the reflection effect,after eliminating the respiration signal from the received RF-signal.To fuse multiple RF-signals from the tag array for accurate sensing,we capture the relationship between the RF-signals from the tag array and the corresponding movement from the heartbeat or respiration,via our reflection effect model and the moving effect mechanism,respectively.Based on the above techniques,we are able to perform data fusion over RF-signals from the RFID tag array for accurate sensing on HRV. 1.5 Contributions This paper makes four contributions:First,to the best of our knowledge,this is the first work that investigates the feasibility of Heart Rate Variability assessment based only on the COTS RFID.We leverage the RFID tag array to perform accurate sensing on HRV assessment in a lightweight and distinguishable approach.Second,we have conducted in-depth investigation on the sensing mechanism of RFID tag array.We develop a reflection effect model and a moving effect mechanism,respectively,to capture the relationship between the RF-signals from the tag array and the corresponding movement from the heart beat or respiration.Third,we design novel algorithms to extract the HRV from the RF-signals mixed with heartbeat signals,respiration signals,and ambient noises.We use wavelet-based signal denoising and signal fusion from tag array to remove the interferences to extract the IBI from the heartbeat signals.Fourth,we implement a system prototype for HRV assessment in the practical environment,and evaluate the performance with extensive experiments.Experiment results show that RF-ECG can achieve a median IBI error of 24ms,i.e,about 3%error of a normal IBI value,which is compatible to existing wired techniques. 2 RELATED WORK Sensor-based heartbeat detection:Photoplethysmogram(PPG)sensors are widely used for heart rate estima- tion by using a pulse oximeter,which illuminates the skin and measures the changes in light absorption [8]. Current wearable devices(e.g.,smartwatches and fitness trackers [3-5])usually use the PPG-based technology to measure the heart rate.However,body motion can easily distort the waveform of PPG,which causes large noise for heart rate sensing.To address this issue,recent research employs the inertial measurement units(IMU)to improve the overall accuracy of PPG [12,18].Besides PPG,Zou et al.develop a heart rate monitoring system using nanofiber-based strain sensors,which could be more compliant and comfortable [43].However,these Proc.ACM Interact.Mob.Wearable Ubiquitous Technol..Vol 2.No.2.Article 85.Publication date:lune 2018.85:4 • C. Wang et al. this challenge, we develop a mechanism to depict the RF-signal variation of the tag array caused by the chest movement, helping to eliminate the part of the respiration signals corresponding to the moving effect. We further build a model to depict the reflection effect on RF-signals of different tags from the tag array. By fusing the RF-signals from multiple tags, we are able to strengthen the heartbeat signals while suppressing the signal interferences from the others. The second challenge is to achieve a fine-grained heart beat estimation for the HRV assessment according to the reflection effect. Instead of simply calculating the heart rate, which could be estimated through the Fast Fourier Transform results, the fine-grained HRV assessment requires to estimate the beat-to-beat interval by performing accurate beat segmentation. To address this challenge, after eliminating the respiration signals, we apply the wavelet-based denoising method to further filter out the ambient noise signals outside the frequency band of heart rate. The filtered signals thus can show a clear periodic pattern to facilitate fine-grained IBI segmentation. Finally, we propose a PCA-based scheme to derive a template to depict the inter-beat signals and use it to iteratively perform the IBI segmentation. The third challenge is to understand the sensing mechanism of RFID tag array and leverage the RFID tag array to perform accurate sensing on HRV. Specifically, as multiple tags are deployed on the human body, these tags can be regarded as a non-rigid body array to perceive the moving effect, since the tags may have relative displacement during the process of respiration; they can also be regarded as a rigid body array to perceive the reflection effect, after eliminating the respiration signal from the received RF-signal. To fuse multiple RF-signals from the tag array for accurate sensing, we capture the relationship between the RF-signals from the tag array and the corresponding movement from the heartbeat or respiration, via our reflection effect model and the moving effect mechanism, respectively. Based on the above techniques, we are able to perform data fusion over RF-signals from the RFID tag array for accurate sensing on HRV. 1.5 Contributions This paper makes four contributions: First, to the best of our knowledge, this is the first work that investigates the feasibility of Heart Rate Variability assessment based only on the COTS RFID. We leverage the RFID tag array to perform accurate sensing on HRV assessment in a lightweight and distinguishable approach. Second, we have conducted in-depth investigation on the sensing mechanism of RFID tag array. We develop a reflection effect model and a moving effect mechanism, respectively, to capture the relationship between the RF-signals from the tag array and the corresponding movement from the heart beat or respiration. Third, we design novel algorithms to extract the HRV from the RF-signals mixed with heartbeat signals, respiration signals, and ambient noises. We use wavelet-based signal denoising and signal fusion from tag array to remove the interferences to extract the IBI from the heartbeat signals. Fourth, we implement a system prototype for HRV assessment in the practical environment, and evaluate the performance with extensive experiments. Experiment results show that RF-ECG can achieve a median IBI error of 24ms, i.e., about 3% error of a normal IBI value, which is compatible to existing wired techniques. 2 RELATED WORK Sensor-based heartbeat detection: Photoplethysmogram (PPG) sensors are widely used for heart rate estima￾tion by using a pulse oximeter, which illuminates the skin and measures the changes in light absorption [8]. Current wearable devices (e.g., smartwatches and fitness trackers [3–5]) usually use the PPG-based technology to measure the heart rate. However, body motion can easily distort the waveform of PPG, which causes large noise for heart rate sensing. To address this issue, recent research employs the inertial measurement units (IMU) to improve the overall accuracy of PPG [12, 18]. Besides PPG, Zou et al. develop a heart rate monitoring system using nanofiber-based strain sensors, which could be more compliant and comfortable [43]. However, these Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 2, Article 85. Publication date: June 2018