is that we can precisely measure both the amplitude and the 4.0 L05 phase of individual reflection paths.Then,RESPTRACKER Direct turns the indoor multipath effect into our friends by recom- Multipath bining the multipath signals belonging to the same user.Our Reflections signal combination algorithm performs a multi-dimensional 70,0 140,0210.0280.0350.0420,0490.0560.0630.0700.0 Distance (cm) search and analysis among different distances,multiple re- ceiving microphones,and different time-frames,based on the (a)CIR amplitude of a single frame amplitude and phase measurement of the ZC signal.In this €2800 Multipath way,we can reliably cluster reflection paths to different users g2100 Reflections even if they have similar respiration rates.With our two- 140.0 stage scheme,RESPTRACKER can detect reliable single person 700 respiration signal at a distance of 3 meters and track the 060 6.0 120 180 24.0 movement of each user within 20 seconds after movements. Time (s) And,we can also separate multiple subjects'respiration and (b)Time variations of CIR amplitudes. Amplitude at 50cm Amp ude at80c track each of them in domestic settings. Technical Challenges and Solutions:The first challenge is to reliably separate multiple breath signals.Existing work for multi-user breath detection [9]leverages the ICA algorithm to extract different subjects'respiration.As multiple reflections 120 18D 240 Time (s) of wireless signal are mixed at the receiver due to the limited range resolution,they need a reliable decomposition algorithm (c)Filtered CIR amplitude at different distances. d Waveform Ground Truth Waveform to separate them.To address this challenge,we use the ZC sequence to distinguish different sound reflection paths with a high resolution of less than 10cm.In addition,we can measure the features of individual paths in terms of the channel impulse response (CIR).In this way,each path contains less 60 12.0 18.0 24.0 Time (s) interference of other subjects so that the difficulty of signal (d)Reconstructed respiration signal. decomposition is greatly reduced. The second challenge is to expand the monitor range to the Figure 2.CIR waveform of a single subject room-scale.Since the ultrasonic signal attenuates quickly in indoor environments,the measurement of a single path could be noisy and inaccurate.Traditional delay-and-sum algorithm determine whether there are users'movements and then track for beamforming blindly combines signals from the same the distance change of each reflection path.Therefore,we can distance and angle where the weak respiration signal may be quickly use the historical data to regain synchronization within destroyed by the out-of-phase combination.To resolve this twenty seconds after the movement. issue,we use a multi-dimensional signal combination scheme Summary of Experimental results:In the single user to select and recombine the respiration signals from the same scenario,our system can robustly estimate the respiration rate user.We first leverage multiple microphones that are common with an error under 0.6 Beats per Minute (BPM)for different on COTS devices,such as Amazon Echo and Google Home, environments,such as in the hallway,offices.and conference to collect multiple copies of the sound reflections.Based on rooms.RESPTRACKER can also achieve an error of less than 1 the multipath phenomenon,we collect sound reflections on BPM within a distance of three meters and maintain an error of paths at different distances that arrive at the same microphone. less than 0.8 BPM while the user is moving.In the multi-user By clustering these multi-dimensional reflection signals.we scenario,RESPTRACKER can separate the respiration signal of can determine whether a given path on a given microphone more than four users in the same room and achieve an error contains the respiration signal and which user the respiration of less than 1 BPM for each user. signal belongs to.In this way,we are able to combine a II.SYSTEM OVERVIEW large number of weak paths from the same user,thereby reconstructing the respiration signal reliably and achieving RESPTRACKER aims at multiple-person room-scale respir- long-distance monitoring. ation tracking.Therefore,the system is supposed to detect and The third challenge is to track the respiration signal while separate the weak reflection signals at a long range reliably. the subject is moving.As users may not keep static in their daily routine,our monitoring system should be able to keep A.Design Motivations tracking while users change their position or orientation. To understand the design challenges for long-range respir- To achieve respiration tracking under dynamic position and ation signal detection and separation,we provide a typical orientation,we divide the signal into short observation slots respiration signal illustration in Figure 2.Figure 2(a)shows with a duration of twenty seconds.Within each slot,we first the amplitude of multipath signals at different distances,whereis that we can precisely measure both the amplitude and the phase of individual reflection paths. Then, RESPTRACKER turns the indoor multipath effect into our friends by recom￾bining the multipath signals belonging to the same user. Our signal combination algorithm performs a multi-dimensional search and analysis among different distances, multiple re￾ceiving microphones, and different time-frames, based on the amplitude and phase measurement of the ZC signal. In this way, we can reliably cluster reflection paths to different users even if they have similar respiration rates. With our two￾stage scheme, RESPTRACKER can detect reliable single person respiration signal at a distance of 3 meters and track the movement of each user within 20 seconds after movements. And, we can also separate multiple subjects’ respiration and track each of them in domestic settings. Technical Challenges and Solutions: The first challenge is to reliably separate multiple breath signals. Existing work for multi-user breath detection [9] leverages the ICA algorithm to extract different subjects’ respiration. As multiple reflections of wireless signal are mixed at the receiver due to the limited range resolution, they need a reliable decomposition algorithm to separate them. To address this challenge, we use the ZC sequence to distinguish different sound reflection paths with a high resolution of less than 10cm. In addition, we can measure the features of individual paths in terms of the channel impulse response (CIR). In this way, each path contains less interference of other subjects so that the difficulty of signal decomposition is greatly reduced. The second challenge is to expand the monitor range to the room-scale. Since the ultrasonic signal attenuates quickly in indoor environments, the measurement of a single path could be noisy and inaccurate. Traditional delay-and-sum algorithm for beamforming blindly combines signals from the same distance and angle where the weak respiration signal may be destroyed by the out-of-phase combination. To resolve this issue, we use a multi-dimensional signal combination scheme to select and recombine the respiration signals from the same user. We first leverage multiple microphones that are common on COTS devices, such as Amazon Echo and Google Home, to collect multiple copies of the sound reflections. Based on the multipath phenomenon, we collect sound reflections on paths at different distances that arrive at the same microphone. By clustering these multi-dimensional reflection signals, we can determine whether a given path on a given microphone contains the respiration signal and which user the respiration signal belongs to. In this way, we are able to combine a large number of weak paths from the same user, thereby reconstructing the respiration signal reliably and achieving long-distance monitoring. The third challenge is to track the respiration signal while the subject is moving. As users may not keep static in their daily routine, our monitoring system should be able to keep tracking while users change their position or orientation. To achieve respiration tracking under dynamic position and orientation, we divide the signal into short observation slots with a duration of twenty seconds. Within each slot, we first (a) CIR amplitude of a single frame. (b) Time variations of CIR amplitudes. 0.0 6.0 12.0 18.0 24.0 30.0 Time (s) 0.0 0.5 1.0 Normalized Amplitude Amplitude at 50cm Amplitude at 80cm (c) Filtered CIR amplitude at different distances. 0.0 6.0 12.0 18.0 24.0 30.0 Time (s) -0.2 0.0 0.2 Normalized Amplitude Reconstructed Waveform Ground Truth Waveform (d) Reconstructed respiration signal. Figure 2. CIR waveform of a single subject determine whether there are users’ movements and then track the distance change of each reflection path. Therefore, we can quickly use the historical data to regain synchronization within twenty seconds after the movement. Summary of Experimental results: In the single user scenario, our system can robustly estimate the respiration rate with an error under 0.6 Beats per Minute (BPM) for different environments, such as in the hallway, offices, and conference rooms. RESPTRACKER can also achieve an error of less than 1 BPM within a distance of three meters and maintain an error of less than 0.8 BPM while the user is moving. In the multi-user scenario, RESPTRACKER can separate the respiration signal of more than four users in the same room and achieve an error of less than 1 BPM for each user. II. SYSTEM OVERVIEW RESPTRACKER aims at multiple-person room-scale respir￾ation tracking. Therefore, the system is supposed to detect and separate the weak reflection signals at a long range reliably. A. Design Motivations To understand the design challenges for long-range respir￾ation signal detection and separation, we provide a typical respiration signal illustration in Figure 2. Figure 2(a) shows the amplitude of multipath signals at different distances, where