Patent Publication Number: US-2022218224-A1

Title: Sleep tracking and vital sign monitoring using low power radio waves

Description:
BACKGROUND 
     Vital signs, such as heartrate and breathing rate, can be useful for monitoring a person&#39;s health. Additionally, the amount a person moves while sleeping can be indicative of how soundly he is sleeping and of other health characteristics. Typically, to capture vital signs, perform sleep tracking, or both a sensor needs to be placed directly on the skin of the user being monitored. Such an arrangement, especially while sleeping, may not be desirable. Wearing such a sensor can be uncomfortable or easily forgotten by the user before going to bed. Further, such a contact sensor can become displaced during sleeping and may stop accurately sensing the user&#39;s vital signs and/or sleep patterns, resulting in a loss of valuable data. 
     SUMMARY 
     Various embodiments are described related to a smart-home device that performs radar-based measurement of vital signs. In some embodiments, a smart-home device that performs radar-based measurement of vital signs is described. The device may include a housing. The device may include an RF emitter located within the housing. The device may include an RF receiver located within the housing. The device may include a radar processing circuit located within the housing that may process data received from the RF receiver and may output raw waveform data to a processing system. The device may include a processing system, comprising one or more processors, that may be in communication with the radar processing circuit. The processing system may be configured to receive the raw waveform data from the radar processing circuit. The processing system may be configured to filter, from the waveform data, waveform data indicative of static objects to obtain motion-indicative waveform data. The processing system may be configured to analyze the motion-indicative waveform data to determine one or more frequencies of movement present within the motion-indicative waveform data. The processing system may be configured to perform spectral analysis on the motion-indicative waveform data to determine a spectral-analysis state of a monitored region. The processing system may be configured to determine that the spectral-analysis state of the monitored region matches a predefined spectral-analysis state during which vital sign monitoring may be permitted. The processing system may be configured to, in response to determining that the spectral-analysis state of the monitored region matches the spectral-analysis state during which vital sign monitoring may be permitted, determine one or more vital signs of a monitored user present within the monitored region based on analyzing the motion-indicative waveform data. The processing system may be configured to output the one or more determined vital signs of the monitored user. 
     Embodiments of such a method may include one or more of the following features: The RF emitter may emit frequency-modulated continuous-wave (FMCW) radar. The processing system may be further configured to filter the one or more frequencies to sum harmonics of the one or more frequencies caused by the one or more vital signs. The processing system being configured to determine that the spectral-analysis state of the monitored region matches the spectral-analysis state during which vital sign monitoring may be permitted comprises the processing system being further configured to use a neural network to analyze the spectral-analysis state of the monitored region. The smart-home device may be a smartphone. The device may further include a smartphone charger stand. The smartphone may be docked with the smartphone charger stand such that a radar sensor of the smartphone may be pointed to emit radio waves at a location where the monitored user will be sleeping. The smartphone may be placed under a sleeping location such that a radar sensor of the smartphone may be pointed to emit radio waves at the sleeping location where the monitored user will be sleeping. The one or more vital signs may include a breathing rate. The one or more vital signs may include a heartrate. 
     In some embodiments, a method for radar-based measurement of vital signs is described. The method may include receiving, by a smart-home device, waveform data based on received radio waves. The method may include filtering, from the waveform data, waveform data indicative of static objects to obtain motion-indicative waveform data. The method may include analyzing the motion-indicative waveform data to determine one or more frequencies of movement present within the motion-indicative waveform data. The method may include performing spectral analysis on the motion-indicative waveform data to determine a spectral-analysis state of a monitored region. The method may include determining that the spectral-analysis state of the monitored region matches a predefined spectral-analysis state during which vital sign monitoring may be permitted. The method may include determining one or more vital signs of a monitored user present within the monitored region based on analyzing the motion-indicative waveform data. The method may include, in response to determining that the spectral-analysis state of the monitored region matches the spectral-analysis state during which vital sign monitoring is permitted, recording the one or more determined vital signs of the monitored user. 
     Embodiments of such a method may include one or more of the following features: The emitted radio waves may be emitted as part of frequency-modulated continuous-wave (FMCW) radar. The method may include filtering the one or more frequencies to sum harmonics of the one or more frequencies caused by the one or more vital signs to one or more fundamental frequencies of the one or more vital signs. Determining that the spectral-analysis state of the monitored region matches the spectral-analysis state during which vital sign monitoring may be permitted comprises using a neural network to analyze the spectral-analysis state of the monitored region. The method may further include emitting, by the smart-home device, radio waves at a monitored region. The smart-home device may be a smartphone. The method may include docking the smartphone with a charger stand such that a radar sensor of the smartphone may be pointed to emit radio waves at a location where the monitored user will be sleeping. The method may include placing the smartphone under a sleeping location such that a radar sensor of the smartphone may be pointed to emit radio waves at the sleeping location where the monitored user will be sleeping. The one or more vital signs may include a breathing rate. The one or more vital signs may include a heartrate. 
     In some embodiments, a non-transitory processor-readable medium comprising processor-readable instructions is described. The processor-readable instructions may be configured to cause the one or more processors to receive raw waveform data. The one or more processors may filter, from the waveform data, waveform data indicative of static objects to obtain motion-indicative waveform data. The one or more processors may analyze the motion-indicative waveform data to determine one or more frequencies of movement present within the motion-indicative waveform data. The one or more processors may perform spectral analysis on the motion-indicative waveform data to determine a spectral-analysis state of a monitored region. The one or more processors may determine that the spectral-analysis state of the monitored region matches a predefined spectral-analysis state during which vital sign monitoring may be permitted. The one or more processors may determine one or more vital signs of a monitored user present within the monitored region based on analyzing the motion-indicative waveform data. In response to determining that the spectral-analysis state of the monitored region matches the spectral-analysis state during which vital sign monitoring is permitted, the one or more processors may output the one or more determined vital signs of the monitored user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A further understanding of the nature and advantages of various embodiments may be realized by reference to the following figures. In the appended figures, similar components or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label. 
         FIG. 1A  illustrate an embodiment of a bedside smart home device using low power radio waves to perform sleep tracking and vital sign monitoring. 
         FIG. 1B  illustrate an embodiment of an under-bed smart home device using low power radio waves to perform sleep tracking and vital sign monitoring. 
         FIG. 2  illustrates an embodiment of a sleep tracking and vital sign monitoring system. 
         FIG. 3  illustrates an embodiment of waveform data in which movement due to vital signs of a user are observable. 
         FIG. 4  illustrates an embodiment of two frequency-domain graphs indicative of energy level at different frequencies. 
         FIG. 5  illustrates an embodiment of two graphs indicative of the impulse nature of a human vital sign, such as heartrate. 
         FIG. 6  illustrates an embodiment of a method for using low power radio waves to perform sleep tracking and vital sign monitoring. 
         FIG. 7  illustrates an embodiment of a state diagram for determining when tracking of vital signs is permitted. 
     
    
    
     DETAILED DESCRIPTION 
     A smart home device, such as a smartphone, tablet computer, home assistant device, may have integrated low-power radar circuitry or be connected with an external radar device. Such low power radar circuitry may have many uses. Possible uses can include measuring vital signs of a user, tracking sleep patterns of the user, or both. Traditionally, to measure vital signs of a user, a sensor needs to be in contact with the user&#39;s skin, such as a chest-band or wrist-band. However, by using radar, it can be possible to identify large movements of the user that are indicative of sleep patterns and, in some circumstances, small movements of the user that are attributed to vital signs, such as the user&#39;s breathing and the user&#39;s heartrate. 
     Such a radar-based arrangement that does not require any sensor to be in physical contact with the user being monitored may be especially beneficial since a smart home device may be temporarily placed (e.g., in the case of a smartphone), semi-permanently installed (e.g., in the case of a home assistant device), or permanently installed bedside. For example, when a user goes to bed, she may already be in the habit of leaving the smartphone charging on a stand or connected with a wired charger wire nearby. Thus with little to no adjustment of the user&#39;s routine, the user may be able to reliably gather vital signs and sleep tracking data while the user is sleeping. 
     A smart home device may emit radar, such as frequency-modulated continuous-wave (FMCW) radar. Such radio waves may be transmitted at or around 60 GHz. Reflected radar pulses may be used to determine both distance and phase shifts. By comparing raw waveform data from multiple reflected radar pulses, reflected waveform data corresponding to static objects may be identified. Waveform data corresponding to static objects may be filtered out such that motion-indicative waveform data is obtained. The motion indicative waveform data may then be analyzed to identify one or more frequencies corresponding to vital signs. The motion indicative waveform data may be further processed using machine-learning, such as a neural network, to classify whether the user was generally still or moving when the motion indicative waveform data was captured. This data may be used to perform sleep tracking and determine whether vital signs, such as heartrate and breathing rate, were gathered while the monitored user was generally still. 
       FIG. 1A  illustrate an embodiment  100 A of a bedside smart home device using low power radio waves to perform sleep tracking and vital sign monitoring. In embodiment  100 A, user  101  is being monitored while sleeping. User  101  has placed his smartphone  104 , which is functioning as a smart home device, on wireless charging cradle  105  on nightstand  109 . Smartphone  104  may be generally placed on a horizontal plane with the sleeping user and has its radar emitter and receiver pointed towards user  101 . Radio waves may be emitted and sensed in field  106 . User  101  is the closest person to smartphone  104 . Static objects that are not expected to move may be in the region monitored by smartphone  104 . For example, static objects in the monitored region can include headboard  102 , mattress  103 , and/or nightstand  109  (or some other form of table or flat surface that wireless charging cradle  105  can be placed on. These objects may reflect a significant amount of emitted radio waves, but, as detailed herein, movement data due to vitals of user  101  can successfully be filtered out. 
       FIG. 1B  illustrate an embodiment  100 B of an under-bed smart home device using low power radio waves to perform sleep tracking and vital sign monitoring. Such an arrangement may be beneficial if user  101  does not have access to a stand that can effectively point smartphone  104  at user  101  from nightstand  109 . In embodiment  100 B, user  101  is being monitored while sleeping. User  101  has placed his smartphone  104 , which is functioning as a smart home device, under his bed. Smartphone  104  may be generally placed such that its RF emitter and RF receiver are pointed towards user  101  from under mattress  103 . (If a box spring is located between smartphone  104  and user  101 , while metal may tend to interfere with the emitted radio waves, the metal of the box spring may typically be sparse enough to allow monitoring of user  101 .) Radio waves may be emitted and sensed in field  107 . The frequency of wavelength emitted may penetrate through the bedframe, mattress, and bedding such that vitals and movement of user  101  can be monitored. User  101  is the closest person to smartphone  104 . Static objects that are not expected to move may be in the region monitored by smartphone  104 . Similar to embodiment  100 A, static objects in the monitored region can include headboard  102  and mattress  103 . In other embodiments, smartphone  104  may be placed between pillow  108  and mattress  103 . 
       FIG. 2  illustrates an embodiment of a sleep tracking and vital sign monitoring system  200 . System  200  may include smart home device  201 , network  240 , and cloud server system  250 . Smart home device  201  can represent various computerized devices that may be found within a home, such as in a region where a user may sleep. For example, smart home device  201  may be a cellular phone or smartphone. Smart home device  201  may be a home assistant device, such as a Google® Home or Amazon® Alexa. Such a home assistant device may be interacted with via voice commands spoken by a user. The home assistant device may then respond to the user&#39;s command via synthesized speech, for example: “Hi Computer, please enable sleep and vital tracking.” Other possible forms of smart home device  201  can include: smart earbuds, a smart thermostat; a smart smoke and/or carbon monoxide detector; a smart doorbell; a smart watch; a robotic device; or a smart security camera. 
     Smart home device  201  may include network interface  203 , radar subsystem  205 , processing module  210 , sleep data datastore  220 ; and vital statistic datastore  230 . Radar subsystem  205  may include RF emitter  206 , RF receiver  207 , and radar processing circuit  208 . RF emitter  206  can emit radio waves, such as in the form of continuous-wave (CW) radar. RF emitter  206  may use frequency-modulated continuous-wave (FMCW) radar. The FMCW radar may operate in a continuous sparse-sampling mode over a relatively long period of time. RF emitter  206  may transmit at or about 60 GHz. The power level used for transmission may be very low such that radar subsystem only has an effective range of several meters or even a shorter distance. RF receiver  207  may receive radio wave reflections off of nearby objects of radio waves emitted by RF emitter  206 . The reflected radio waves may be interpreted by radar processing circuit  208 . Radar processing circuit  208  may output raw waveform data, which can also be referred to as the raw “waterfall” data for analysis by a separate processing entity. Radar subsystem  205  may be incorporated together as part of a single integrated circuit (IC) or radar processing circuit  208  may be a single IC that is in communication with RF emitter  206  and RF receiver  207 . In some embodiments, radar subsystem is integrated as part of smart home device  201 . For instance, a smartphone may have a RF emitter  206  and RF receiver  207  arranged such that radio waves are emitted in the same direction in which a screen of the smartphone is facing. In other embodiments, an external device that includes radar subsystem  205  may be connected with smart home device  201  via wired or wireless communication. For example, radar subsystem  205  may be an add-on device to a home assistant or may be incorporated as part of a charging stand for a smartphone. 
     For radar subsystem  205 , if FMCW is used, an unambiguous FMCW range may need to be defined. Within this range, objects can be accurately determined to be nearer or farther away. However, outside of this range, if an object is detected, it could incorrectly interpreted as nearer than an object within the unambiguous range. When using smart home device  201  to monitor sleep patterns and vital statistics, a user may be instructed that the user should be the closest person to the smart home device  201 . However, it may be possible that another person or an animal is present within the bed. It may be necessary to the define the FMCW unambiguous range to be far enough, such as two meters, such that both persons (or, approximately the width of the bed) falls within the unambiguous FMCW range of radar subsystem  205 . Two meters may an ideal distance since it is approximately the width of a large commercially-available bed (e.g., a king size bed). 
     Raw waveform data may be passed from radar subsystem  205  to processing module  210 . Processing module  210  may include one or more processors. Processing module  210  may include one or more special-purpose or general-purpose processors. Special-purpose processors may include processors that are specifically designed to perform the functions detailed herein. Such special-purpose processors may be ASICs or FPGAs which are general-purpose components that are physically and electrically configured to perform the functions detailed herein. General-purpose processors may execute special-purpose software that is stored using one or more non-transitory processor-readable mediums, such as random access memory (RAM), flash memory, a hard disk drive (HDD), or a solid state drive (SSD). Processing module  210  may include: movement filter  211 ; frequency emphasizer  212 ; range-vitals transform engine  213 ; range gating filter  214 ; spectral summation engine  215 ; and neural network  216 . Each of the components of processing module  210  may be implemented using software or as specialized hardware. 
     The raw waveform data output by radar subsystem  205  may be received by processing module  210  and first processed using movement filter  211 . In some embodiments, it is important that movement filter  211  is initially used to perform filtering. That is, the processing performed by processing module  210  may not be commutative. Typically, vital sign determination and sleep monitoring may occur when a monitored user is sleeping or attempting to sleep in a bed. In such an environment, there may typically be little movement. Such movement may be attributed to the user moving within the bed (e.g., rolling over while trying to get to sleep or while asleep) and the user&#39;s vital signs, including movement due to breathing and movement due to the monitored user&#39;s heartbeat. In such an environment, a large portion of emitted radio waves from RF emitter  206  may be reflected by static objects in the vicinity of the monitored user, such as a mattress, box spring, bed frame, walls, furniture, bedding, etc. Therefore, a large portion of the raw waveform data received from radar subsystem  205  may be unrelated to user movements and the user&#39;s vital measurements. 
     Movement filter  211  may include a waveform buffer that buffers receive “chirps” or slices of received raw waveform data. For instance, sampling may occur at a rate of 10 Hz. In other embodiments sampling may be slower or faster. Movement filter  211  may buffer twenty seconds of received raw waveform chirps. In other embodiments, a shorter or longer duration of buffered raw waveform data is stored. This buffered raw waveform data can be filtered to remove raw waveform data indicative of stationary objects. That is, for objects that are moving, such as a monitored user&#39;s chest, the user&#39;s heartbeat and breathing rate will affect the distance and velocity measurements made by radar subsystem  205  and output to movement filter  211 . This movement of the user will result in “jitter” in the received raw waveform data over the buffered time period. More specifically, jitter refers to the phase shifts caused by moving objects reflecting emitted radio waves. Rather than using the reflected FMCW radio waves to determine a velocity of the moving objects, the phase shift induced by the motion in the reflected radio waves is used to measure vital statistics, including heartrate and breathing rate, as detailed herein. 
     For stationary objects, such as furniture, a zero phase shift (i.e., no jitter) will be present in the raw waveform data over the buffered time period. Movement filter  211  can subtract out such raw waveform data corresponding to stationary objects such that motion-indicative raw waveform data is passed to frequency emphasizer  212  for further analysis. Raw waveform data corresponding to stationary objects may be discarded or otherwise ignored for the remainder of processing. by processing module  210 . 
     In some embodiments, an infinite impulse response (IIR) filter is incorporated as part of movement filter  211 . Specifically, a single-pole IIR filter may be implemented to filter out raw waveform data that is not indicative of movement. Therefore, the single-pole IIR filter may be implemented as a high-pass, low-block filter that prevents raw waveform data indicative of movement below a particular frequency from passing through to frequency emphasizer  212 . The cut-off frequency may be set based on known limits to human vital signs. For example, a breathing rate may be expected to be between 10 and 60 breaths per minute. Movement data indicative of a low frequency than 10 breaths per minute may be excluded by the filter. In some embodiments, a band-pass filter may be implemented to exclude raw waveform data indicative of movement at high frequencies that are impossible or improbable for human vital signs. For instance, a heartrate, which can be expected to be above a breathing rate, may be unlikely to be above 150 beats per minute. Raw waveform data indicative of a higher frequency may be filtered out by the band pass filter. 
     In some embodiments, it may be possible to further fine-tune the frequencies of raw waveform data that movement filter  211  passes to frequency emphasizer  212 . For instance during an initial configuration phase, a user may provide information about the monitored user (e.g., himself, a child), such as age data. Table 1 indicates typical respiratory rates for various ages. Similar data may be present for heartrate. The filter may be configured to exclude data that is outside of the expected breathing rate, heartrate range, or both. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                   
                 Breathing Rate Range 
               
               
                   
                 Age 
                 (breaths per minute) 
               
               
                   
                   
               
             
            
               
                   
                 Birth-6 weeks 
                 30-60 
               
            
           
           
               
               
               
            
               
                 6 
                 months 
                 25-40 
               
               
                 3 
                 years 
                 20-30 
               
               
                 6 
                 years 
                 18-25 
               
               
                 10 
                 years 
                 17-23 
               
            
           
           
               
               
               
            
               
                   
                 Adults 
                 12-18 
               
            
           
           
               
               
               
            
               
                 65-80 
                 years old 
                 12-28 
               
               
                 &gt;80 
                 years old 
                 10-30 
               
               
                   
               
            
           
         
       
     
     The vital signs of the monitored user being measured are periodic impulse event: a user&#39;s heartrate may vary overtime, but it can be expected that the user&#39;s heart will continue to beat periodically. This beating is not a sinusoidal function, but rather can be understood as an impulse event, more analogous to a square wave having a low duty cycle that induces motion in the user&#39;s body. Similarly, a user&#39;s breathing rate may vary over time, but breathing is a periodic function performed by the user&#39;s body that is analogous to sinusoidal function, except that a user&#39;s exhale is typically longer than an his inhale. Further, at any given time, only a particular window of waveform data is being analyzed. Since only a particular window of waveform data is being analyzed, even a perfect sinusoid within that window can result in spectral leakage in the frequency domain. Frequency components due to this spectral leakage should be deemphasized. 
     Frequency emphasizer  212  may work in conjunction with range-vitals transform engine  213  to determine the one (e.g., breathing) or two (e.g., breathing plus heartbeat) frequency components of the raw waveform data. Frequency emphasizer  212  may use windowing, such as a 2D Hamming window (other forms of windowing is possible, such as a Hann window), to emphasize important frequency components of the raw waveform data and to deemphasize or remove waveform data that is attributable to spectral leakage outside of the defined window. Such windowing may decrease the magnitude of raw waveform data that is likely due to processing artifacts. The use of windowing can help reduce the effects of data-dependent processing artifacts while preserving data relevant for being able to separately determine heartrate and breathing rate. 
     Since heartrate and breathing rate are periodic impulse events, in the frequency domain heartrate and breathing rate may be represented by different fundamental frequency, but each may have many harmonic components at higher frequencies. One of the primary purposes of frequency emphasizer  212  is to prevent the frequency ripples of harmonics of the monitored user&#39;s breathing rate from affecting the frequency measurement of the monitored user&#39;s heartrate (or the reverse). While frequency emphasizer  212  may use a 2D Hamming window, it should be understood that other windowing functions or isolating functions can be used to help isolate frequency ripples of the monitored user&#39;s breathing rate from the frequency ripples of the monitored user&#39;s heartrate. 
     Range-vitals transform engine  213  analyzes the received motion-filtered waveform data to identify and quantify the magnitude of movement at specific frequencies. The analysis of range-vitals transform engine  213  may assume that the frequency components of the motion waveform data is sinusoidal. Further, the transform used at by range-vitals transform engine  213  can also identify the distance at which the frequency is observed. Frequency, magnitude, and distance can all be determined at least in part because radar subsystem uses FMCW radar system. 
     Prior to applying the transform of range-vitals transform engine  213 , a zero-padding process may be performed by range-vitals transform engine  213  to add a number of zeros to the motion-filtered raw waveform data. By performing a zero-padding process, the resolution within the frequency domain can be increased effectively allowing for more accurate low-rate measurements (e.g., a low heartrate, a low breathing rate). For example, zero-padding can help artificially increase resolution to detect differences of half a breath per minute compared to a resolution of a breath per minute without zero-padding. In some embodiments, three to four times the number of zeros compared to the buffered sample size of the raw waveform data may be added. For example, if twenty seconds of buffered raw waveform data is analyzed, sixty to eighty seconds of zero padding may be added to the sample. Specifically, the range of three to four times zero padding of the sample was found to substantially increase resolution while not making the transform process overly complex (and, thus, processor use intensive). 
     In order to determine the amount of zero padding to be performed, equations 1-3 may be used. In equation 1, RPM resolution may ideally be less than 1. 
     
       
         
           
             
               
                 
                   
                     RPM 
                     resolution 
                   
                   = 
                   
                     6 
                     ⁢ 
                     0 
                     * 
                     
                       chirp_rate 
                       
                         
                           n 
                           ⁢ 
                           _ 
                           ⁢ 
                           fft 
                         
                         ⁢ 
                         
                           _ 
                           ⁢ 
                           slow 
                         
                         ⁢ 
                         _time 
                       
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                      
                   1 
                 
               
             
           
         
       
       
         
           
             
               
                 
                   
                     
                       n 
                       ⁢ 
                       _ 
                       ⁢ 
                       FFT 
                     
                     ⁢ 
                     
                       _ 
                       ⁢ 
                       slow 
                     
                     ⁢ 
                     _time 
                     ⁢ 
                     _min 
                   
                   = 
                   
                     
                       ( 
                       
                         
                           nearest_ 
                           ⁢ 
                           power 
                         
                         ⁢ 
                         _of 
                         ⁢ 
                         _ 
                         ⁢ 
                         2 
                       
                       ) 
                     
                     ⁢ 
                     
                       ( 
                       
                         60 
                         * 
                         chirp_rate 
                       
                       ) 
                     
                   
                 
               
               
                 
                   Eq 
                   . 
                      
                   2 
                 
               
             
           
         
       
     
     In some embodiments, a chirp rate (chirp rate) of 30 Hz may be used. Such a frequency may have sufficient margin from Nyquist limits of the upper limit of breathing rate and heartbeat rate. n_FFT_slow_time_min may, therefore, be 2048. Given a 20 second window for estimating respiration statistics, Equation 3 results in a value of 600. 
     
       
         
           
             
               
                 
                   
                     
                       n 
                       ⁢ 
                       _chirps 
                     
                     ⁢ 
                     _for 
                     ⁢ 
                     _respiration 
                   
                   = 
                   
                     
                       20 
                       * 
                       chirp_rate 
                     
                     = 
                     600 
                   
                 
               
               
                 
                   Eq 
                   . 
                      
                   3 
                 
               
             
           
         
       
     
     This value of 600 is smaller than the required vitals-FFT size and makes us to perform a 3× to 4× zero padding. A balance in how much zero padding to perform may be based on increasing the frequency resolution and associated increases in the amount of computation needed to perform the FFT. A 3× to 4× zero padding has been found to provide sufficient resolution for heartrate and breath rate while moderating the amount of computation needing to be performed. 
     Range-vitals transform engine  213  can perform a Fourier transform (FT) to determine the frequency components of the received raw waveform data. Specifically, a fast Fourier transform (FFT) may be performed by range-vital transform engine  213  to determine the specific frequencies and magnitudes of waveform data at such frequencies. Following range-vitals transform engine  213  identifying the frequency, magnitude, and distance determination, further analysis may be needed to further reduce the effects of heartrate, breathing rate, or both not being sinusoidal signals. 
     Range-vitals transform engine  213  may perform a transform similar to a range-doppler transform with several possible differences. Since phase shifts are used rather than velocity, the data input to the transform has different properties. The FMCW operates in a continuous space-sampling mode over long periods of time (rather than a burst fast-sampling mode over a short period of time).Additionally, as previously detailed, zero padding is performed to allow for sufficient resolution for accurate determination of heartrate and breathing rate. 
     Range gating filter  214  is used to monitor only a defined range of interest and exclude waveform data due to movement beyond the defined range of interest. For arrangements detailed herein, the defined range of interest may be 0 to 1 meter. In some embodiments, this defined range of interest may be different or possibly set by a user or service provider. A goal of this arrangement may be to monitor only the one person closest to the device (and exclude any other person farther away, such as a person sleeping next to the person being monitored). Range and range gating filter  214  serves to remove movement data attributed to objects outside of the defined range of interest and sum the energy of movement data attributed to objects within the defined range of interest. 
     Spectral summation engine  215  may receive the output from range gating filter  214 . Spectral summation engine  215  may function to transfer the measured energy of harmonic frequencies of the heartrate and breathing rate and sum the harmonic frequency energy onto the fundamental frequency&#39;s energy. This function can be referred to as a harmonic sum spectrum (HSS). Heartrate and breathing rate are not sinusoidal, therefore, in the frequency domain, harmonics will be present at frequencies higher than the fundamental frequency of the user&#39;s breathing rate and the fundamental frequency of the user&#39;s heartrate. One of the primary purposes of spectral summation engine  215  is to prevent harmonics of the monitored user&#39;s breathing rate from affecting the frequency measurement of the monitored user&#39;s heartrate (or the reverse). The HSS may be performed at the second order by summing the original spectrum with a down-sampled instance (by a factor of two) of the spectrum. This process may be repeated such that higher order harmonics are added to the fundamental frequency. 
     At this stage, for a person in bed who is lying still (with the exception of movement due to breathing and heartrate), it will be expected that two major frequency peaks will present in the frequency data. However, if the monitored user is physically moving, such as rolling over in bed, the energy will be significant distributed across the frequency spectrum (a broadband distribution). Such large physical movement may manifest itself in the frequency data as being a large number of small peaks. If the bed is empty, rather than a person being present, there may be no or almost no frequency components above the noise floor since movement filter  211  has previously filtered raw waveform data corresponding to static objects. 
     Spectral summation engine  215  may output an indication of heartrate (e.g., in beats per minute) and breathing rate (e.g., in breaths per minute). Neural network  216  may be used to determine whether the heartrate and/or breathing rate output by spectral summation engine  215  should be considered valid. Neural network  216  may be trained (e.g., using supervised learning performed using a training set of data) to distinguish at least three states, such as those indicated in Table 2 by performing a spectral analysis. Each state in Table 2 is associated with a different spectral energy and sparsity profile. The neural network may be trained to distinguish between each state based on the spectral energy profile output by spectral summation engine  215 . 
     
       
         
           
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 State of Monitored Region 
                 Spectral Energy 
                 Spectral Sparsity 
               
               
                   
               
             
            
               
                 User present and vitals-only 
                 High 
                 Low 
               
               
                 movement 
               
               
                 User present and moving (limb 
                 High 
                 High 
               
               
                 and torso movements) 
               
               
                 No user present 
                 Low 
                 Low 
               
               
                   
               
            
           
         
       
     
     The state of the monitored region determined by neural network  216  may be used in determining the monitored user&#39;s sleep state. The state of the monitored region determined by neural network  216  may further be used to determine if the vital statistics output by spectral summation engine  215  should be trusted or ignored. For accurate vital statistic determination, heartrate and breathing rate may only be identified as likely accurate when neural network  216  determines that the user is present and still (i.e., no large physical movements, however movement is occurring due to breathing and/or heartbeat). The vital statistics output by spectral summation engine  215  may only be stored locally or may be transmitted to cloud server system  250  for remote storage. 
     Neural network  216  may be initially trained using a large set of training data that has been properly classified as mapping the spectral frequency profile of the specific output of spectral summation engine  215  to the ground-truth user state. The neural network may be a fully connected neural network that is not time-dependent. In some embodiments, a machine-learning arrangement, classifier, or other form of artificial intelligence other than a neural network may be used. 
     The sleep state determined by neural network  216  may be stored, along with time data, to sleep data datastore  220 . The vital statistics output by spectral summation engine  215  may only be stored to vital statistic datastore  230  when neural network  216  indicates that the monitored user is present and still. Other vital statistic data may be discarded or possibly flagged to indicate it is less likely to be correct. The data stored to sleep data datastore  220  and vital statistic datastore  230  may be stored only locally at smart home device  201 . Such an implementation may help alleviate a concern about health-related data being transmitted and stored remotely. In some embodiments, the monitored user may elect to have sleep data and vital statistic data transmitted via network interface  203 , stored, and analyzed externally, such as by cloud server system  250 . Storage by cloud server system  250  may have significant benefits, such as the ability for the user to access such data remotely, allow access to a medical provider, or participate in research studies. The user may retain the ability to delete or otherwise remove the data from cloud server system  250  at any time. 
     In some embodiments, processing module  210  may be wholly or partly located remotely from smart home device  201 . While radar subsystem  205  may need to be local to the monitored user, the processing of processing module  210  may be moved to cloud server system  250 . In other embodiments, another smart home device that is in local communication (e.g., via a LAN or WLAN) with smart home device  201  may perform some or all of the processing of processing module  210 . In some embodiments, a local communication protocol, such as involving a mesh network, can be used to transmit the raw waveform data to the local device that will be performing the processing. Such communication protocols can include Wi-Fi, Bluetooth, Thread, or communication protocols of the IEEE 802.11 and 802.15.4 families. Similar to the processing, storage of the sleep data and vital statistic data may occur at cloud server system  250  or another smart home device location to smart home device  201 . In still other embodiments, processing module  210  may be incorporated with radar subsystem  205  as a single component or system of components. 
     The stored sleep data of sleep data datastore  220  and the vital statistic data of vital statistic datastore  230  may be used to provide the user with short-term and long-term visual and trends relating to his sleeping patterns, vital statistics, or both. For instance, each morning, representation engine  217  may output or make available graphs, statistics, and trends. For example, a graph that is indicative of sleep data from the previous night and, possibly one or more graphs indicative of breathing rates and heartrate during the previous night. Similar graphs, trends, and statistics may be output for significantly longer periods of time, such as weeks, months, years, and even multi-year stretches of time. Other uses for sleep data and vital statistics may be possible. For instance, if certain triggers regarding heartrate, breathing rate, and/or sleeping patterns are triggered a medical professional may be notified. Additionally or alternatively, a notification may be output to the user indicating that the collected data is potentially concerning or is indicative of a healthy person. In some instances, specific sleep problems may be identified, such as sleep apnea. 
       FIG. 3  illustrates an embodiment  300  of raw waveform data in which movement due to vital signs of a user are observable. Embodiment  300  is representative of smart home device  201  being aimed at a monitored sleeping user from a distance of less than one meter who is generally still. In embodiment  300 , slight “waves” are visible in the raw waveform data. These waves are caused by the user&#39;s breathing pattern at approximately 13.5 breaths per minute. Along the x-axis, each chirp index corresponds to a received chirp of radio waves prior to any processing by movement filter  211 . Therefore, for each chirp-index along the x-axis, sixty-four samples were taken as indicated by the sample index on the y-axis. For each of these samples, a value is measured to indicate RF intensity. In embodiment  300 , a large portion of the raw waveform data is due to static objects. Due to such a large portion of the data being indicative of radio waves reflected by static objects, this raw waveform data is first filtered out by movement filter  211 . 
       FIG. 4  illustrates an embodiment  400  of two frequency-domain graphs indicative of energy level. In graph  401 , without any motion-based filtering having been performed before analyzing the raw waveform data, it can be seen that by far the most energy is present at and around a frequency of zero. This high level of energy at low frequencies may be caused by static objects in combination with zero padding. However, in graph  402 , motion-based filtering has been performed by movement filter  211 , such that energy related to static objects has been removed. 
     This removal allow for a clear peak to be seen around 13.5 respirations per minute (rpm). It should be understood that if movement due to the user&#39;s heartbeat is being measured, the x-axis of graph  402  could be extended (and reflect frequency, rather than rpm) such that a second significant peak at the user&#39;s heartrate would be visible. 
       FIG. 5  illustrates an embodiment  500  of two graphs indicative of the impulse nature of a vital sign, such heartrate. A heart may typically be beating for ⅛ of the time it is resting. In graph  501 , periodic impulses are illustrated over time, which can be indicative of chest displacement due to breathing or chest displacement due to a heartbeat. When converted to the frequency domain in graph  502 , a fundamental frequency and many higher harmonic frequencies are present due to the measured data of graph  501  not being a sinusoid. These upper harmonic values may be identified and summed with the fundamental frequency by spectral summation engine  215 . 
     Various methods may be performed using system  200 .  FIG. 6  illustrates an embodiment of a method  600  for using low power radio waves to perform sleep tracking and vital sign monitoring. Method  600  may be performed using system  200  or some other form of system that can transmit, receive, and analyze radar, such as an FMCW radar. At block  605 , radio waves may be emitted. The radio waves emitted may be continuous-wave radar, such as FMCW. The FMCW radar may operate in a continuous sparse-sampling mode over a relatively long period of time. The radio waves emitted may be emitted by RF emitter  206  of radar subsystem  205 . At block  610 , reflections of the radio waves may be received, such as by RF receiver  207  of radar subsystem  205 . The reflections received at block  610  may be reflected off of moving objects (e.g., a person having a heartbeat and breathing) and stationary objects. A phase shift may be present in the radio waves reflected by a moving object. 
     At block  615 , raw waveform data, which can also be referred to as waterfall data, may be created based on received reflected radio waves. The reflected radio waves may be indicative of distance and a phase shift. At a given frequency, such as 10 Hz, a number of samples may be taken, such as 64 samples. For each of these samples, intensity and phase shift may be measured. Over time, a window of raw waveform data may be created and stored in a buffer for analysis. Referring to  FIG. 2 , block  615  may be performed by radar processing circuit  208  or by processing module  210 . 
     At block  620 , samples of the waveform data that have been buffered may be compared. Waveform data that is indicative of static objects (that is, zero phase shift), which can be defined as objects having movement below a particular frequency (or at least a threshold phase shift), may be filtered out and discarded such that only waterfall data that is indicative of movement above a particular frequency is saved for further analysis. The particular frequency may be defined based on characteristics of the person to be monitored, such as age, as detailed in Table 1. Block  620  may be performed before block  625  to remove a large portion of the waveform data attributed to static objects and more readily make data attributed to small movements of a user attributed to vital signs be detectable. 
     At block  625 , the motion-indicative waveform data may be analyzed. This analysis may be performed to identify and separate data attributable to heartrate and breathing rate. Block  625  may be broken down into multiple blocks; in some embodiments, only a subset of these blocks may be performed and/or the order of such blocks may be varied. 
     At block  626 , a window of raw waveform data is to be analyzed through block  625 , the Fourier transform of the windowed raw waveform data develops non-zero values at frequencies other than the fundamental frequency due to spectral leakage. To deemphasize artifacts in the frequency domain due to spectral leakage, a particular type of window may be applied, such as a Hamming window. Such a window may help emphasize the fundamental frequencies due to vital signs within the window but deemphasize artifacts due to spectral leakage. While a Hamming window has been found to be effective, other forms of windowing may be used, such as a Hann window, a Parzen window, or a triangular window. 
     At block  627 , a range-vitals transform may be performed. As part of the range-vitals transform, zero-padding may be performed. This zero-padding may help increase the frequency resolution to accurately identify the frequency of vital signs, such as breathing and heartrate. Further, as part of range-vitals transform, a Fourier transform may be performed to convert waveform data into the frequency domain. 
     At block  628 , range-gated filtering may be performed. Block  628  may be performed by Block  628  may be performed by range gating filter  214  of processing module  210  or some other processing system. to exclude spectral energy from the waveform data attributed to objects outside of a defined distance range. Block  628  may be performed in order to ensure that only a user closest to the radar emitter/receiver (e.g., radar subsystem  205 ) is monitored to the exclusion of other persons or animals in the vicinity (e.g., sleeping next to the user being monitored). 
     At block  629 , a harmonic summation may be performed. Block  629  may be performed by spectral summation engine  215  of processing module  210  or by some other processing system. The harmonic summation may function to transfer the measured energy of harmonic frequencies of the heartrate and breathing rate and sum the harmonic frequency energy onto the fundamental frequency&#39;s energy. Heartrate and breathing rate are not sinusoidal, therefore, in the frequency domain, harmonics will be present at frequencies higher than the fundamental frequency of the user&#39;s breathing rate and the fundamental frequency of the user&#39;s heartrate. 
     At block  630 , a determination may be made whether the vital sign monitoring is currently permitted. Vital sign monitoring may only be judged to be accurate when the monitored user is in bed and is not moving (other than the small movements due to vital signs). A state analysis may be performed as detailed in relation to  FIG. 7  to determine if the monitored user is in bed, not moving, and in a position to have vital signs monitored. Block  630  may be performed using a trained neural network that can distinguish between various user states. Only when a user is lying still in bed may block  630  proceed to block  635 . Otherwise, block  630  may proceed to block  640 . 
     At block  635 , vital sign data calculated via block  625  may be stored and/or combined with other vita sign data. Vital sign data may only be stored locally to help protect the monitored user&#39;s privacy. Alternatively, the vital sign data may be securely transmitted and stored at a cloud-based server which the monitored user has previously authorized for use in storage of his vital sign data. Before vital sign data is transmitted off of the device performing the monitoring, the user can be required to provide explicit permission that such transmission is to occur, where the vital sign data will be stored, how long the vital sign data will be stored, and how such vital sign data will be used. In some embodiments, the vital sign data is output for presentation and is not stored for longer than is necessary for presentation. 
     At block  640 , a high-level sleep inference may be performed. The high-level sleep inference may determine, whether or not the user is present in bed, whether the user is entering or exiting bed, when the user is performing large movements (e.g., rolling over, tossing and turning, etc.), or if the user is getting high-quality sleep (e.g., laying still). Similar to vital sign data of block  635 , such data may be output and/or recorded. Sleep state data may only be stored locally to help protect the monitored user&#39;s privacy. Alternatively, the sleep state data may be securely transmitted and stored at a cloud-based server that the monitored user has previously authorized for use in storage of his sleep state data. Before sleep state data is transmitted off of the device performing the monitoring, the user can be required to provide explicit permission that such transmission is to occur, where the sleep state data will be stored, how long the sleep state data will be stored, and how such sleep state data will be used. In some embodiments, the sleep state data is output for presentation and is not stored for longer than is necessary for presentation. 
       FIG. 7  illustrates an embodiment  700  of a state machine for tracking a monitored user&#39;s sleep state. Embodiment  700  may be used as part of block  640  for determining a user&#39;s current sleep state. Embodiment  700  may include five possible sleep states: entering bed state  701 ; not in bed state  702 ; motion in bed state  703 ; no motion in bed state  705 ; and exiting bed state  704 . 
     If no motion-indicative waveform data is present, this may be indicative that the user is not in bed. A user who is in bed can be expected to always be moving in at least small amounts due to their vital signs. Therefore, if zero movement is observed, the user may be judged to be in state  701 . Following state  701  being determined, the next possible state that may be determined is state  702 . In state  702 , in state  702 , the monitored user is entering bed. Significant user motion may be sensed, such as according to Table 2. This may be indicative of a user entering bed and may cause the state to transition from state  701  to state  702 . 
     From state  702 , motion may continue to be detected in bed, such as due to the user rolling around, getting positioned, moving pillows, sheets, and/or blankets, reading a book, etc. state  702  may transition to state  703  while such motion continues to be detected. Alternatively, if motion is detected then zero motion is detected, this may be indicative that state  705  has been entered by the monitored user exiting bed. If this condition occurs, state  702  may transition to state  705 , then back to state  701 . 
     From state  703 , the monitored user may be determined to be exiting bed at state  705  may be become motionless at state  704 . To be “motionless” at state  704  refers to no large movements being performed by the monitored user, but he user continuing to perform small motions due to vital signs. In some embodiments, only when the monitored user&#39;s state is determined to be state  704  are vital signs treated as accurate and/or stored, recorded, or otherwise used to measure the user&#39;s vital signs. Data collected during state  703  and state  704  may be used to determine the monitored user&#39;s general sleep patterns (e.g., how much time tossing and turning, how much quality sleep, when deep sleep occurred, when REM sleep occurred, etc.). 
     The methods, systems, and devices discussed above are examples. Various configurations may omit, substitute, or add various procedures or components as appropriate. For instance, in alternative configurations, the methods may be performed in an order different from that described, and/or various stages may be added, omitted, and/or combined. Also, features described with respect to certain configurations may be combined in various other configurations. Different aspects and elements of the configurations may be combined in a similar manner. Also, technology evolves and, thus, many of the elements are examples and do not limit the scope of the disclosure or claims. 
     Specific details are given in the description to provide a thorough understanding of example configurations (including implementations). However, configurations may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary detail in order to avoid obscuring the configurations. This description provides example configurations only, and does not limit the scope, applicability, or configurations of the claims. Rather, the preceding description of the configurations will provide those skilled in the art with an enabling description for implementing described techniques. Various changes may be made in the function and arrangement of elements without departing from the spirit or scope of the disclosure. 
     Also, configurations may be described as a process which is depicted as a flow diagram or block diagram. Although each may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process may have additional steps not included in the figure. Furthermore, examples of the methods may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks may be stored in a non-transitory computer-readable medium such as a storage medium. Processors may perform the described tasks. 
     Having described several example configurations, various modifications, alternative constructions, and equivalents may be used without departing from the spirit of the disclosure. For example, the above elements may be components of a larger system, wherein other rules may take precedence over or otherwise modify the application of the invention. Also, a number of steps may be undertaken before, during, or after the above elements are considered.