85:6·C.Wang et al. -35 -Tagl (ue!ped) -Tagl 40 Tag2 ap) Tag3 Tag2 Tag4 00a00a090a Tag3 Tag5 -Tag4 -47 Tag6 Tag5 Tag6 .50 2 4 6 2 6 Time (s) Time (s) (a)RSSI trend in bag swing (b)Phase trend in bag swing Fig.3.Preliminary study:Measurement of periodic signal on the RFID tag array. generate a periodic signal source by swinging a water bag as a plummet and sense the period of the water bag from a pre-deployed tag array.As shown in Figure 2,we hang a water bag to emulate the periodic signal and investigate the signal changes as we swing the water bag.A 2 x 3 tag array is deployed on the surface of a box, which is 10cm away from the water bag,to sense the periodic signal of the water bag.The size of the tag array is designed based on the size of human chest,so that the same tag array can be deployed on the chest area in the clothes of the human subject.An RFID antenna is deployed 2m away from the tag array to continuously interrogate the tags on the array. We observe that both the phase and RSSI waves have clear periodic patterns during the swing process of the water bag,but the periodic shapes are different from each tag.In particular,we swing the water bag as a pendulum to generate the periodic signal.The length of the pendulum is 25cm so that the period is about 1Hz in our setup. We show both the RSSI and phase trend of the selected tags from the tag array,respectively,in Figure 3(a)and Figure 3(b).We can clearly observe the periodical pattern in the waves of RSSI and phase trend during the swing process of water bag.The reason is that,the swing periodically changes the position of the reflection surface, thus the propagation paths(i.e.,multi-path effect)of the RF-signal are changed as well,which further leads to the periodical variation in the RF-signals.In regard to the difference of the absolute RSSI value,it is caused by the different positions of each tag.Moreover,even if all the tags present the same cycle time,the exact shape of their waveforms are different among the tags.This indicates that different tags on the tag array have different sensitivities to the reflection effect of the swing bag.We will build a theoretical model to explain the phenomenon later in Section 3.3. 3.2 Measurement of HRV in Real Settings We further investigate how the actual heartbeat affects the RF-signals in real settings.Specifically,we attach a 2 x 3 tag array on the chest area in the clothes of the human subject.We first let the human subject hold the breath for 20s,indicating that the chest movement of breathing can be negligible.We then let the human subject breathe normally for 20s,and thus there exists obvious chest movement of breathing.We respectively collect the phase/RSSI values of these two sets.We select an arbitrary tag from the tag array and present the results in Figure 4. According to Figure 4(a),we can observe weak but fairly clear periodic heartbeat patterns from the phase sequences,since the chest movement of breathing can be negligible.According to Figure 4(b),we can observe obvious periodic respiration patterns for the chest movement of breathing,as the moving effect due to the chest movement clearly changes the phase values.However,the periodic heartbeat patterns can hardly be detected Proc.ACM Interact.Mob.Wearable Ubiquitous Technol,Vol.2,No.2,Article 85.Publication date:June 2018
85:6 • C. Wang et al. Time (s) 0 2 4 6 8 RSSI (dBm) -50 -45 -40 -35 Tag1 Tag2 Tag3 Tag4 Tag5 Tag6 (a) RSSI trend in bag swing Time (s) 0 2 4 6 8 Phase (Radian) -2 -1 0 1 2 Tag1 Tag2 Tag3 Tag4 Tag5 Tag6 (b) Phase trend in bag swing Fig. 3. Preliminary study: Measurement of periodic signal on the RFID tag array. generate a periodic signal source by swinging a water bag as a plummet and sense the period of the water bag from a pre-deployed tag array. As shown in Figure 2, we hang a water bag to emulate the periodic signal and investigate the signal changes as we swing the water bag. A 2 × 3 tag array is deployed on the surface of a box, which is 10cm away from the water bag, to sense the periodic signal of the water bag. The size of the tag array is designed based on the size of human chest, so that the same tag array can be deployed on the chest area in the clothes of the human subject. An RFID antenna is deployed 2m away from the tag array to continuously interrogate the tags on the array. We observe that both the phase and RSSI waves have clear periodic patterns during the swing process of the water bag, but the periodic shapes are different from each tag. In particular, we swing the water bag as a pendulum to generate the periodic signal. The length of the pendulum is 25cm so that the period is about 1Hz in our setup. We show both the RSSI and phase trend of the selected tags from the tag array, respectively, in Figure 3(a) and Figure 3(b). We can clearly observe the periodical pattern in the waves of RSSI and phase trend during the swing process of water bag. The reason is that, the swing periodically changes the position of the reflection surface, thus the propagation paths (i.e., multi-path effect) of the RF-signal are changed as well, which further leads to the periodical variation in the RF-signals. In regard to the difference of the absolute RSSI value, it is caused by the different positions of each tag. Moreover, even if all the tags present the same cycle time, the exact shape of their waveforms are different among the tags. This indicates that different tags on the tag array have different sensitivities to the reflection effect of the swing bag. We will build a theoretical model to explain the phenomenon later in Section 3.3. 3.2 Measurement of HRV in Real Settings We further investigate how the actual heartbeat affects the RF-signals in real settings. Specifically, we attach a 2 × 3 tag array on the chest area in the clothes of the human subject. We first let the human subject hold the breath for 20s, indicating that the chest movement of breathing can be negligible. We then let the human subject breathe normally for 20s, and thus there exists obvious chest movement of breathing. We respectively collect the phase/RSSI values of these two sets. We select an arbitrary tag from the tag array and present the results in Figure 4. According to Figure 4(a), we can observe weak but fairly clear periodic heartbeat patterns from the phase sequences, since the chest movement of breathing can be negligible. According to Figure 4(b), we can observe obvious periodic respiration patterns for the chest movement of breathing, as the moving effect due to the chest movement clearly changes the phase values. However, the periodic heartbeat patterns can hardly be detected 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.85:7 2.2 -52 -525 10 a的人心 10 10 15 Time(s) Time(s) Frequency(Hz) (a)The phase/RSSI sequence without breath-(b)The phase/RSSI sequence with breathing(c)Frequency domain analysis of the phase ing sequence Fig.4.Preliminary study:measurement of HRV with and without breathing. anymore,since the reflection effect is orders of magnitude smaller than the moving effect in the tag array.Besides, we cannot detect any periodic patterns from the RSSI sequences in both situations,since the resolution of RSSI is rather coarse-grained.We further perform FFT analysis on the phase sequences from the frequency domain. Figure 4(c)shows the experiment results in both situations.For the situation without breathing,we can clearly find a small peak at 1.355Hz,which is corresponding to the heartbeat frequency band.This asserts the reflection effect exists but relatively small.For the situation with breathing,we can clearly detect a peak at 0.2Hz,which corresponds to the respiration frequency band,besides,we can only detect a very small peak at 1.3Hz,which is almost buried in the noises.Therefore,to obtain the precise heartbeat signal,it is essential to first remove the influence of respiration and then strengthen the heartbeat reflection. 3.3 Modeling HRV via Tag Array Sensing In this subsection,we model the relationship between the RF-signals from the tag array and the heart displacement in the reflection effect,which is the periodic signal of the heartbeat. 3.3.1 Extracting the Reflection Signal.To understand how the heart displacement affects the reflection effect,it is essential to extract the reflection signal from the received RF-signal,i.e.,the RF-signal reflected from the heart to the tags.Hence,we use a signal propagation model to depict the RF-signal transmission with the reflection effect.As shown in Figure 5,we use A,T,B and C to denote the RFID antenna,RFID tag,reflection object and background environment,respectively.First,the antenna A sends the continuous wave to activate the tags.Due to the multi-path effect,the tagT receives a superposed signal,which contains the line-of-sight(LOS)signal SaT(blue line),the reflection signal Sa from the reflection object B(red line),as well as the reflection signal Sac from the background environment (green line).Then,after the specified tag is successfully activated,it backscatters the signal to the antenna with necessary data modulation.Hence,the raw signal received by the antenna A can be represented as: S,=hT+ah(S-+T+SA-B-T+S领-→+C→T), (1) where h represents the signal attenuation due to propagation path loss and h is the reflection coefficient of the tag.Since both the LOS signal and the reflection signal from the background environment are usually stable during the whole propagation,we combine them as Sr.0=hTah(Sa-T+SA→c→T), (2) and denote the remained reflection signal from the reflection object as Sr.1=hT-→ahrSa→B-→T (3) 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 • 85:7 Time (s) 0 5 10 15 20 Phase (Radian) 1.8 2 2.2 RSSI (dBm) -54 -53 -52 (a) The phase/RSSI sequence without breathing Time (s) 0 5 10 15 20 Phase (Radian) 1.7 1.9 2.1 RSSI (dBm) -54.5 -53.5 -52.5 (b) The phase/RSSI sequence with breathing Frequency (Hz) 0 0.5 1 1.5 2 FFT 0 50 100 150 Phase without breath Phase with breath (c) Frequency domain analysis of the phase sequence Fig. 4. Preliminary study: measurement of HRV with and without breathing. anymore, since the reflection effect is orders of magnitude smaller than the moving effect in the tag array. Besides, we cannot detect any periodic patterns from the RSSI sequences in both situations, since the resolution of RSSI is rather coarse-grained. We further perform FFT analysis on the phase sequences from the frequency domain. Figure 4(c) shows the experiment results in both situations. For the situation without breathing, we can clearly find a small peak at 1.355Hz, which is corresponding to the heartbeat frequency band. This asserts the reflection effect exists but relatively small. For the situation with breathing, we can clearly detect a peak at 0.2Hz, which corresponds to the respiration frequency band, besides, we can only detect a very small peak at 1.3Hz, which is almost buried in the noises. Therefore, to obtain the precise heartbeat signal, it is essential to first remove the influence of respiration and then strengthen the heartbeat reflection. 3.3 Modeling HRV via Tag Array Sensing In this subsection, we model the relationship between the RF-signals from the tag array and the heart displacement in the reflection effect, which is the periodic signal of the heartbeat. 3.3.1 Extracting the Reflection Signal. To understand how the heart displacement affects the reflection effect, it is essential to extract the reflection signal from the received RF-signal, i.e., the RF-signal reflected from the heart to the tags. Hence, we use a signal propagation model to depict the RF-signal transmission with the reflection effect. As shown in Figure 5, we use A, T, B and C to denote the RFID antenna, RFID tag, reflection object and background environment, respectively. First, the antenna A sends the continuous wave to activate the tags. Due to the multi-path effect, the tag T receives a superposed signal, which contains the line-of-sight (LOS) signal SA→T (blue line), the reflection signal SA→B→T from the reflection object B (red line), as well as the reflection signal SA→C→T from the background environment (green line). Then, after the specified tag is successfully activated, it backscatters the signal to the antenna with necessary data modulation. Hence, the raw signal received by the antenna A can be represented as: Sr = hT→AhT(SA→T + SA→B→T + SA→C→T), (1) where hT→A represents the signal attenuation due to propagation path loss and hT is the reflection coefficient of the tag. Since both the LOS signal and the reflection signal from the background environment are usually stable during the whole propagation, we combine them as Sr,0 = hT→AhT(SA→T + SA→C→T), (2) and denote the remained reflection signal from the reflection object as Sr,1 = hT→AhTSA→B→T. (3) Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 2, Article 85. Publication date: June 2018
85:8·C.Wang et al. Reflection Object(Heart) Sr =hT-AhT (SA+B→T+SAT+Sc→T) dA-B △dBT≈△h cos pr Ah PT D'D C SA-C-T Fig.5.Reflection model of heart displacement. To extract the reflection signal S.1 from the superposed signal S,we could subtract the complex signal S.o[41]. which can be measured simply by removing the reflection object. Figure 6 shows the RSSI and phase variation of the reflection signals from periodic signal of the water bag according to the subtraction Sr.1=S,-Sr.0.We note that,for each reflection signal,the RSSI variation is irregular and its value is usually less than -60dBm,as the power of the reflection signal is rather weak and easy to be affected.In fact,since the reflection object,ie.,the water bag,only moves a small distance,theoretically the RSSI variation should be very small according to Friis Equation [16].Meanwhile,the phases of the reflection signal from different tags all have obvious periodical patterns in the waveforms.Moreover,they not only have some similarities in the waveform contours,but also have differences in the waveform details among each other. Therefore,this implies that all the tags are subjected to similar reflection influence,but the performance in sensitivity are different among different tags. 3.3.2 Estimating Heart Displacement via a Single Tag.After extracting the reflection signal,it is essential to further figure out the relationship between the heart displacement and the reflection RF-signal.Since the phase of reflection signal is more sensitive to the heart displacement than RSSI,we use the phase as a metric for RF-signal to depict the corresponding relationship.According to Eq.(3),let Abe the wave length of RF-signal,the phase of reflection signal S,.can be calculated from the reflection path length as: 8g=2x(女月+d-8+d87+0eon)mod2元， (4) 入 where the superscript'indicates the reflection signal and Ocons is the constant phase deviation due to reflection. Since d is fixed during the whole propagation and econs is constant,when the reflection object moves from BtoB',the phase change△9only depends on the change of path length△da-→sand△dg-T,as: △85=2mAdA-8+Adg-7 mod 2. (5) 入 Without loss of generality,we first consider the path length change Adg.Assume that the points D and D'are respectively the projection of the points B and B'on the horizontal line of T in Figure 5,then the edge length dsp =dg'p.When the reflection object moves from B to B',the reflection path changes from ds to dgT,which are,respectively,the hypotenuses of two right triangles ABDT and AB'D'T.Let or and to denote the angle /BTD and /B'T D',respectively.Then,the path length change Ads-can be calculated as follows: △dg→T=dgT-dsT= dsp dsD (6) sin sinor Proc.ACM Interact.Mob.Wearable Ubiquitous Technol.,Vol.2,No.2,Article 85.Publication date:June 2018
85:8 • C. Wang et al. B A T B‘ !" #$ #$ % !&'() * !" +,- #) ./ 0 ")(1") 2.1('() 3 .1() 3 .4()5 D’ D Reflection Object (Heart) #1 C Fig. 5. Reflection model of heart displacement. To extract the reflection signal Sr,1 from the superposed signal Sr , we could subtract the complex signal Sr,0 [41], which can be measured simply by removing the reflection object. Figure 6 shows the RSSI and phase variation of the reflection signals from periodic signal of the water bag according to the subtraction Sr,1 = Sr −Sr,0. We note that, for each reflection signal, the RSSI variation is irregular and its value is usually less than −60dBm, as the power of the reflection signal is rather weak and easy to be affected. In fact, since the reflection object, i.e., the water bag, only moves a small distance, theoretically the RSSI variation should be very small according to Friis Equation [16]. Meanwhile, the phases of the reflection signal from different tags all have obvious periodical patterns in the waveforms. Moreover, they not only have some similarities in the waveform contours, but also have differences in the waveform details among each other. Therefore, this implies that all the tags are subjected to similar reflection influence, but the performance in sensitivity are different among different tags. 3.3.2 Estimating Heart Displacement via a Single Tag. After extracting the reflection signal, it is essential to further figure out the relationship between the heart displacement and the reflection RF-signal. Since the phase of reflection signal is more sensitive to the heart displacement than RSSI, we use the phase as a metric for RF-signal to depict the corresponding relationship. According to Eq.(3), let λ be the wave length of RF-signal, the phase of reflection signal Sr,1 can be calculated from the reflection path length as: θ r T = 2π( dT→A + dA→B + dB→T λ + θcons) mod 2π, (4) where the superscript r indicates the reflection signal and θcons is the constant phase deviation due to reflection. Since dT→A is fixed during the whole propagation and θcons is constant, when the reflection object moves from B to B ′ , the phase change ∆θ r T only depends on the change of path length ∆dA→B and ∆dB→T, as: ∆θ r T = 2π ∆dA→B + ∆dB→T λ mod 2π. (5) Without loss of generality, we first consider the path length change ∆dB→T. Assume that the points D and D′ are respectively the projection of the points B and B ′ on the horizontal line of T in Figure 5, then the edge length dBD = dB′D′. When the reflection object moves from B to B ′ , the reflection path changes from dB→T to dB′→T, which are, respectively, the hypotenuses of two right triangles △BDT and △B′D′T. Let ϕT and ϕ ′ T to denote the angle ∠BT D and ∠B ′T D′ , respectively. Then, the path length change ∆dB→T can be calculated as follows: ∆dB→T = dB′T − dBT = dBD sinϕ ′ T − dBD sinϕT . (6) 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.85:9 -60 -Tagl Tagl -Tag2 Tag2 Tag3 Tag3 Tag4 0 Tag4 Tag5 Tag5 80 -Tag6 Tag6 -90 2 6 0 2 6 8 Time (s) Time (s) (a)The RSSI of the reflection signal (b)The phase of the reflection signal Fig.6.The extracted reflection signal. Let Ah be the displacement of the reflection object(i.e.,the heart displacement dgs),it is essentially equal to △h=dgg=dpD= dgD」 dsD tan (7) tano By combining the two equations to remove dsD,we obtain sino-sin △dg-T=△ (8) sin(or-) If we define the angle deviation Aor=or-o,then o=or-Aor,thus △dg-T=△h(cos+sin 1-cos△9虹). (9) sin△pr In regard to the heartbeat,the reflection object(ie.,the heart)actually moves a rather small distance Ah,thus the angle deviation.Hence=tan.Therefore,Eq()can be simplified as follows: △dgT≈△h cos r. (10) Similarly,for the path length change Ads,let denote the angle between AB and the horizontal line,we also obtain△d→s≈△hcosoa, Therefore,by combining Eq.(10)and Eq.(5),we obtain △8g≈2 Ah(cos97+cosp》 mod 2. (11) Hence,for any arbitrary tag T.given the phase change of the reflection signal A0,the heart displacement Ah can be estimated as follows: (△G+2πk)n △h≈ 2π(cosr+cosoa) ,where k=·,-1,0,1, (12) Here,k represents the periods of the phase values.Since the heart displacement is rather small,k equals to 0 in our problem. 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 • 85:9 Time (s) 0 2 4 6 8 RSSI (dBm) -90 -80 -70 -60 Tag1 Tag2 Tag3 Tag4 Tag5 Tag6 (a) The RSSI of the reflection signal Time (s) 0 2 4 6 8 Phase (Radian) -4 -2 0 2 4 Tag1 Tag2 Tag3 Tag4 Tag5 Tag6 (b) The phase of the reflection signal Fig. 6. The extracted reflection signal. Let ∆h be the displacement of the reflection object (i.e., the heart displacement dB′B), it is essentially equal to ∆h = dB′B = dD′D = dBD tanϕ ′ T − dBD tanϕT . (7) By combining the two equations to remove dBD, we obtain ∆dB→T = ∆h sinϕT − sinϕ ′ T sin(ϕT − ϕ ′ T ) . (8) If we define the angle deviation ∆ϕT = ϕT − ϕ ′ T , then ϕ ′ T = ϕT − ∆ϕT, thus ∆dB→T = ∆h(cosϕT + sinϕT 1 − cos ∆ϕT sin ∆ϕT ). (9) In regard to the heartbeat, the reflection object (i.e., the heart) actually moves a rather small distance ∆h, thus the angle deviation ∆ϕT → 0. Hence, 1−cos ∆ϕT sin ∆ϕT = tan ∆ϕT 2 → 0. Therefore, Eq (9) can be simplified as follows: ∆dB→T ≈ ∆h cosϕT. (10) Similarly, for the path length change ∆dA→B, let ϕA denote the angle between AB and the horizontal line, we also obtain ∆dA→B ≈ ∆h cosϕA . Therefore, by combining Eq. (10) and Eq. (5), we obtain ∆θ r T ≈ 2π∆h(cosϕT + cosϕA) λ mod 2π. (11) Hence, for any arbitrary tag T , given the phase change of the reflection signal ∆θ r T , the heart displacement ∆h can be estimated as follows: ∆h ≈ (∆θ r T + 2πk)λ 2π(cosϕT + cosϕA) , where k = · · · , −1, 0, 1, · · · (12) Here, k represents the periods of the phase values. Since the heart displacement is rather small, k equals to 0 in our problem. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 2, No. 2, Article 85. Publication date: June 2018