A Vital Sign and Sleep Monitoring Using Millimeter Wave

Transcription

1 A Vital Sign an Sleep Monitoring Using Millimeter Wave ZHICHENG YANG, University of California, Davis PARTH H. PATHAK, George Mason University YUNZE ZENG, University of California, Davis XIXI LIRAN, University of California, Davis PRASANT MOHAPATRA, University of California, Davis Continuous monitoring of human s breathing an heart rates is useful in maintaining better health an early etection of many health issues. Designing a technique that can enable contactless an ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmvital, a system that uses 6 GHz millimeter wave (mmwave) signals for vital sign monitoring. We show that the mmwave signals can be irecte to human s boy an the RSS of the reflections can be analyze for accurate estimation of breathing an heart rates. We show how the irectional beams of mmwave can be use to monitor multiple humans in an inoor space concurrently. mmvital also provies sleep monitoring with sleeping posture ientification an etection of central apnea an hypopnea events. It relies on a novel human fining proceure where a human can be locate within a room by reflection loss base object/human classification. We evaluate mmvital using a 6 GHz testbe in home an office environment an show that it provies the mean estimation error of.43 Bpm (breathing rate) an.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 1 ms of well time on reflection. We also emonstrate that mmvital is effective in monitoring multiple people in parallel an even behin a wall. 1. INTRODUCTION Monitoring vital signs such as breathing rate an heart rate can provie crucial insights in human s well-being an can etect a wie range of meical problems. Continuous an ubiquitous monitoring of person s vital signs is a challenging problem an the current solutions require the person to wear eicate evices. Wearable evices such as wrist-worn heart rate monitors, chest straps for breathing rate etection are require to be connecte to the human s boy at all times (even uring sleep), making them a less convenient alternative. This has motivate the esign of contactless solutions for vital sign monitoring where person s smartphone or other nearby communication infrastructure (e.g. WiFi) can be leverage for the purpose. In the RF-base vital sign monitoring solutions propose in [Liu et al. 15; Patwari et al. 14; Abelnasser et al. 15; Ravichanran et al. 15], WiFi signal reflecte from the human boy is use to estimate the breathing an heart rates. Although these papers have solve important challenges in esigning contactless vital sign monitoring, they have many practical limitations. Due to the omni-irectional propagation commonly use in.4/5 GHz WiFi, signal can be reflecte from multiple humans in an inoor space. This makes it ifficult to istill the vital signs of multiple humans from the reflecte signal. Due to this reason, the majority of the WiFi-base vital sign monitoring research assumes there is only a single human subject in the range of WiFi enpoints or if there are multiple humans in the range, their vital signs are sufficiently ifferent from each other. However, in most practical scenarios, there are more than one human in inoor spaces like homes, offices, hospitals an the vital signs of iniviuals can vary substantially in a short time. The WiFi signal can also reflect from many inoor objects, an complex signal processing is necessary to extract the human reflecte signal in multi-path rich inoor environment. This makes it ifficult to etermine the tiny motion of heartbeats from the reflecte signal an hence [Patwari et al. 14; Abelnasser et al. 15; Ravichanran et al. 15] are primarily limite to measuring only the breathing rate. This paper investigates the use of 6 GHz millimeter-wave (mmwave) signal for ubiquitous an non-invasive vital sign monitoring. The 6 GHz mmwave frequency

2 A: Z. Yang et al. ban provies over 7 GHz (57-64 GHz) of unlicense spectrum. With the evelopment of IEEE 8.11a [IEEE 1], the mmwave ban is shown to enable high-spee (up to 7 Gbps) inoor wireless local/personal area networks. Its suitability for applications such as point-to-point vieo streaming has resulte in rapi commercialization with the evelopment of WiFi+6 GHz wireless access points [Intel 14], smartphone chipsets etc. With this momentum, the 6 GHz mmwave is likely to be an omni-present technology of inoor WLANs/WPANs in homes an offices in the coming years. We emonstrate that the 6 GHz mmwave signal can provie highly accurate an reliable vital sign monitoring. Due to the high attenuation loss of 6 GHz signal, irectional beamforming is employe using phase array or horn antenna to concentrate the signal in one irection. We show that mmwave signals reflecte off a human boy can accurately represent minute chest motion necessary to estimate his/her breathing an heart rate. Due to the irectional nature, the signal is not affecte by any other motion outsie the transmitter (Tx) an receiver (Rx) beams. Even more importantly, the irectional beams reuce the signal footprint of monitoring each human, which in turn allows higher spatial reuse where multiple human subjects can be monitore in parallel within a room. In this paper, we present mmvital, a comprehensive vital sign an sleep monitoring system using mmwave. mmvital can measure a human s breathing rate an heart rate in ifferent positions (staning, sitting, sleeping etc.) without requiring any proactive actions from the human. It is robust to ifferent istances an incient angles of the impinging signal as humans can change their locations anywhere in the room. In aition to vital signs, mmvital can also monitor a person s quality of sleep with ientifying sleeping postures, because frequent changes in posture is foun to be correlate with the quality of sleep [Oksenberg an Silverberg 1998]. Another important factor to impact the sleep quality is the breathing isorer. For this purpose, mmvital can accurately etect apnea events for patients suffering from sleep isorers such as central apnea an hypopnea. mmvital solves multiple challenges towars builing a practical monitoring system. With mmwave, it is necessary to perform Tx an Rx beamforming towars the human in orer to reflect the signal off his/her boy. To aress this challenge, mmvital utilizes a human fining process where the inoor surrouning is profile an monitore in terms of mmwave reflections. The human fining proceure results in accurate beamforming angles to point an reflect the signal from human(s). We ientify the separation conitions that ictates how multiple humans can be monitore using mmwave an how mmvital can provie very high concurrency of monitoring compare to the omni-irectional WiFi-base solutions. This feature can facilitate parallel monitoring of multiple patients on nearby bes in a hospital room or ifferent family members in a home. CHANGED: This can be easily implemente on the future commercial 6 GHz evices, which are suppose to be multiple noes in a typical living environment ue to high power loss at 6 GHz. These evices can form reflective paths bounce from/to human boy to enable our propose work. The contributions of this paper can be summarize as follows: (1) We empirically emonstrate the feasibility of monitoring of breathing an heart rates using 6 GHz mmwave signals. We evelop a state-of-the-art testbe an esign mmvital, a vital sign monitoring system that utilizes RSS of the mmwave signal reflecte from a human to provie accurate vital sign monitoring while being robust to ifferent incient angles an istances. () We evelop a contactless sleep monitoring system that oes not require the user to wear any evices on his/her boy uring sleep. mmvital can monitor a person s sleep by ientifying sleeping postures an etecting the posture changes. It also observes human s breathing pattern uring the sleep for etecting sleep apnea events (specif-

3 Vital Sign an Sleep Monitoring Using Millimeter Wave A:3 ically central apnea an hypopnea). CHANGED: We note that research presente in this paper is an extension of our previously publishe work [Yang et al. 16a]. Compare to [Yang et al. 16a], the novel contribution of this work is the evelopment an evaluation of sleep monitoring techniques incluing posture classification an apnea etection. (3) A novel human fining technique is evelope that can be use by mmvital to locate a user base on the reflection loss of a human boy. We evelop a reflection loss base classification that can accurately an efficiently istinguish reflection from a human boy or other objects (e.g. walls, chairs etc.). (4) We systematically stuy an empirically erive the (angular an istance) separation necessary between humans for concurrently monitoring their vital signs. Aitionally, we establish the relationships between the necessary separation an the antenna beamwith, an outline possible cases such as blockage, multiple reflections etc. (5) We o an extensive evaluation of mmvital using our 6 GHz testbe with 7 participants in office an home environment. The mean estimation error across all participant is observe to be.43 breaths per minute (Bpm) an.15 beats per minute (bpm). The human fining proceure achieves object-human classification accuracy of 98.4% with 1 ms of well time on reflection changes. mmvital is also accurate in behinthe-wall breathing rate estimation with the mean estimation error of.58 Bpm. The rest of the paper is organize as follows. Section iscusses the relate work. Section 3 provies the overview of mmvital an Section 4 iscusses the breathing rate an heart rate estimation moules, Section 5 represents the posture event etection an classification, an apnea etection moules. Section 6 etails the human fining proceure an Section 7 iscusses the monitoring of multiple people. Section 8 provies the numerical results an we conclue in Section 9.. RELATED WORK RF signal base contact-free solutions for vital sign an sleep monitoring have receive increasing attention in recent years. Our work is relate to the prior works in the following three areas,.4/5 GHz ban an mmwave ban for sensing as well as the sleep apnea monitoring systems..4/5 GHz base vital sign monitoring: The authors in [Abelnasser et al. 15] propose to use WiFi RSS for respiratory monitoring. However, it requires the person to hol a evice or stan in the line-of-signt path between TX an RX noes for accurate monitoring. [Liu et al. 15] leverage fine-graine Channel State Information (CSI) of WiFi to track vital signs. The primary focus of the work is to measure the vital signs when a person is sleeping an the approach is applicable to monitoring a single person in a controlle environment. Also, the propose solution can only monitor multiple humans when their vital signs are sufficiently istinct. However, in practical scenarios, iniviual s vital signs can vary substantially in a short time perio. A similar work [Ravichanran et al. 15] stuie a natural setting in the home with both LOS an NLOS cases to estimate a single person s respiration rate using a customize WiFi harware. The work is limite to measuring breathing rate (not heart rate) an can only monitor multiple humans breathing rate when they are sufficiently ifferent. CHANGED: [Huang et al. 16] propose a self-calibrating vital sign monitoring system base on the Doppler raar, however the require emoulation proceure only focuse on the case of single subject monitoring. [Prakash et al. 16] evelope a GSM-base IoT network to monitor vital signs, in which computational an network processors are interface with sensing components an tranmit signals through GSM an SMTP server. Different from mmvital, the vital sign sensors in [Prakash et al. 16] are require to be attache on users. A wireless sensor system using

4 A:4 Z. Yang et al. evices was propose to monitor vital signs in [Patwari et al. 14] which requires the eployment of many sensor noes/links for the accurate monitoring of a single human. [Aib et al. 15] propose to use FMCW raar for breathing an heart rates estimation. The propose work can monitor multiple human subjects in parallel. However, it utilizes a customize eicate harware with a large banwith of 1.8 GHz (between 5-7 GHz). In contrast, mmvital can reuse the IEEE 8.11a commercial communication/networking harware for the purpose of vital sign monitoring. Although we use customize mmwave platform (ue to the unavailability of off-the-shelf harware), our techniques simply rely on RSS an can be easily implemente on low-cost future commercial 6 GHz WLAN evices. mmwave sensing an networking: CHANGED: While some research has foun that the exposure uner mmwave might moerately increase human skin s temprature [Ganhi an Riazi 1986; Wu et al. 15], mmwave is otherwise shown to have no significant etrimental effects on human boy [Price 1], given that the transmission power an antenna gain are carefully chosen as per the regulations [Rappaport et al. 14]. Previous works have also stuie vital signs etection using mmwave signal in ifferent frequencies, such as 8 GHz [Petkie et al. 9], 94 GHz [Mikhelson et al. 1; Bakhtiari et al. 1] an 6 GHz [Kao an Lin 13; Chuang et al. 1]. The primary focus of these papers have been to emonstrate the feasibility of vital sign monitoring with mmwave. They assume simple controlle settings with one human subject at a close istance from the transmitter an receiver. In aition, [Bakhtiari et al. 1] an [Chuang et al. 1] focuse on mmwave sensor an harware/chip esign for vital sign etection. In our work, our focus is on solving practical challenges such as fining the human s location in a room an realizing the true potential of irectional beams by sensing multiple humans concurrently. mmvital is esigne to be operational in realistic inoor environments like offices, homes etc. as shown in our evaluation. 6 GHz millimeter wave sensing has been stuie extensively in literature for other applications like target tracking, automotive raar etc. In a recent work, [Wei an Zhang 15] propose high-precision tracking of objects (writing with pen) using mmwave beam scanning. The reflection characteristics of ifferent objects for mmwave signals have been stuie in [Langen et al. 1994]. Authors in [Sur et al. 15] provie a link level measurement of blockage an reflection in an inoor environment, an our reflection measurements closely match their results ue to the similarity of 6 GHz transmitter an receiver system. 6 GHz communication has been stuie for outoor picocells [Zhu et al. 14] with its reflection an absorption characteristics, for wireless links in ata center networks [Zhou et al. 1] an for WLANs [Nitsche et al. 15] with beamforming assiste via out-of-ban.4/5 GHz WiFi. The reflection, blockage an beam-steering characteristics stuie in these works are in agreement with our work. Sleep monitoring systems: Polysomnography is a commonly use clinical test to iagnose the sleep apnea isorer [Spriggs 14], whereas it requires the patient to attach multiple sensors uring the sleep in hospital. CHANGED: To overcome this inconvenience issue, [Kayyali et al. 8] showe a home-base polysomnography system for monitoring sleep isorers, rather than the conventional polysomnography equipment where subjects are require to sleep in a hospital laboratory. [Masa et al. 11] propose a home respiratory polygraphy, a cost-effective home-base sleep monitoring system, for the iagnosis of sleep apnea-hypopnea synrome. Furthermore, some portable an/or mobile evice base sleep monitoring systems have also been evelope in recent times. [Mamelak an Hobson 1989] mounte transucers on a heaban to monitor sleep states by preicting non-rapi-eye-movement an rapieye-movement. [Rofouei et al. 11] evelope a wearable neck-cuff system for realtime monitoring of sleep isorers, which consists mainly of a neck-cuff an a cell-

5 Vital Sign an Sleep Monitoring Using Millimeter Wave A:5 Fig. 1: mmvital 6 GHz transmitter an receiver system phone/computer with Bluetooth wireless communication. [Fullpower 14] esigne a mobile application for sleep apnea, snore, heart rate, an weight loss monitoring. [Nguyen et al. 16] presente a wearable sleep staging monitoring system that uses a in-ear recorer to evaluate bioelectrical brain wave signals. However, all of approaches above still require either a variety of specialize sensors or mobile evices attache to the subject. [Mack et al. 9] evelope a ballistocariography-base monitoring system to monitor breathing, heartbeat, an musculoskeletal movements. [Shin et al. 1] use balancing tubes to evaluate the performance of an air-mattress sensor system on monitoring heart rate, breathing rate, snoring events, sleep apnea, an boy movement of subject. [Norman et al. 14] valiate a mattress-base monitoring system that etects sleep isorere breathing behaviors without sensors attache to the subject. Although subjects are not require to wear sensors in these methos, the mattresses are customize to embe various pressure sensors. [Nanakumar et al. 15] recently propose to use soun wave with Frequency Moulate Continuous Wave (FMCW) to measure breathing an etect apnea events, in which the smartphone running the application nees to be place close to the subject. CHANGED: Our non-intrusive approach base on mmwave wireless signals oes not require any other aitional sensors attache to subjects, or any moification of the sleeping furniture. It can be easily couple with future 6 GHz WLAN evices. 3. SYSTEM DESIGN We now escribe our 6 GHz communication platform, esign goals, challenges an provie an overview of mmvital.

6 A:6 Z. Yang et al mmwave Communication Platform mmvital is implemente using 6 GHz transmitter an receiver which use a mmwave evelopment platform provie by Vubiq [Pasternack 15]. The mmwave platform provies a 6 GHz RF front-en an a waveguie moule as shown in Fig. 1. CHANGED: On the transmitter sie, we use a signal generator (Keysight EXG N517B) that prouces a 1 MHz baseban sine wave signal that is input to the Vubiq transmitter moule. On the receiver sie, the 6 GHz receiver moule is connecte to a spectrum analyzer (Keysight EXA N91A) that allows us to analyze the receive baseban signal. We calculate the Receive Signal Strength (RSS) using the power spectral ensity istribution provie by the spectrum analyzer. The RSS values are available at an average of 6 samples per secon in experiment setup, which is sufficient for monitoring breathing an heart rates. Due to the unavailability of any reconfigurable phase array, we use a horn antenna with 3-B beamwith of 1 (estimate first null beamwith (FNBW) of 4 ) an 4 Bi gain on the transmitter an receiver. A mechanical rotator is use to form the beams in ifferent irections an scan the surrounings for reflections. 3.. Design Goals an Challenges To compensate for the high attenuation loss, 6 GHz raios use irectional antenna (e.g. horn antenna or phase array). The central objective of mmvital is to exploit the irectional nature of mmwave communication to accurately measure a human s breathing an heart rates. The mmwave Tx irects its signal to the human boy an the reflecte signal is receive by the Rx. In terms of esign, mmvital shoul be able to exploit the irectional nature of mmwave an measure the vital signs of multiple humans concurrently. mmvital shoul be non-invasive, which means that it shoul not require the human to perform any specific actions to monitor the vital signs. mmvital shoul also be ubiquitous where the monitoring can be performe anywhere within the reach of Tx an Rx. Aitionally, the monitoring shoul be accurate even when human is sleeping, staning or sitting, an shoul be robust to ifferent postures (front, back, right an left). Although the irectional nature of 6 GHz communication reuces the inaccuracies introuce by other motions an inoor multi-paths, it also raises multiple challenges: (1) Because a human can be at ifferent places compare to the Tx an Rx, it is necessary that the reflection-base monitoring is robust to ifferent incient angles of signal onto the human boy an ifferent postures (front, back, left, right) of the boy. We stuy the impact of incient angle an boy postures on vital sign monitoring, an also evelop a technique to ientify sleep postures an etect sleep apnea. () Before starting the vital signs monitoring, it is first require to fin the human in the vicinity of Tx an Rx. Since various inoor objects (e.g. wall, metal cabinet etc.) reflect the mmwave signal, mmvital nees to istinguish the reflections from objects an humans. Although this can be accomplishe by inspecting each reflection for heartbeats, the time overhea of such inspection with many possible reflections inoors can be very high. We evelop an algorithm for human fining, where the Tx an Rx engage in an iterative scanning to profile the inoor environment in terms of its current reflections, an inspect them to ientify the reflection from human boy. (3) With narrow beamwiths, it is possible to use multiple non-overlapping beams to monitor the vital signs of multiple humans in parallel. The relative position of multiple humans result in many complex reflection scenarios such as the blockage of one human by the other or multiple reflections. We systematically classify various scenarios an stuy the angular an istance separation necessary for concurrent sensing.

7 Vital Sign an Sleep Monitoring Using Millimeter Wave A:7 Tx Rx 1 Omni sweep process Human Fining Subsystem Reflection loss evaluation Human/ object classification 3 Vital Sign Monitoring Subsystem Tx an Rx Beamforming towars human(s) RSS samples of reflecte signal Data enoising an filtering Breathing rate estimation Posture classification Heart rate estimation Sleep monitoring Apnea etection Fig. : Overview of mmvital 3.3. System Overview Various components of mmvital are shown in a block iagram in Fig.. At a high level, mmvital contains two subsystems - (i) Human fining subsystem an (ii) Vital sign monitoring subsystem. The goal of the former subsystem is to fin the human in a room (or an inoor space) so that Tx can irect its signal towars the human an Rx can receive the reflection. For accomplishing this efficiently, mmvital utilizes an omni-sweep proceure that profiles the inoor environment in terms of its reflections an tracks any changes to it. When new reflections are etecte, the reflection loss is evaluate to classify if they are from movable objects (e.g. chairs, laptops etc.) or a human. mmvital leverages the iversity in material permittivity to accurately ientify human reflections. Once the Tx an Rx beamforming angles towars the human are etermine, the secon subsystem performs the vital sign monitoring. The signal reflecte by the human boy are analyze through RSS samples to estimate the vital signs. The RSS samples first unergo ata enoising proceure where a sliing winow-base moving average filter is applie to remove the high-frequency noise. Apart from that, we also apply a banpass frequency filter with cutoff of.1- Hz in orer to remove the impact of slow moving DC component as well as moerate to high frequency human movements [Munguia Tapia 8] (e.g. shaking of boy parts). The filtere RSS samples are then use by four separate moules. The breathing rate an heart rate moules further apply their custom filters an peak etection algorithms (iscusse in the next section) for estimation. The filtere samples are also utilize by the posture moule to ientify a user s current sleeping posture. The apnea etection moule monitors a human s breathing pattern to etect any central apnea or hypopnea events. CHANGED: We note that posture classification an apnea etection o not require estimating vital signs, an hence, can operate separately an in parallel. We first provie the etails of vital signs subsystem in the next section an efer to the iscussion on human fining subsystem to Section 6.

8 A:8 Z. Yang et al. Amplitue RSS (Bm) Amplitue -1-5 Raw Breath -5 1 Heart Beat Time (s) Fig. 3: Raw RSS of the reflecte signal an extracte breathing an heart beats 4. MEASURING VITAL SIGNS 4.1. Breathing an Heart Rates In orer to estimate the breathing rate, we transform the filtere RSS ata to the frequency omain. We observe that the RSS signal is very sensitive to the perioic movement of human breathing, which results in a peak (ominant frequency) in the frequency omain. The frequency of the peak represents the breathing rate at a coarsegrain. However, simply selecting the highest peak is not always accurate ue to variations introuce by noise an motion. To achieve a better accuracy, we select the highest magnitue peak as well as the frequency of the two ajacent bins, an create a custom narrow ban-pass filter. We apply the filter on the RSS ata an perform an Inverse FFT (IFFT) to yiel the filtere time-series ata. We then use a simple peak etection algorithm for precisely counting the breathing rate in Bpm. The normal heart rate of an ault is known to be in the range of 6-1 [Ganong an Barrett 5] bpm. However, uring high-intensity activities like exercising, the heart rate can excee 17 bpm [Ganong an Barrett 5]. For such activities an to etect any other abnormal conitions, we select the heart rate range to be 5- bpm. Similar to the breathing rate estimation, we apply an FFT on the RSS time-series ata an etermine the ominant frequency. In this case, we select the highest magnitue peak along with four ajacent bins of frequency to create the custom ban-pass filter because the heart beat motion is smaller than the breathing motion an can exhibit larger variations. We apply the filter, perform IFFT an use the peak etection for estimating heart rate. Fig. 3 shows an example of raw RSS samples along with filtere breathing an heart beat samples. We offset the RSS values by the transmission power to present the RSS an RSS loss on the same scale. After applying the customize filters, we apply peak etection algorithm for accurate counting. We note the mmvital estimates the vital

9 Vital Sign an Sleep Monitoring Using Millimeter Wave A:9 Tx Rx (a) Experiment setup of evaluating ifferent incient angles Power Re.ection Coe/cient Gabriel Ganhi Hwang Measure Incient Angle ( / ) (b) Impact of incient angle on reflecte power (theoretical an measure) Fig. 4: CHANGED: The impact of incient angle: more transmitte signal power is reflecte back to the receiver as the signal incient angle increases signs in real-time using a sliing winow of 3 secons offset by approximately 1 ms (every 6 RSS samples in our testbe). 4.. Impact of Incient Angle Because a human can be anywhere within the Tx an Rx vicinity while being monitore for his/her vital signs, the transmitte signal can impinge on the human boy at any angle (referre as incient angle). We now investigate the impact of incient angle on the reflecte signal, an the robustness of breathing rate an heart rate estimation. The amount of energy reflecte from an object can be quantifie using power reflection coefficient which can be erive from reflection coefficient. The reflection coefficient is the ratio of the complex amplitue of the reflecte electromagnetic wave to that of the incient wave. The coefficient epens on factors such as the permittivity (a complex value) of the object material an the signal incient angle. The reflection coefficient (r) can be calculate [Ahmai-Shokouh et al. 9] as r 1 e jω 1 r i e jω r i, for i {, } (1) where ω πl λ ɛ /ɛ 1 sin γ, l enotes the thickness of the reflecting source; λ enotes the signal wavelength; γ is the incient angle; ɛ 1 an ɛ are the permittivities of the first meium an the secon meium, respectively. In a simplifie single layer moel, the first meium can be assume as air which has the permittivity of 1. r an r are the Fresnel s reflection coefficients when the electric fiel is perpenicular an parallel to the incience plane, respectively. The coefficients can be calculate as r cos γ ɛ /ɛ 1 sin γ. () cos γ + ɛ /ɛ 1 sin γ

10 A:1 Z. Yang et al. RSS(Bm) 1 5 / / 1 5 / / Raw RSS Filtere RSS Time (s) Fig. 5: CHANGED: Raw an filtere RSS for 5, 5, 45 an 65 incient angles. The RSS samples are comparatively less noisy at higher incient angles ue to reuce reflection loss. r ɛ cos γ ɛ ɛ 1 ɛ 1 sin γ. (3) ɛ cos γ + ɛ ɛ 1 ɛ 1 sin γ The reflection coefficient (r) can be use to estimate the power loss ue to reflection (or power reflection coefficient) as L R P O PI r where P O an P I are the values of reflecte (after reflection) an incient (before reflection) power respectively. We empirically evaluate the impact of incient angle on the reflection power loss as shown in Fig. 4a. In the experiments, the Tx an Rx move symmetrically on a circle of 3 meter raius. The human sits at the center of the circle, an both Tx an Rx point their horn antennas to the human. Since the P I in unknown, we first use an aluminum plate at each incient angle in the place of the human an measure the receive power. As an aluminum plate is regare as a perfect reflector (reflection loss nearly B), an we use its receive power as a reference for human measurements. Fig. 4b shows the reflection loss (L R ) for incient angles from 5 to 75. It also compares the theoretical value of reflection loss calculate using Eq.1 an (perpenicular). For the calculations, three ifferent values of human boy/skin permittivity are consiere base on previous work from [Gabriel et al. 1996], [Ganhi an Riazi 1986], an [Hwang et al. 3]. These values are 7.89 j1.9, 8.89 j13.15 an 8.5 j4.13 at 6 GHz, respectively [Zhaobov et al. 11]. We observe that our measurements are in agreement with the permittivity moels of [Gabriel et al. 1996] an [Hwang et al. 3]. Higher

11 Vital Sign an Sleep Monitoring Using Millimeter Wave A:11 RSS (Bm) Variance Han Time (s) Fig. 6: CHANGED: An example of posture change event: posture change can be etecte by monitoring the RSS variance for a pre-efine amount of time variations observe in the measurements are ue to human s breathing motion. Examples of the reflecte RSS (raw an filtere) at ifferent incient angles are shown in Fig. 5. It can be seen that as the incient angle increases, the RSS samples become less an less noisy mostly ue to the ecrease in the reflection loss. The main observation in Fig. 5 is that the reflecte RSS is representative of the breathing motion at all incient angles. Hence, mmvital is robust to the human changing location relative to Tx an Rx. Also, if mmvital is use to monitor humans when they o not change their locations (e.g. sleeping in beroom or hospital be), it is avisable to eploy the Tx an the Rx at larger incient angles to increase the estimation accuracy. We will evaluate the vital sign estimation accuracy for multiple human subjects with ifferent incient angles in Section SLEEP MONITORING mmvital performs sleep monitoring through ientifying user s current posture an the etection of apnea events Posture Detection an Classification Detecting human s current sleep posture an posture change events is useful in etermining the quality of sleep as well as sleep relate isorers [Oksenberg an Silverberg 1998]. Sleep posture affects the breathing pattern an oxygen inhalation, an can aggravate existing sleep isorers such as apnea [Oksenberg an Silverberg 1998]. For example, a sleeping-on-stomach posture may cause some breathing obstruction that impacts the subject s breathing pattern an oxygen inhalation. In this work, we are primarily intereste in etecting human s sleeping posture an apnea events by observing the RSS of the reflecte signal in mmvital. mmvital utilizes the observe variance in the enoise RSS samples to etect posture change events. As shown in Fig. 6, a typical posture change event causes consierable variation in the RSS for a certain amount of time. We leverage this observation to

12 Magnitue A:1 Z. Yang et al. 1 Front Right Left.6 Back Frequency (Hz) Fig. 7: CHANGED: Frequency omain analysis of RSS can be use to classify ifferent postures Table I: CHANGED: Time winow features (time an frequency omains) use for posture classification Time omain Mean Maximum Variance Range First, secon, an thir quartiles Frequency omain Energy (Eq. 4) Entropy (Eq. 5) Dominant frequency ratio Mean Variance esign a threshol-base technique that etects if the RSS variance is higher than a certain value for a given amount of time to etermine a posture change event. For the posture classification, we are intereste in four sleep postures - sleeping on back (Front), sleeping on stomach (Back), facing left (Left) or facing right (Right). mmvital leverages the fact that when wireless signal strikes the human boy, the reflection is affecte by the boy posture. Fig. 7 shows the frequency omain of the filtere

13 Vital Sign an Sleep Monitoring Using Millimeter Wave A:13 Table II: CHANGED: Stanar eviation of filtere RSS at ifferent postures Postures Stanar eviation Front Right Left.598 Back RSS samples for a subject in four ifferent postures. It is observe that signal reflecte off the human s chest in facing right posture has much higher magnitue than facing left. However, the ifference in breathing motions in front an back postures cannot be observe through visual inspection. To classify among the four postures, we use a set of statistical features calculate over a moving time winow of RSS samples. Table. I lists the time an frequency omain features that we use in posture classification. The ominant frequency ratio [Munguia Tapia 8] is calculate as the ratio of the highest magnitue FFT coefficient to the sum of magnitue of all FFT coefficients. The energy [Munguia Tapia 8] is calculate as Energy winow length/ i1 m (4) where m is the magnitue of FFT coefficients. The entropy [Munguia Tapia 8] is calculate as winow length Entropy n i log (n i ) (5) where n i is the normalize value of FFT coefficients. Fig. 8 shows the effectiveness of the selecte features in posture classification. We observe that energy provies important inication about the posture. The energy reflects the position of human s heart compare to the Tx an Rx. When the human is facing right, his/her heart is closer to the signal s point of impact on human boy. CHANGED: The receive signal is thus more sensitive to the chest movement cause by breathing an heart beats. Table II shows the stanar eviation of four postures, in which the facing right posture has the largest value. In the frequency omain, facing right results in highest observe energy in the reflecte RSS. This is followe by front an back postures where reflection from front exhibits more energy than the back. When the human is facing left, least energy is observe ue to his/her heart being farthest from the reflection point among all four postures. We buil a ecision tree-base machine learning classifier with the features escribe above, an mmvital uses the classifier to ientify user s current posture. 5.. Apnea Detection Apnea isorer is wiely characterize as isruption in normal breathing pattern especially uring sleep. It was shown in [Nanakumar et al. 15] it affects over 18 i1

14 A:14 Z. Yang et al. Dominant Frequency Ratio Right Front Back Left Energy Fig. 8: Frequency omain analysis provies useful clues for posture classification. Energy an Dominant frequency raio from the frequency omain features are selecte as the example features Fig. 9: CHANGED: Examples of central apnea an hypopnea events as observe through RSS variations million American aults with a variety of resulting isorers. There are two major types of breathing pattern changes that are associate with ifferent types of apnea. In the first type, a human s oro-nasal airflow is obstructe an there is no breathing relate movements in the lungs an chest muscles. In central apnea, this stoppage of breathing occurs for more than 1 secons [Berry et al. 1]. In the secon type, the intensity of breathing motion an movement of chest muscles is substantially low. In hypopnea, such low intensity breathing happens for more than 1 secons [Berry et al. 1]. Although there are other types of apnea such as obstructive apnea, mixe apnea etc., the two types of changes in breathing pattern (stoppage or low intensity) are commonly associate with them. Hence, we primarily focus on central apnea an hypopnea. Our intuition behin this investigation is that uring both apnea events, the motion of chest muscles is significantly ifferent. If so, it is possible to etect the ap-

15 Vital Sign an Sleep Monitoring Using Millimeter Wave A:15 Tx T α Object Path loss Reflection loss R (a) An example reflection event β Rx RSS Loss (B) Monitor screen Pressure cooker Metal trash can Plastic trash can Empty 5-gal water bottle Office chair back Wooen boar Partition Human (b) RSS loss for reflection from ifferent objects an human. CHANGED: T an R are set to 4m, an.5m Fig. 1: Reflection loss for ifferent types of objects nea events using mmvital monitoring system. CHANGED: Since apnea is an abnormal breathing pattern, the frequency filter that extracts the breathing rate is not require. Instea, we can monitor the flatness or the shallow variations on the enoise RSS samples to etect the apnea events. Utilizing mmwave for monitoring apnea events has an ae avantage that its time-series RSS samples are alreay a goo representation of human chest motion. This means that with only enoising an filtering process (mentione in Section 3.3), the apnea event can be etecte in real-time. Examples of filtere RSS samples with two types of apnea events are shown in Fig. 9. We use NeuLog respiration monitor (chest strap) [NeuLog 15] to establish the groun truth of apnea events. Fig. 9 emonstrates that the processe mmwave RSS samples provie clear inications of apnea (similar to polysomnography tests). The central apnea is etecte when the istance between two peaks is (or absence of any peak for) more than 1 secons. The hypopnea events are etecte by measuring the amplitue of the reflecte RSS. If the amplitue rops below 3% of normal breaths for more than 1 secons [Berry et al. 1], a hypopnea event is etecte. 6. FINDING HUMAN FOR VITAL SIGN MONITORING A major challenge in the esign of mmvital is that it is require to etermine where the human is before it can start monitoring his/her vital signs. In home, office or a similar inoor place where human can freely move from one place to another, mmvital shoul be able to fin the human an point the transmitte signal towars his/her boy. In this section, we introuce a human fining proceure that can use to etermine precise Tx an Rx angle to transmit the signal to a human boy an receive the reflection respectively. The challenge of human fining is further complicate by the fact that 6 GHz signal is reflecte by many ifferent objects in an inoor environment. Such objects inclue walls, metal objects such as cupboars, monitors, microwave, trash-cans etc. In the presence of many possible reflections, it is ifficult for the Tx an Rx to know which reflection is inee coming from a human. mmvital utilizes a novel approach to istinguish human from objects base on reflective loss as it is known to be ifferent for ifferent objects [Langen et al. 1994]. The permittivity ictates the amount of signal that penetrates the object an reflects from

16 A:16 Z. Yang et al. 36 Tx Angle ( / ) RSS without Person (Bm) Rx Angle ( / ) 36-5 (a) Inoor reflection profile without person an laptop (b) Inoor reflection profile with person an laptop Fig. 11: CHANGED: Changes in the reflection profile can be monitore to etect new reflections an to etermine if the new reflection is from a human or an object it. Apart from penetration an reflection, the signal is also absorbe by the objects an scattere from its surface. However, absorption an scattering effect are ifficult to measure in our system. Instea, mmvital leverages the ifference in reflection loss ue to the ifferent permittivity to istinguish the objects from a human. In orer to measure the reflection loss, it is first necessary to remove the effect of istance epenent path loss. Fig. 1a shows an example reflection event from an object. If transmission power is P T, transmit antenna gain is G T, receive power is P R, an receiver antenna gain is G R, the total loss L (P T + G T + G R ) P R is as follows L L P ( T ) + L P ( R ) + L R (ɛ o ) (6) where L R (ɛ o ) is reflection loss from object with permittivity of ɛ o, T an R are the istances of the object from the Tx an Rx, respectively. The reflection loss can be calculate as shown in Section 4.. CHANGED: The path loss L P ( T ) an L P ( R ) at istance can be calculate using Friis moel of free-space attenuation as L P ( T ) log 1 ( 4π T λ ) (7) L P ( R ) log 1 ( 4π R λ ) (8) where λ is the signal wavelength. The Tx an Rx have the knowlege of P T an P R, an the istance between them (). As shown in Fig. 1a, T an R can be calculate using the angle of transmission (α) an reception (β) to erive L P ( T ) an L P ( R ). Using Eq. 6, 7 an 8, they can calculate the reflection loss L R (ɛ o ). The reflection loss can then be use to istinguish if the reflection is coming from a human or an object. To evaluate the feasibility of the object/human classification base on reflection loss, we test a variety of reflective objects an place them at one fixe location (fixe path loss) one by one. The observe RSS values for the objects an human are shown in Fig. 1b. CHANGED: Base on a 3-secon measurement for each target, we can observe that ifferent objects an human, epening on their material permittivity, reflects ifferent amount of signal. Hence, we use regression on the reflection loss to

17 Vital Sign an Sleep Monitoring Using Millimeter Wave A:17 Algorithm 1 Human Fining Proceure Input: Tx power (P T ), RSS variation tolerance threshol (κ), Beam sweep step size (s ), number of previous RSS samples (k), Human/object classifier (Ψ( )), istance between Tx an Rx () Output: Tx an Rx angles towars human Proceure: 1: for α : 36 o #Omni-sweep proceure : for β : 36 o 3: R(α,β) t RSS(α, β) 4: t (α,β) Rt 1 (α,β) Rt (α,β) 5: if t (α,β) > κ then #Change in reflection 6: Calculate T an R using α, β an 7: for i : t k t o #Analyze last k samples 8: L i (α,β) P T R(α,β) i L P ( T ) L P ( R ) 9: Γ (α,β) Γ (α,β) L i (α,β) 1: en for 11: if Ψ(Γ (α,β) ) Human then #Human-object 1: return α, β #classification 13: en if 14: en if 15: β β + s 16: en for 17: α α + s 18: en for ientify if it is from an object or a human. In our experiments, we observe that the human/object classification can be one using even a single RSS sample base on the reflection loss, making the human fining proceure very efficient. With the use of more RSS samples, the confience of classification can be further improve since the RSS for human reflecte signal varies more ue to heartbeat an breathing motion compare to the objects. Before the classification can be applie, mmvital is require to fin the reflection profile of the inoor environment. The reflection profile can be foun by a brute-force omni-irectional sweeping of Tx an Rx beams. The proceure is formally escribe in Algorithm 1. For each Rx angle (in steps from to 36 ), the Tx scans the entire 36 to etermine all reflections. Note that although the omni-sweep proceure is brute-force, it can be complete in a short time with igital beamforming where beam switching can be performe at much smaller time scales. Also, the proceure is only require to be performe when human s vital signs can no longer be monitore an human fining proceure has to be initiate. Also, both Tx an Rx can use iscrete steps for angle increment for generating non-overlappe beams (similar to sectors in 8.11a) to reuce the time complexity. To emonstrate the omni-sweep proceure, we use our testbe to buil the reflection profile of a room. Due to our horn antenna 3-B beamwith of 1, the Rx scans all irections in increments of 1. For each Rx angle, Tx scans the entire 36 with continuous rotation. The reflection profile in the absence of any human is shown in Fig. 11a. As we can see, a typical room has many ifferent reflections from wall an other objects. In mmvital, the profile can be built in the absence of a human an then only the changes in reflections nee to be monitore to fin human. Note that a change

18 A:18 Z. Yang et al. A B δ T δ R Tx Rx Tx Blockage Multiple reflections Multi-beam sensing Rx Tx (a) Three cases of monitoring multiple humans Rx Tx Rx Angular separation (b) Angular separation between Subject A an B Fig. 1: (a) Relative position of humans compare to Tx an Rx gives rise to three cases - blockage, multiple reflections an multi-beam sensing (b) Angular separation between two humans can be calculate as min(δ T, δ R ) in reflection can occur ue to the presence of a human (increase if new reflection an possible ecrease if existing reflection blocke) as well as movement of any existing object (e.g. moving a chair or laptop). Fig. 11b shows the reflection profile with a human an a laptop (with metal enclosure) in the room. Both laptop an human reflect the signal, which can be tagge as the change in the reflections. As in Algorithm 1, each change in reflection is inspecte using the reflection-loss base classifier to ientify if the change is ue to a human or an object. As we observe from Fig. 11b, in some cases, the existing reflections exhibit minor ifference in RSS at ifferent times. This can be ue to the changes in object temperature. In orer to ignore such minor variations, we utilize a RSS variation tolerance threshol (κ). Whenever a change in RSS is within the threshol, mmvital oes not consier the reflection for object-human classification. Lastly, for the change reflections, the Tx an Rx angles are use to etermine the path loss, an calculate the reflection loss as Eq. 6. This reflection loss is input to the object-human classifier, an if the reflection loss is classifie as human, the Tx an Rx angles of the reflections are use to start monitoring the human for his/her vital signs. 7. MONITORING MULTIPLE HUMANS Due to the irectional nature of 6 GHz communication, it can monitor multiple humans in a room concurrently. Monitoring multiple humans is useful in many practical scenarios. For example, more than one family members in a house or multiple patients in hospital room can be monitore in parallel using the same mmwave Tx-Rx pair. In this section, we first systematically categorize various scenarios of monitoring multiple people with mmwave an then stuy how much separation between humans is necessary to sense their vitals signs in parallel Single an Multi-Beam Sensing The cases of monitoring multiple humans can be classifie as follows (refer to Fig. 1a) - (1) Single-Beam Sensing: In single-beam sensing, two (or more) humans are monitore with the use of only one pair of fixe Tx an Rx beams without any switching of beam irection. These cases can be further classifie in two classes - a) Blockage where one human blocks the other human completely, allowing only one human to be monitore at a time.

19 Vital Sign an Sleep Monitoring Using Millimeter Wave A:19 T1 Human A Human B Loc. δ min R4 ϕ R3 θ R R1 B1 L1 L L3 L4 L1, R1 5 o L, R 8 o L3, R3 1 o L4, R4 13 o T1, B1 13 o Tx Rx Fig. 13: Experiment setup use for evaluating the impact of angular separation b) Multiple reflections where mmwave signal is reflecte partially from both the humans. With multiple reflections, it is possible to sense the vital signs both the humans if they have istinct heart an breathing rates. Most of the current.4/5 GHz RF-base vital sign monitoring research [Liu et al. 15; Ravichanran et al. 15; Abelnasser et al. 15] assume such istinct vital signs of users for all users within the omni-irectional range of the Tx an Rx. However, in reality, multiple users can have similar vital signs an even the vital signs vary substantially over time. With mmvital, such restrictions of istinct vital signs are limite only to a small region within the beams. () Multi-Beam Sensing: When there is a secon human outsie the Tx an Rx beams of the first human being monitore, both of them can be monitore by switching the beams between the two at a fast rate (using igital beamforming in nanosecons [Vales-Garcia et al. 1]). We refer to such cases as multibeam monitoring (Fig. 1a), where multiple non-interfering beams are use by the Tx an Rx to monitor people outsie the beams of each other. 7.. Human Separation We now stuy the separation necessary between two humans for all cases of single an multi-beam sensing. In orer to characterize the spatial reuse (from sensing perspective) in the area of interest, we ientify two types of separation - angular an istance. As shown in Fig. 1b, let δ T enote the angle between the straight lines connecting Tx to human A an human B. Similarly, let δ R enote the angle between the straight lines connecting Rx to human A an human B. The angular separation (δ min ) is efine as min(δ T, δ R ). The istance separation (s) is simply calculate as the Eucliean istance between human A an human B Angular Separation. We now empirically erive a relationship between the minimum require angular separation an the antenna beamwith. In our experiment

20 A: Z. Yang et al. Normalize FFT Magnitue R1 L1.5 1 Frequency (Hz) Normalize FFT Magnitue 1 R4 L T1 B Frequency (Hz) (a) FFT of RSS samples for subject A when subject B is at locations R1 or L1 (blockage) (b) FFT of RSS samples for subject A when subject B is at locations R4, L4, T1 or B1 (multibeam sensing) Normalize FFT Magnitue Human A Human B R3 R L L Frequency (Hz) (c) FFT of RSS samples for subject A when subject B is at locations R3, R, L, or L3 (multiple reflections) Fig. 14: FFT of RSS samples for subject A when subject B stans at ifferent locations (Fig. 13) to create blockage, multiple reflections an multi-beam sensing scenarios setup (Fig. 13), one human (A) stans in the center with Tx an Rx pointing their beam to him/her. Another human (B) changes his/her position from L 1 to L 4 an R 1 to R 4 to vary the angular separation from 5 to 13. Recall that in our testbe, the FNBW φ 4 an 3-B beamwith θ 1. We intentionally ask humans A an B to breathe at ifferent rates (human A at 8-11 Breath per minute (Bpm) an human B at 1-7 Bpm) in orer to make their breathing rates istinguishable in the frequency omain. We repeat the setup an experiments in three ifferent rooms for verification.

21 Vital Sign an Sleep Monitoring Using Millimeter Wave A:1 ϕ θ θ ϕ Blockage Multiple reflections Multi-beam sensing Fig. 15: Schematic showing physical regions of blockage, multiple reflections an multi-beam sensing Figs. 14 show normalize FFT of the receive RSS for the 1 cases. We choose locations L 1 an R 1 to emulate blockage scenarios where either signal is blocke by human A before it reaches human B (L 1 ) or signal reflection from human B (R 1 ) is blocke by human A before it reaches the receiver. We observe from Fig. 14a that for Locations L 1 an R 1 of human B, only human A s breathing can be etecte from the reflecte RSS. For locations L, L 3, R an R 3, the transmitte signal is reflecte from both humans A an B (multiple reflections). This can be seen as FFT peaks aroun Hz (human B) an Hz (human A) in Fig. 14c. It shows that vital signs of both humans can be calculate by applying appropriate frequency ban-pass filters. At locations L 4, R 4, T 1, an B 1, no breathing of human B is etecte in Fig. 14b, inicating that human B is outsie the Tx an Rx beams being use for human A. For these cases, both A an B can be monitore by switching the beams between them in time. The following relationship between the angular separation an the beamwith is observe from the above experiments. It is also schematically shown in Fig. 15. (1) Blockage: when the angular separation between two humans is less than half the 3B beamwith of antenna δ min θ, one human is likely to block the signal from impinging the other human. () Multiple reflections: when the angular separation between two humans is θ δ min φ, both humans can be monitore in parallel as long as their breathing/heart rates are ifferent. (3) Multi-beam sensing: when δ min > φ, both humans can be monitore by switching the beams between the two at a fast rate Distance Separation. The angular separation is essential to unerstan the conitions that ictate single an multi-beam sensing cases. However, it oes not provie sufficient insight in how much physical istance is require between two humans for parallel sensing. In this section, we stuy istance separation an erive its relationship with beamwith an relative positioning of Tx, Rx an human subjects. Fig. 16 shows an example setup where the Tx an Rx pair is monitoring a human at location O. Let α OAB an β OBA inicate the Tx an Rx beam irections respectively. We assume that Tx is locate at Point A( /, ) an Rx is locate at Point

22 A: Z. Yang et al. E A(-/,) C θ α O F D θ β B(/,) Fig. 16: Four nearest locations where another human can be present without interfering with the target human at Point O 1 >3 Y(m) Tx Rx 5 [-.5, ] [.5, ] X(m) Fig. 17: Best case istance separation in a 1m 1m room B(/, ) where is the istance between Tx an Rx. We can observe from Fig. 13 that another human can be at four nearest locations (points C(x C, y C ), D(x D, y D ), E(x E, y E ) an F (x F, y F ) in Fig. 16) for multi-beam sensing. Here, we primarily focus on the multi-beam sensing cases as they are more practical compare to multiple reflection cases which occur in relatively smaller area an also require the users vital signs to be ifferent. We efine the istance separation (s) to be the minimum of the four istances OC, OD, OE an OF. The istance s inicates the best-case of minimum separation necessary between the two humans. It is epenent on α, β an φ (FNBW), an can occur in any irection from the human at location O. The four istances OC, OD, OE an OF can be calculate by eriving the coorinates of the points in the -D Eucliean space. Note that we assume θ φ because the 3 B beamwith can be approximate as half of FNBW in most mmwave horn antennas. Using these, X an Y coorinates of Point O (when θ < α, θ < β) can be calculate

23 Vital Sign an Sleep Monitoring Using Millimeter Wave A:3 Tx Rx Metal trash can Water machine Rx 3m Microwave Tx Plastic trash can as (a) Office room (b) Apartment beroom Fig. 18: Experiment setup in office an home x O cos α sin β + sin(α + β) ; y sin α sin β O sin(α + β). (9) The complete erivation of the coorinates of points an four istances can be foun in CHANGED: [Yang et al. 16b]. We use them to calculate the best-case istance separation (s ). In the experiment setup of Fig. 13, we also evaluate two aitional locations (T 1 an B 1 ) for human B. As we observe from Fig. 14b, we observe that presence of human B is not etecte in the FFT which means that it has a sufficient istance separation from human A to allow multi-beam sensing. We also evaluate the best-case istance separation for all points in a room of 1m 1m an the results are shown in Fig. 17. We fin that for majority of locations within the room, the value of s is relatively small (meian 1.67 meters) which inicates that mmvital in our testbe setup can monitor multiple humans even when they are relatively close to each other. 8. EVALUATION We perform an extensive evaluation of mmvital in two ifferent inoor scenarios as shown in Fig. 18. The first room is a laboratory room (size: 6m 9m) in a university builing with objects such as cubicle partitions, white boars, metal cupboar, computers etc. The secon room is an apartment beroom of size 4.5m 6m with a be in the center an other furniture on the sies. Fig. 18 shows the positions of Tx an Rx. CHANGED: The transmission power is set to Bm an the horn antenna is 4 Bi, both of which are uner the regulation at 6 GHz frequency [Rappaport et al. 14]. Power ensity is a commonly use metric to evaluate the amount of power expose to a human boy. The power ensity (P D ) can be calculate as P D P T G T 4 π (1) T where P T an G T enote transmission power an the gain of antenna, respectively; T enotes the istance between the object an the transmitter in meters. This inicates that even though the human is very close to the transmitter (at.1m), the

24 Amplitue RSS (Bm) Amplitue A:4 Z. Yang et al Raw RSS Filtere RSS Groun Truth Time(s) Fig. 19: CHANGED: An example of raw RSS, filtere RSS, an groun truth on monitoring breathing rate observe power ensity is 191 mw/m, which is still significantly lower than the maximum allowable value of W/m at 3 GHz 3 GHz as per the ANSI/IEEE C stanar [Wu et al. 15]. We enroll 7 participants over a perio of two weeks to monitor their vital signs. Out of the 7 subjects, 6 subjects have a Boy Mass Inex (BMI) of aroun 19-5 kg/m an one subject (Subject #6) has the BMI of aroun 9 kg/m. The BMI is correlate with breathing rate an other cariovascular parameters [Sorlien 15]. CHANGED: The table III shows the etaile information of subjects. The groun truth is establishe using a finger pulse oximeter [Gurin 15] (for heart rate) an Neulog chest-strap respiration monitor [NeuLog 15]. CHANGED: Note that the finger pulse oximeter is only able to provie a numeric value of heart beats. On the other han, the wearable breathing rate monitor recors the complete breathing signal as shown in Fig. 19. Base on the groun truth, the mean estimation error is efine as Mean estimation error Σm i1 groun truth (Bpm/bpm) estimate vital sign (Bpm/bpm), m (11) where m enotes the uration of measurement in minutes Accuracy of Vital Sign Monitoring Breathing an Heart Rate Estimation Accuracy. For evaluating the breathing an heart rates of participants, we use three ifferent incient angles in the university room. Figs. a an b show the mean estimation error with (95% confience internals) for breathing rate (Bpm) an heart rate (bpm) for the 7 participants at 3 ifferent incient angles (7, 5, 3 ). The accuracy is calculate for 3 experiment runs of 1 minutes for each of the participants. For the incient angle of 7, the mean estimation error in breathing rate an heart rate estimation is less than.5 Bpm an.5 bpm respectively for all 7 participants. This shows that 6 GHz vital sign monitoring can provie highly reliable estimate of breathing an heart rates. The estimation error increases with the ecrease in the incient angles, which proves the relationship

25 Vital Sign an Sleep Monitoring Using Millimeter Wave A:5 Table III: CHANGED: Gener, height, weight, an BMI information of 7 subjects Subject Inex Gener Height (cm) Weight (kg) BMI # 1 M # M # 3 F # 4 M # 5 F # 6 M # 7 M Mean Estimation Error (Bpm) Breathing Rate #1 # #3 #4 #5 #6 #7 Subject (a) Mean estimation error of breathing rate of 7 participants at three ifferent incient angles Mean Estimation Error (bpm) Heart Rate #1 # #3 #4 #5 #6 #7 Subject (b) Mean estimation error of heart rate of 7 participants at three ifferent incient angles Fig. : Accuracy of breathing rate an heart rate between the reflection loss an the incient angle iscusse in Section 4.. At higher incient angles, the reflection loss ecreases as well as the reflecte RSS is observe to be less noisy. Both these factors increase the vital sign estimation accuracy. We observe that the breathing an heart rate estimation errors are slightly higher for Subject #6 which is likely ue to higher BMI. Since these experiments are performe with the participants either staning or sitting, the estimation error is likely to be even lower when they are sleeping on be as breathing rate is substantially more stable when a human is sleeping.

26 A:6 Z. Yang et al. RSS Loss (B) Distance(m) 1 (a) Impact of istance variation on RSS loss an breathing rate estimation accuracy Accuracy (%) Accuracy (%) Breath Heart Front Back Left Right Facing Direction (b) Accuracy of breathing rate an heart rate uner four typical human postures Fig. 1: Impact of istance apart, an robustness to ifferent postures Robustness to Distance an Posture. We also evaluate the impact of human s istance from Tx an Rx on the observe RSS an the accuracy of vital sign monitoring. We fix the incient angle to be 45 an the location of the human subject, while moving the Tx an Rx away from the human in steps of 1 meter. We evaluate the RSS an breathing rate estimation accuracy while varying the Tx-human (an Rx-human) istance from 1 meter to 1 meters. Fig. 1a shows RSS loss an breathing rate estimation accuracy for the varying istance. Note that the istance in Fig. 1a inicates the Tx to human (or human to Rx) istance, so the total signal propagation istance (Tx to human an human to Rx) is actually ouble. As expecte, the RSS loss increases an estimation accuracy ecreases with increase in istance. The confience intervals on RSS loss inicates that sufficient variations in the signal is observe even at a larger istances (upto Tx-human istance of 8 meters). Recall that this signal variation is useful in fining the human an istinguishing its reflection from other objects (Section 6). For istances lower than 8 meters, the mean breathing rate estimation error is less than.4 Bpm (mean accuracy is 98.8%), an beyon 8 meters the mean estimation error rops close to 1.7 Bpm, (mean accuracy is 97%). This shows that the vital sign estimation of mmvital is robust to istances common in rooms of typical inoor spaces like offices an homes. Since the signal can impinge anywhere on the human boy epening on the human s orientation relative to the Tx an Rx, we evaluate the impact of a human s facing irection (or posture) on breathing an heart rate estimation accuracy. In this experiment, 5 participants are aske to CHANGED: lay own an preten to sleep on the be in the apartment beroom (Fig. 18b) for 3 minutes (repeate 1 times) in four ifferent postures - sleeping on back (Front), sleeping on stomach (Back), facing left (Left) or facing right (Right). CHANGED: The antenna height of Tx an Rx is set as 1.m. The results of breathing rate an heart rate estimation accuracy are shown in Fig. 1b. We observe that the highest breathing rate estimation accuracy is observe for the front posture in which the signal irectly strikes an reflects from a human s chest area which exhibits the maximum breathing motion. However, for the other three postures also breathing rate estimation accuracy remains close to 98%. In terms of heart rate estimation, front posture also provies highest accuracy, followe

27 Vital Sign an Sleep Monitoring Using Millimeter Wave A:7 # of Subjects Posture Classi-cation Apnea Central Apnea Hypopnea Accuracy of Classi-cation % Fig. : Accuracy of posture classification for 5 subjects. # of subjects refers to how many subjects are involve in the training an testing Accuracy of Detection % Fig. 3: Accuracy of central apnea an hypopnea etection by the back posture. In both front an back postures, reflecte signal better captures the heart beat motion compare to left an right postures. When the human is facing right, his/her heart is towars the incoming signal from the Tx compare to when she is facing left, resulting in a better heart rate estimation accuracy for the right posture. In all cases, we observe that mmvital achieves high vital sign monitoring accuracy even when human is in ifferent postures Behin a Wall Estimation. The breathing rate estimation accuracy is evaluate for behin-the-wall case shown in Fig. 18a. Here, a human stans on the other sie of the wall from the Tx-Rx pair. Because penetration loss an reflection loss change epening on the incient angle, two incient angles (1, ) are evaluate. The mean estimation error of breathing rate is observe to be.58 Bpm an.93 Bpm for 1 an, respectively. In contrast to line-of-sight cases, increase in incient angle increases the estimation error in behin-the-wall cases. This is because at higher incient angles, more signal is reflecte an lesser signal penetrates through the wall to strike a human boy. The RSS reflecte also unergoes the same phenomenon an the receive RSS carries a weaker signature of breathing motion. It is worth noting that if the application oes not require behin-the-wall monitoring, the transmission power can be reuce or larger incient angles can be use (more reflection, less penetration) to contain the 6 GHz signal within the room. 8.. Posture Classification an Apnea Detection We evaluate our posture etection an classification an apnea etection schemes with 5 participants sleeping in an apartment beroom scenario (Fig. 18b). For the posture etection, the etection scheme in Section 5.1 is applie on the time uration in which each participant is aske to make 1 ranom posture changes. The variance-base posture change etection scheme achieves a very high accuracy of 99.6%. For the posture classification, we use the machine learning classifier escribe in Section 5.1 using the ata from varying number of participants. For each participant, we evaluate 4 instances of four postures (total 16 for one participant). The results of posture classification accuracy are provie in Fig.. 1-fol cross-valiation is use to evaluate the machine learning classifier. The results show that when traine an teste for the same participant (number of subject is 1), the average posture clas-

28 A:8 Z. Yang et al. Fig. 4: Accuracy of object/human classification with varying well time (number of RSS samples). CHANGED: TP, FN, FP, an TN enote true positive, false negative, false positive, an true negative, respectively sification accuracy is higher than 98%. For a classifier built using the ata of all 5 participants (number of subject is 5), the posture classification accuracy is higher than 9%. The reuction in classification accuracy with more number of subjects can be attribute to how ifferent people assume the postures an BMI variations. It is also observe that the majority (over 89% of misclassifie instances) of misclassification occurs between back an facing left postures. Fig. 3 shows the apnea event etection accuracy for central apnea an hypopnea. For the apnea experiments, participants emulate the apnea events while lying on the be in ifferent postures. We evaluate 16 central apnea an 16 hypopnea events using the technique escribe in Section 5.. The etection accuracy of central apnea an hypopnea is observe to be 93.7% an 87.5% respectively. The lower etection accuracy of hypopnea is mostly ue to the fact that uring the emulate events, ifferent participants rop the breathing intensity at ifferent levels (not necessarily 3%), making it ifficult to set the etection threshol, which in turn results in the unsuccessful etection of some events Reflection Loss base Human Fining The human fining proceure escribe in Algorithm 1 is evaluate in the laboratory room scenario. We create ifferent scenarios where 8 objects (laptop, metal kitchen utensil, plastic trash can, metal trash can, empty 5 gal. water bottle, chair, wooen boar, partition boar) an a human subject are ranomly relocate insie the room. Similarly, Tx an Rx are also move to ranomly chosen points in the room. The movement of objects, human an Tx-Rx ensures that a wie variety of istances an incient angles are evaluate for moving as well as non-moving objects (walls, tables etc.). For each of the scenarios, we fin the reflection profile to etermine the reflection from move objects an the human. The reflection loss base classification is then applie to the RSS values of change reflections as escribe in Section 6. The results of the classification are presente in Fig. 4. We vary the time interval for which RSS samples are collecte (well time) at each angle before performing the classification. It can be observe that as the well time increases, the accuracy of human-object clas-

29 Vital Sign an Sleep Monitoring Using Millimeter Wave A:9 Blockage of human B Multiple reflections Multi-beam sensing.3 1 A.6.9 >1. Tx Y(m) -.5 X(m).5 Blockage of human A Mean estimation error (Bpm) Rx (a) Heat map showing the mean estimation error of human A s breathing rate when human B is staning at ifferent square blocks (b) Experiment setup with ifferent separations between two humans Fig. 5: Accuracy of human fining an multiple people sification increases. A reflection is from a human or an object can be etermine with average accuracy of 96.% only with one RSS sample (available after 16 ms). With 1 ms of well time, the accuracy increases to 98.4% with false positive rate of < %. This means that the human fining proceure is highly robust to environment changes an can accurately etermine the Tx an Rx angles for monitoring Monitoring Vital Signs of Multiple People To evaluate the accuracy of vital sign estimation for multiple people using mmvital, we carefully esign an experiment as shown in Figs. 5a an 5b. In this setup, we choose a rectangular area of 6m.5m in which human subject A stans at the center of the rectangle. The Tx an Rx point their antenna beams towars human A. We then ask human B to stan at ifferent.5m.5m square blocks within the rectangle. In orer to create an etect all of scenarios we propose in Section 7, we ask both the subjects to intentionally breathe at ifferent rates. For each position of human B, we estimate the breathing rate of human A. Fig. 5a shows a heat map where the color of each square block inicates human A s mean estimate breathing rate error, when human B is staning in the square. Note that human A s location remains unchange uring the experiment. In Fig. 1a, the signal transmitte towars or reflecte from human A is affecte by the presence of human B at ifferent positions, resulting in (1) blockage, () multiple reflections an (3) multi-beam sensing cases as iscusse in Section 7. For the case that human B is between Tx an human A (the black blocks), the transmitte signal oes not reach human A, resulting in blockage of human A. When human B is close to human A, transmitte signal is reflecte from both the humans (multiple reflections). However, ue to the presence of human B, the mean estimation error of breathing rate of human A rops close to.9 Bpm (four ajacent positions of human A). For many locations when human B is sufficiently far from human A, it oes not interfere with the vital sign monitoring (multi-beam sensing). This means that human B is outsie the current Tx an Rx beam of human A (angular separation higher than half of the FNBW), an multiple separate Tx-Rx beam pairs can be use to monitor both humans. Due to the unavailability of igital beamforming phase array antenna for 6 GHz, we leave the evaluation of multiple humans through fast switching Tx-Rx beams to future work.