Patent Description:
Vital signs, such as a breathing rate and heart rate, are physical measurements of the body that can be used to monitor a general health status of a living being. While breathing rates and heart rates may be measured by various methods, such methods under certain circumstances might not be feasible. In particular, for a mostly static target, a high velocity resolution for detection of a breathing rate may be more easily obtained compared to a heart rate, which is more challenging. Also for radar systems, the detection of the heart rate faces many challenges, due to environmental noise, harmonic components of the breathing rate, and random body movement. Thus, there is a demand for improved radar data processing for heart rate detection.

<CIT> discloses a narrow-beam millimeter wave human body heartbeat/respiration sign monitoring device with controllable irradiation direction. The direction of a main beam of a radar device can be changed by using a beam control mechanism for automatically searching a maximum position. This can be used for monitoring the heartbeat amplitude of the body surface of the human body according to the amplitude characteristic of the demodulated intermediate frequency signal phase of the radar receiver.

This demand is satisfied by the subject matter of the independent claims. Further beneficial embodiments are given by the dependent claims.

According to a first aspect, the present disclosure relates to an apparatus for measuring a heart rate of a target in a field of view of a radar sensor. The apparatus comprises processing circuitry configured to determine a frequency spectrum corresponding to time-domain data based on a receive signal generated by the radar sensor. The processing circuitry is further configured to determine a breathing rate frequency based on a maximum amplitude of the frequency spectrum. Furthermore, the processing circuitry is configured to determine a plurality of section-frequency-spectra, each corresponding to a respective section of the time-domain data, based on the breathing rate frequency, and to determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum. The processing circuitry is further configured to subtract the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum, and to estimate a heart rate of the target based on a maximum amplitude of the difference spectrum. According to a second aspect, the present disclosure relates to a radar system comprising an apparatus as described herein and a radar sensor configured to generate the receive signal.

According to a third aspect, the present disclosure relates to a method for measuring a heart rate of a target in a field of view of a radar sensor. The method comprises determining a frequency spectrum corresponding to time-domain data based on a receive signal generated by the radar sensor. The method further comprises determining a breathing rate frequency based on a maximum amplitude of the frequency spectrum. Furthermore, the method comprises determining a plurality of section-frequency-spectra, each corresponding to a respective section of the time-domain data, based on the breathing rate frequency, and further comprises determining an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum. Furthermore, the method comprises subtracting the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum and estimating a heart rate of the target based on a maximum amplitude of the difference spectrum.

According to a fourth aspect, the present disclosure relates to a program having a program code for performing a method as described herein when the program is executed on a processor or programmable hardware.

When two elements A and B are combined using an "or", this is to be understood as disclosing all possible combinations, i.e. only A, only B as well as A and B, unless expressly defined otherwise in the individual case.

<FIG> illustrates a block diagram of an example of an apparatus <NUM>. The apparatus <NUM> is to be considered in the context of a radar sensor. For instance, the apparatus <NUM> may be integrated into a radar system comprising the radar sensor such as explained below with reference to <FIG> or may be external to the radar system. In the former case, the apparatus <NUM> may be external to or (e.g., partially or fully) integrated into the radar sensor. For instance, the apparatus <NUM> may be distributed between the radar sensor and a location external to the radar sensor.

The apparatus <NUM> comprises processing circuitry <NUM> and, optionally, interface circuitry <NUM>. In case interface circuitry <NUM> is present, the interface circuitry <NUM> may be communicatively coupled (e.g., via a wired or wireless connection) to the processing circuitry <NUM>, e.g., for data exchange between the interface circuitry <NUM> and the processing circuitry <NUM>. The interface circuitry <NUM> may be any device or means for communicating or exchanging data. In case the apparatus <NUM> comprises the interface circuitry <NUM>, the interface circuitry <NUM> may be configured to receive data indicating a receive signal of the radar sensor. For instance, the interface circuitry <NUM> may be communicatively coupled to the radar sensor or to a storage device storing the data. The interface circuitry <NUM> may receive the data, e.g., via a wired or wireless coupling to the radar sensor or the storage device.

Depending on the specific implementation, the apparatus <NUM> may dispense with the interface circuitry <NUM>. For example, the processing circuitry <NUM> may determine said data. For instance, the processing circuitry <NUM> may be integrated into the radar sensor. The radar sensor may be any device that uses radio waves to, e.g., detect and locate objects. The radar sensor may be an, e.g., FMCW (frequency modulated continuous wave) radar sensor. For instance, the radar sensor may be configured to emit, by a transmitter, a radio frequency signal (Tx signal) into a field of view (a scene) of the radar sensor and receive, by a receiver, a reflection (echo; Rx signal) of the radar frequency signal. The radar sensor or an external device coupled to the radar sensor may generate radar data based on the received reflection of the radio frequency signal by, e.g., sampling the received reflection by means of an analog-to-digital converter (ADC).

The radio frequency signal (i.e. receive signal) may be in the form of a plurality of chirps, including parts of a received reflection signal which are correlated to respective emitted chirps. A chirp may be a radio frequency signal that varies in frequency over time. The frequency of the chirp may be swept over a specific frequency range, e.g., over the chirp bandwidth. For instance, the chirp may be a linearly modulated signal, i.e., a signal of which the frequency increases or decreases linearly over time.

The receive signal may be any type of signal that is generated in a receiver of the radar sensor. The receive signal may be an intermediate frequency, IF, signal, which may e.g., be created by mixing the reflection with a local oscillator signal at a specific frequency. The processing circuitry <NUM> may determine data indicating the receive signal by, e.g., sampling the receive signal and perform further processing of the data within the radar sensor. The processing circuitry <NUM> may optionally modify the sampled receive signal in a pre-processing step, e.g., for noise-reduction, DC-removal (direct current) or alike. For instance, the apparatus <NUM> may comprise memory configured to store such generated data.

The radar sensor may mix the received echo with a replica of the emitted signal using a mixer to produce an IF signal xIF(t) (e.g., a beat signal). The radar sensor may comprise one or more antennas to receive the reflected signal and the radar sensor may comprise an amplifier to receive the reflected signals from its antennas. The beat signal xIF(t) may be filtered with a low-pass filter (LPF) and then sampled by the ADC. The ADC may advantageously be capable of sampling the filtered beat signals xout(t) with a sampling frequency that is smaller than the frequency of the received signal received by the receiving antennas.

Alternatively, the processing circuitry <NUM> may partially determine the data. For instance, the processing circuitry <NUM> may determine a first part of the data, whereas at least one external processing circuitry may determine at least a second part of the data. The processing circuitry <NUM> and the external processing circuitry may, e.g., be connected within a distributed computing environment for jointly determining the data. In this case, the processing circuitry <NUM> may either be integrated into the radar sensor or may be external to the radar sensor. The processing circuitry <NUM> may receive the second part of the data, e.g., via an interface to the external processing circuitry such as interface circuitry <NUM>, and further process the first and the second part of the data.

In another alternative, the processing circuitry <NUM> is partially integrated into the radar sensor and is partially external to the radar sensor. In such cases, the interface circuitry <NUM> is optional. The processing circuitry <NUM> may, for instance, comprise a first part (first processing circuitry) which is integrated into the radar sensor and a second part (second processing circuitry) which is external to the radar sensor. In this case, the determination of the data and/or further processing may be performed by the first and second part of the processing circuitry <NUM> in a distributed manner.

The processing circuitry <NUM> may be, e.g., a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which or all of which may be shared, a digital signal processor (DSP) hardware, an application specific integrated circuit (ASIC), a microcontroller or a field programmable gate array (FPGA). The processing circuitry <NUM> may optionally be coupled to, e.g., read only memory (ROM) for storing software, random access memory (RAM) and/or non-volatile memory.

As part of the above-described radar system and its multiple components, the various embodiments of the apparatus <NUM> described herein are provided for determining a heart rate and a breathing rate of a target in the field of view of the radar sensor. For this, the apparatus <NUM> is configured to receive data based on the receive signal, including time-domain data <NUM>, and perform processing steps based the time-domain data <NUM>. The time-domain data <NUM> may be checked and/or processed into other forms of data before being used for generating a plurality of frequency spectra that may be analyzed for determining the heart rate and breathing rate of the target.

The processing circuitry <NUM> of the apparatus <NUM> is configured to determine a frequency spectrum corresponding to the time-domain data <NUM> and to determine a breathing rate frequency based on a maximum amplitude therein. The breathing rate frequency may be determined by an analysis of the frequency spectrum, including a search for and identification of a maximum amplitude of the frequency spectrum. The breathing rate frequency may be determined by identifying a frequency bin, within which the amplitude is at a maximum value for the entire frequency spectrum. The breathing rate frequency may be set to a value within a frequency range corresponding to the frequency bin, which may be equivalent to a center frequency within the frequency bin.

The frequency spectrum may take the form of a variety of frequency spectrum types. For example, the frequency spectrum may be an amplitude spectrum, wherein the frequency spectrum comprises a plurality of amplitudes, each amplitude corresponding to a respective frequency bin (or frequency component). In a particular example, the frequency spectrum may be a magnitude spectrum representing a magnitude or absolute value of complex amplitudes in a frequency domain. As such, the magnitude spectrum may provide information about an intensity or strength corresponding to a frequency component without considering phase information. Alternatively, the frequency spectrum may be a power spectrum representing a power of each frequency component. The frequency spectrum may also be a normalized spectrum, representing the amplitudes or powers of the frequency components scaled to a specific reference or normalized to a specific value. The various types of frequency spectra presented for the above-described frequency spectrum may also analogously apply to all other frequency spectra used in further processing.

The processing circuitry <NUM> is further configured to determine a plurality of section-frequency-spectra, each corresponding to a respective section of the time-domain data <NUM>. Furthermore, the plurality of section-frequency spectra are determined based on the breathing rate frequency. For example, the time-domain data <NUM> may be divided into a plurality of sections, wherein each section corresponds to a time interval, which may be the same amount of time for each section. The time interval may be defined by the inverse of the breathing rate frequency. For example, the inverse of the breathing rate frequency may provide a time interval equivalent to an amount of time between successive breaths taken at a frequency equivalent to the breathing rate frequency. As such, each section of the time-domain data <NUM> may include a single breath at a similar time instance within the time interval.

The processing circuitry <NUM> is further configured to determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum. Each of the section-frequency-spectra may comprise a common frequency range and a common number of frequency bins (e.g. a same resolution). The processing circuitry <NUM> may be configured to sum the respective amplitudes of each section-frequency-spectrum and divide the sum by the number of section-frequency-spectra. More specifically, the respective amplitude values of each section-frequency-spectrum that each correspond to a particular frequency bin, such as a frequency bin of an equivalent position or index, may be summed and divided by the number of section-frequency-spectra. This may also be done for each frequency bin of the frequency spectra, so that each frequency bin has associated therewith an averaged amplitude value. The plurality of averaged amplitude values, each associated with its respective frequency bin, may be used for determining the averaged-frequency-spectrum.

The processing circuitry <NUM> is configured to subtract the averaged-frequency-spectrum from the (original) frequency spectrum to obtain a difference spectrum. For the subtraction, the respective amplitude values of each frequency bin of the averaged-frequency-spectrum may be subtracted from the respective amplitude values of each frequency bin of the (original) frequency spectrum. For example, an amplitude value of a particular frequency bin of the averaged-frequency-spectrum may be subtracted from an amplitude value of an analogous frequency bin with an equivalent position or index of the (original) frequency spectrum. This may be done for each frequency bin of the averaged-frequency-spectrum and the (original) frequency spectrum to obtain a difference value for each frequency bin. The respective difference values may then be used to obtain a full difference spectrum. The difference spectrum may also be considered as a difference dataset with respective difference-in-amplitude values, wherein the difference dataset may be plotted according to the respective frequency bins to provide a difference (frequency) spectrum.

Furthermore, the processing circuitry <NUM> is configured to estimate a heart rate of the target based on a maximum amplitude of the difference spectrum. The heart rate may correspond to a specific heart rate frequency, which may correspond to the maximum amplitude of the difference spectrum. Similar to finding the breathing rate frequency based on a maximum amplitude, the heart rate frequency may be determined by an analysis of the difference spectrum, including a search for and identification of a maximum amplitude of the difference spectrum. The heart rate frequency may be determined by identifying a frequency bin, within which the amplitude is at a maximum value for the entire difference spectrum. The heart rate frequency may be determined as a value within a frequency range corresponding to the frequency bin, which may be equivalent to a center frequency within the frequency bin.

As such, the processing circuitry <NUM> is configured to obtain a heart rate frequency (e.g., estimate a heart rate) and obtain (i.e. determine) a breathing rate frequency. The heart rate frequency and breathing rate frequencies may each be used to provide health information related to the target in the field of view of the radar sensor. Such heart rate and breathing rate frequencies may also be recorded multiple times, which may performed over a period of days, weeks, months, or years to obtain long-term health information related to the target. The apparatus <NUM> may comprise other features that may improve the accuracy of the obtained breathing rate and/or heart rate frequencies. Such features will be discussed in greater depth with reference to <FIG>. The features of the apparatus <NUM> previously discussed are also discussed in greater depth with illustrative examples with reference to <FIG>.

<FIG> depicts an exemplary frequency spectrum <NUM>-<NUM>. The y-axis may correspond to an amplitude of the frequency. As previously described, the amplitude may correspond to either a magnitude or a power of the frequency spectrum, which may have been normalized or re-scaled with respect to a specific value. The x-axis of the frequency spectrum <NUM>-<NUM> may correspond to values for a frequency within a pre-specified frequency range. The frequencies of the x-axis may be measured in inverse minutes, which may be any arbitrary unit per minute (e.g. breaths per minute or beats per minute). The depicted inverse minutes are directly proportional to a scale measured in Hertz (Hz), e.g., s-<NUM> or inverse seconds.

The frequency spectrum <NUM>-<NUM> is shown with a first amplitude peak <NUM>-<NUM> corresponding to the maximum amplitude. The maximum amplitude peak <NUM>-<NUM> is located at a first frequency, which may correspond to the breathing rate frequency. For example, the breathing rate frequency may be <NUM>, which would correspond to <NUM> breaths per <NUM> seconds, or <NUM> breaths per minute, which is a breathing rate within a usual range for a newborn baby (usually ranging from <NUM> to <NUM> breaths per minute). The maximum amplitude is depicted to correspond to a value of <NUM> on the x-axis, or <NUM> breaths per minute.

The frequency spectrum <NUM>-<NUM> is also shown with a second amplitude peak <NUM>-<NUM> at a second frequency that is twice the frequency of the breathing rate frequency. It can be seen that the second frequency corresponds to <NUM> breaths per minute (<NUM>). As such, the second amplitude peak <NUM>-<NUM> may also be referred to as the first harmonic of the breathing rate frequency. Furthermore, the frequency spectrum <NUM>-<NUM> is shown with a third amplitude peak <NUM>-<NUM> at a third frequency that is three times the frequency of the breathing rate frequency. It can be seen that the third frequency corresponds to <NUM> breaths per minute (<NUM>). As such, the third amplitude peak <NUM>-<NUM> may also be referred to as the second harmonic of the breathing rate frequency. The use of the term "amplitude peak" refers to a local maximum that is significantly greater than other possible local maxima, so as to distinguish itself from other local maxima that are not significantly greater in amplitude than various local minima of the same spectrum.

Additionally, the frequency spectrum <NUM>-<NUM> comprises a fourth peak <NUM> that corresponds to a fourth frequency that is slightly below the third introduced frequency. The fourth peak may correspond to a heart rate. Within the depicted scale, the fourth peak may correspond to a heart rate that is approximately <NUM> beats per minute (approx. <NUM>), slightly below the second harmonic frequency of <NUM> breaths per minute (<NUM>). The heart rate of <NUM> beats per minute is a heart rate within a usual range for a resting heart rate of a newborn baby (usually ranging from <NUM> to <NUM> beats per minute).

<FIG> depicts an exemplary averaged-frequency-spectrum <NUM>-<NUM>. As previously described, the averaged-frequency-spectrum <NUM>-<NUM> is based on the breathing rate frequency. The time-domain data <NUM> may be divided into a plurality of sections with a common time interval that is defined by the breathing rate frequency. For example, the common time interval may be the inverse of the breathing rate frequency, such as the previously mentioned breathing rate frequency of <NUM> breaths per minute or <NUM>, as depicted in <FIG>. For this example, the time interval may be a time of approximately <NUM> seconds. More specifically, the time-domain data <NUM> may be divided into a plurality of non-overlapping sections, with each section comprising time-domain data <NUM> that includes data recorded within successive time intervals, each of which may be equivalent to a time amount of <NUM> seconds. Each of the sections corresponding to a portion of the time-domain data <NUM> within a respective window of <NUM> seconds may then have a respective section-frequency-spectrum determined.

An average of the plurality of the section-frequency-spectra may be calculated as previously described to obtain the averaged-frequency-spectrum <NUM>-<NUM>, which is depicted in <FIG>. The averaged-frequency-spectrum <NUM>-<NUM> also comprises a first amplitude peak <NUM>-<NUM> analogous to the first amplitude peak <NUM>-<NUM> of the (original) frequency spectrum <NUM>-<NUM>, with analogous characteristics. For example, the first amplitude peak <NUM>-<NUM> is also centered at the breathing rate frequency, which is also approximately <NUM> breaths per minute (<NUM>). The averaged-frequency-spectrum <NUM>-<NUM> also comprises a second amplitude peak <NUM>-<NUM> analogous to the second amplitude peak <NUM>-<NUM> of the (original) frequency spectrum <NUM>-<NUM>, with similar analogous characteristics. As depicted, this frequency is also located at a first harmonic of the breathing rate frequency. The same is depicted for a third amplitude peak <NUM>-<NUM> analogous to the third amplitude peak <NUM>-<NUM> of the (original) frequency spectrum <NUM>-<NUM>, also located at a second harmonic of the breathing rate frequency.

The major difference between the depicted (original) frequency spectrum <NUM>-<NUM> and the averaged-frequency-spectrum <NUM>-<NUM> is the lack of a heart rate component. Rather the particular frequency component in the averaged-frequency-spectrum <NUM>-<NUM> near the heart rate frequency of <NUM> beats per minute (approx. <NUM>) only comprises noise. In the averaged-frequency-spectrum <NUM>-<NUM>, this frequency is depicted with an arrow and label showing that the "heart rate component has been filtered". The heart rate may be effectively filtered or at least mostly filtered when choosing a time interval for each of the section-frequency-spectra that corresponds to the breathing rate of the target, i.e. the inverse of the breathing rate frequency.

In this scenario, each section of the time-domain data <NUM> will have amplitude values corresponding to a breathing in and breathing out of the target at an analogous position of the respective section. As such, the breathing rate frequency will be depicted very similarly in the averaged-frequency spectrum <NUM>-<NUM> compared to the (original) frequency spectrum <NUM>-<NUM>. Furthermore, the harmonic components appear mostly unaffected. When dividing the time-domain data <NUM> into sections corresponding to the inverse of the fundamental frequency, each section captures a complete period of the signal. In other words, by dividing the time-domain data <NUM> according to the fundamental frequency, each section aligns with a specific phase of the fundamental signal. As a result, the influence by the harmonic components, being integer multiples of the fundamental frequency, tend to remain consistent within each section of the time-domain data <NUM>, assuming the harmonics are stable.

However, the heart rate component, unlike the first and second harmonics, is located at a position in the (original) frequency spectrum <NUM>-<NUM>, such that its corresponding data in the plurality of sections of the time-domain data <NUM> does not remain aligned across the sections. Depending on its position and strength, it may be fully averaged out to no longer be visible in the averaged-frequency-spectrum <NUM>-<NUM> or may be mostly averaged out to be only partially visible in the averaged-frequency-spectrum <NUM>-<NUM>. In either case, the difference spectrum may comprise a maximum amplitude that corresponds to a heart rate estimate.

While the previously described techniques including the determination of the difference spectrum and the heart rate estimation based on the maximum amplitude thereof may provide an adequately accurate heart rate estimation, this estimation may be improved by further processing techniques of the time-domain data <NUM>. Such techniques will be discussed in greater depth with reference to <FIG>.

<FIG> provides a flow chart of exemplary pre-processing steps that may be applied before determining the frequency spectrum <NUM>-<NUM>. The pre-processing steps includes five possible stages, or more specifically, five possible checks, which may be used in different combinations according to various embodiments. In any case where a check, such as meeting a required threshold or range, is not fulfilled, the processing circuitry <NUM> may be configured to keep recording (measuring) further frames of the time-domain data <NUM>, as depicted by the flow chart step <NUM> to wait for the next frame. If each of the applicable checks are fulfilled, the processing circuitry <NUM> may be configured to proceed to further processing beyond pre-processing (i.e. to core processing), as depicted by the flow chart step <NUM>. A first check may be a frame number check <NUM>-<NUM>. For example, the processing circuitry <NUM> may be automated to accept a certain number of frames of the time-domain data <NUM> up to a pre-specified frame-threshold <NUM>-<NUM>. The frame-threshold <NUM>-<NUM> may be <NUM> frames or another pre-specified number.

The pre-specified number of frames may correspond to a pre-specified amount of time (i.e. a time window) that is designated for receiving the receive signal. More specifically, the time-domain data <NUM> may correspond to the time window. For the example of an FMCW radar sensor, if the time window is <NUM> seconds, the time-domain data <NUM> may correspond to <NUM> seconds of the radar system emitting frequency-modulated continuous wave signals and receiving the corresponding echoed (i.e. reflected) signals. For example, in such a case, the apparatus <NUM> may receive <NUM> frames in <NUM> seconds, or <NUM> frame every <NUM> seconds. The time window of <NUM> seconds may be chosen for a desired balance. On one hand, it may be beneficial to obtain more time-domain data for more accurate averaging. On the other hand, the time window should not be extended too long, where a random body movement of the target or other possible disturbances have a higher likelihood to cause the time-domain data <NUM> to become invalid. For example, in the case of the target being a baby, it becomes increasingly unlikely after <NUM> seconds that the baby will remain adequately still for obtaining accurate measurements for valid time-domain data <NUM>. The time window may be adjusted according to each use case or a user input, such as ranging between <NUM> and <NUM> seconds.

A second check may be a noise check <NUM>-<NUM>. The noise check <NUM>-<NUM> may be used to determine whether the time-domain data <NUM> based on the receive signal is below a pre-specified noise-level threshold <NUM>-<NUM>. For example, the processing circuitry <NUM> may be configured to apply a high-pass filter for a processing of the time-domain data <NUM>. Based on the high-pass filter processing, the processing circuitry <NUM> may be configured to calculate an average amplitude of the high-pass filtered time-domain data <NUM> to obtain a noise-level average. The noise-level average may then be checked if it is below the noise-level threshold <NUM>-<NUM>. Proceeding forward, the processing circuitry <NUM> may be configured to determine the frequency spectrum <NUM>-<NUM> if the noise-level average is under a pre-specified noise-level threshold <NUM>-<NUM>.

A third check may be a range check <NUM>-<NUM>. The range check <NUM>-<NUM> may be used to determine whether the receive signal comprises sufficient time-domain data from a pre-specified valid range <NUM>-<NUM>. If it is determined that the time-domain data <NUM> or a required amount of the time-domain data <NUM> does not correspond to the pre-specified range, the processing circuitry <NUM> may be configured to record new measurements for new time-domain data <NUM> (i.e. waiting for the next frame <NUM> for new measurements). The range of the time-domain data <NUM> may be determined by a range discrete Fourier transformation (DFT) of the time-domain data <NUM>. The range-DFT may lead to the apparatus <NUM> obtaining a range buffer divided into a plurality of range bins. More specifically, the results of the range-DFT may be stored in a memory of the apparatus <NUM>, wherein portions of the time-domain data <NUM> are stored according to the respective range bins. A first range bin may correspond to a first range from the radar sensor, a second range bin may correspond to a second range from the radar sensor, etc..

As part of or in addition to the range check <NUM>-<NUM>, the range-DFT may also be performed on the basis of a plurality of frames of the time-domain data <NUM>, which will be discussed in greater detail with reference to <FIG>. Furthermore, two breathing checks <NUM>-1a; <NUM>-1b (i.e. breathing pattern checks) based on respective breathing check verifications <NUM>-2a; <NUM>-2b, will be explained with reference to <FIG>.

<FIG> provides a visual representation for exemplary processing steps for measuring a heart rate of a target in a field of view of a radar sensor, including the above-described range-DFT. As depicted, the processing circuitry <NUM> may obtain a plurality of frames <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-n, which are maintained as separate units of the time domain data <NUM>. The three dots between the <NUM>nd frame and n-th frame are meant to express any number of frames that may be part of the time-domain data <NUM> between the <NUM>nd frame and n-th frame. As previously described, the number of frames may be <NUM> or another pre-specified number.

For example, the processing circuitry <NUM> may be configured to perform mean removal of the time-domain data <NUM>. More specifically, the processing circuitry <NUM> may be configured to calculate a mean value of amplitude values of the time-domain data <NUM> and to subtract the mean value from the amplitude values to generate mean-removed time-domain data. This may shift the previous values, which may include an offset, to be centered on zero. Generally, using a form of mean-removed time-domain data <NUM> may enable generating the frequency spectra <NUM>-<NUM>; <NUM>-<NUM> more accurately. If applicable, the mean-removed version of the time-domain data <NUM> may also be used to perform further checks more accurately, such as confirming a valid breathing rate and heart rate for the target (provided in <FIG>).

The mean removal may be applied for the entire time-domain data <NUM> or on a per frame basis. Using a per frame basis, the mean value of amplitude values may be a mean value of the given frame, which may be subtracted from the amplitude values of the given frame. This may generate mean-removed time domain data specifically for the given frame. As depicted, a first portion of mean-removed time-domain data <NUM>-<NUM> may correspond to a first time frame <NUM>-<NUM>, a second portion of mean-removed time-domain data <NUM>-<NUM> may correspond to a second time frame <NUM>-<NUM>, up to an n-th portion <NUM>-n for an n-th frame <NUM>-n. Furthermore, the previously described range-DFT may be performed using the various portions of mean-removed time-domain data. Further references to the processing of the time-domain data <NUM> may apply either to mean-removed or non-mean-removed time domain data <NUM>.

The processing circuitry <NUM> may be configured to perform a separate range-DFT for each frame <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-n (i.e. on a per frame basis) of the time-domain data <NUM>. If mean-removal of the time-domain data is applied, the range-DFT may be performed for each portion of the mean-removed time-domain data <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-n corresponding to their respective frames. As such, the range-DFT may be referred to as a short time range-DFT being applied to short time range data (respective frames or portions) of the time-domain data <NUM>. Each of the respective range-DFTs being performed on the respective frames or portions of the (mean-removed) time-domain data <NUM> may lead to generating a respective range buffer <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-n, as depicted, corresponding to each frame in <FIG>. In the case that the number of frames for a full iteration of measurements is pre-specified to be <NUM> frames (i.e. n = <NUM>), the processing circuitry <NUM> may perform <NUM> respective range-DFTs for <NUM> frames or portions of the (mean-removed) time-domain data <NUM> to obtain <NUM> respective buffers. Each of the range buffers may comprise an equal number of range bins, so that a particular range bin of a first range buffer may have a corresponding range bin at an equivalent position or with an equivalent index within a second range buffer.

The processing circuitry <NUM> may identify a particular range bin for each range buffer with the greatest value (e.g., greatest amplitude value or mean-removed amplitude value) of the (mean-removed) time-domain data <NUM>. More specifically, a collection of corresponding range bins across the plurality of (e.g., <NUM>) range buffers may be chosen, wherein the collection of range bins each correspond to an equivalent index or position within its respective range buffer. The processing circuitry <NUM> may be configured to choose such a collection of range bins corresponding to a common position or index, each (or most) of the range bins comprising a maximum amplitude of the (mean-removed) time-domain data <NUM>. As such, the collection of corresponding (i.e. analogously positioned) range bins may also be referred to simply as a collective "range bin" or "identified range bin", referring specifically to the common position within each range buffer. <FIG> shows an identified range bin <NUM> corresponding to a maximum amplitude for the (mean-removed) time-domain data <NUM>.

The processing circuitry <NUM> may be configured to check whether or not the identified range bin <NUM> is within a pre-specified selection of range bins for the respective range buffers. The pre-specified selection of range bins may correspond to a range of measurement within an acceptable range of distance away from the radar sensor. Such an acceptable range of distance may be, for example, between <NUM> to <NUM> meters, or another acceptable range of a similar scale (e.g., between <NUM> meters and <NUM> meters). If the identified range bin <NUM> is not within the pre-specified selection of range bins, the time-domain data <NUM> may be considered to be invalid and a new iteration of measurements by the radar system may begin (i.e. wait for the next frame <NUM>). On the other hand, if the identified range bin <NUM> is indeed within the pre-specified selection of range bins, the time-domain data <NUM> may be considered to be within a pre-specified valid range <NUM>-<NUM>.

Based on a determination of a valid range of the time-domain data <NUM>, the processing circuitry <NUM> may determine the frequency spectrum <NUM>-<NUM> and the averaged-frequency-spectrum <NUM>-<NUM> for the (mean-removed) time-domain data <NUM> corresponding to the identified range bin <NUM> for each range buffer <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-n. In other words, the processing circuitry <NUM> may be configured to transform the (mean-removed) time-domain data <NUM> by a range-DFT, select a portion of the data generated, and determine the frequency spectrum <NUM>-<NUM> based on the selected data, which corresponds to a certain portion of the time-domain data <NUM>.

Since the range-DFT may be applied across the range buffers, it may be considered as being applied to long time-domain data. By focusing the Doppler-DFT to the identified range bin <NUM>, the frequency spectra <NUM>-<NUM>; <NUM>-<NUM> may be more accurately determined.

The processing steps presented in <FIG>, including the range-DFT applied to the (mean-removed) time-domain data, may generate a (mean-removed) range-time dataset <NUM>. The range-time dataset <NUM> is depicted to include the data within the identified range bin <NUM>. Based on the identified range bin <NUM>, the range-time dataset <NUM> may be extracted for further checks <NUM>-1a; <NUM>-1b.

<FIG> depicts an exemplary range-time profile <NUM> corresponding to the range-time dataset <NUM>. The range-time profile <NUM> may be used for checking if there is a consistent pattern (i.e. breathing pattern) that corresponds to a breathing of the target. Such breathing pattern checks <NUM>-1a; <NUM>-1b may be performed before the Doppler-DFT, wherein the Doppler-DFT is only performed if the breathing pattern checks <NUM>-1a; <NUM>-1b pass respective thresholds <NUM>-2a; <NUM>-2b (see <FIG>).

The breathing pattern checks <NUM>-1a; <NUM>-1b, which may be applied to the time-domain data <NUM> or specifically, the mean-removed time-domain data, as previously described, corresponding to the identified range bin <NUM>. Alternatively, the processing circuitry <NUM> may be configured to perform mean removal after the range-DFT, for which a mean value of amplitude values in the identified range bin <NUM> is calculated and subtracted from the mean value from the amplitude values in the identified range bin <NUM>. This may shift the information of the time-domain data <NUM> corresponding to the identified range bin <NUM> from the previous values to zero-centered values to generate the mean-removed range-time dataset <NUM> for the identified range bin <NUM>. The mean-removed range-time dataset <NUM> may enable faster processing for further checks of the data to ensure that a valid breathing rate and heart rate may be found for the target in the field of view of the radar sensor. If such checks are passed, then the frequency spectrum may be determined specifically based on the mean-removed range-time dataset <NUM>.

The exemplary range-time profile <NUM> based on the mean-removed range-time dataset <NUM> (with mean-removal performed before or after the range-DFT) shows visually how this shifts the time-domain data <NUM> to be centered around zero. The x-axis of the range-time profile <NUM> may be correspond to any arbitrary unit of time (e.g., seconds), while the y-axis of the range-time profile <NUM> may correspond to a complex number amplitude directly proportional to a range (i.e. distance or displacement), which may be measured using an appropriate unit of thereof.

In fact, the range-time profile <NUM> may provide a visual depiction of a breathing pattern when plotted. A breathing of the target may include a breathing in and a breathing out, which may repeat in a steady pattern over time. For example, while breathing in, air is inhaled and a chest of the target may expand, which may thus decrease the range (i.e. distance or displacement) between the target and the radar sensor. On the other hand, while breathing out, air is exhaled and the chest of the target may contract, which may thus increase the range (distance or displacement) between the target and the radar sensor. Since a breathing pattern must follow a recognizable pattern, certain checks may be implemented to ensure a proper breathing of the target. With a consistent breathing, the processing steps outlined in <FIG> and <FIG> may be reliably performed to estimate the heart rate.

The mean-removed range-time dataset <NUM> may correspond to the mean-removed range-time profile <NUM>, which may depict a variation in the range (distance or displacement) that may be checked for consistency. In the first breathing check <NUM>-1a presented in <FIG>, the processing circuitry <NUM> may be configured to check whether or not a peak-to-peak value for the mean-removed range-time dataset <NUM> is within pre-specified limits based on an average of a plurality of previous peak-to-peak values in the mean-removed range-time dataset <NUM>.

For example, a first peak-to-peak value corresponding to an increase in range (distance or displacement) starting at a time t = <NUM> may be approximately <NUM> x <NUM>-<NUM> arbitrary units,. A second peak-to-peak value corresponding to a decrease in range (distance or displacement) may be approximately <NUM> x <NUM>-<NUM> units. A third, fourth, and fifth peak-to-peak values may also approximately <NUM> x <NUM>-<NUM> units, while a sixth peak-to-peak value, for the third decrease in range (distance or displacement), may be approximately <NUM> x <NUM>-<NUM> units. A plurality of peak-to-peak values may be checked. Optionally, each peak-to-peak value may be checked, wherein a peak-to-peak is determined to correspond to any increase from a local minimum to a local maximum or to correspond to any decrease from a local maximum to a local minimum (wherein local minima or local maxima of a very narrow frequency range may be neglected). A peak-to-peak-threshold <NUM>-2a (depicted as part of the breathing check verification in <FIG>) for all peak-to-peak values may be determined based on a plurality or all of the peak-to-peak values, such as by averaging. The processing circuitry <NUM> may be configured to determine the frequency spectrum for the mean-removed range-time dataset <NUM> if all or a large majority of the peak-to-peak values, as previously presented, are within the limits of the peak-to-peak-threshold <NUM>-2a.

In a second breathing check <NUM>-1b in <FIG>, the processing circuitry <NUM> may be configured to determine a plurality of derivative values corresponding to a rate of change of a range at a respective time of the mean-removed range-time dataset <NUM> (or the range time profile <NUM>). Each derivative may be determined based on an instance of time, which may be within or correspond to an interval of time. The x-axis of the range-time profile <NUM> may correspond to a time unit of seconds. As such, the range-time profile <NUM> may correspond to the previously mentioned time window of <NUM> seconds. Each full cycle from a local minimum (or maximum) to a neighboring local minimum (or maximum) may thus correspond to a time of a <NUM> seconds for one complete breath, which is a time interval that corresponds to a breathing rate frequency of <NUM>, or a breathing rate of <NUM> beats per minute. Within the time window of <NUM> seconds, <NUM> inhalations (increases in range/displacement) are depicted, along with <NUM> exhalations, corresponding to approximately <NUM> breaths for the <NUM> seconds (<NUM>).

Each inhalation may have associated therewith a derivative value. For example, a derivative value may correspond to change in units of range (distance or displacement) of the dataset from the local minimum to the local maximum divided by the change in time in seconds. The same may analogously be true for a change from a local maximum to a local minimum (wherein local minima or local maxima of a very narrow frequency range may be neglected, as previously described). Alternatively or additionally, a derivative value may correspond to a change in range (displacement) divided by a corresponding change in time, wherein the time interval within the range-time profile <NUM> for the corresponding range and time changes may be arbitrarily chosen. The processing circuitry <NUM> may be configured to determine any number of derivative values within the range-time profile <NUM>, which may be from a local minimum to a local maximum or vice versa, or which may correspond to a narrower time interval within such points.

The processing circuitry <NUM> may be configured to determine a variance of such derivative values as a derivative-threshold <NUM>-2b (depicted as part of the breathing check verification in <FIG>), which may be based on an absolute value of the determined derivatives. Furthermore, the processing circuitry <NUM> may check that all or a large majority of the determined derivatives (or their absolute values) are within the derivative-threshold <NUM>-2b. The processing circuitry <NUM> may then determine the frequency spectrum <NUM>-<NUM> for the mean-removed range-time dataset <NUM> if each derivative value is within the derivative-threshold <NUM>-2b.

Generally, the five above-described checks <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-1a; <NUM>-1b may be used in any combination to ensure by means of their respective thresholds <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-<NUM>; <NUM>-2a; <NUM>-2b that the time-domain data <NUM> is valid data, which may be considered reliable for determining a breathing rate and a heart rate of the target within the field of view of the radar sensor. Such checks may be part of a pre-processing of the apparatus <NUM>. If all applied checks are passed, the processing by the apparatus <NUM> may continue to its core-processing steps, as outlined in <FIG> and <FIG>. In addition to the previously outlined core-processing steps, the core-processing may include further processing features, which will be presented in greater detail with reference to <FIG>.

<FIG> provides a flow chart of exemplary optional processing steps <NUM>; <NUM>; <NUM>; <NUM> that may be applied for measuring a heart rate of a target in a field of view of a radar sensor.

A first optional processing step shown in <FIG> is de-noising <NUM> with a finite-impulse-response filter. The processing circuitry <NUM> may be configured to determine the frequency spectrum for the time-domain data and to subsequently apply a finite-impulse-response filter to the amplitude values of the frequency spectrum. A finite-impulse-response, FIR, filter is generally a digital filter used in signal processing. The FIR filter may be used, particularly as a specific form of a high-pass filter, to eliminate signals corresponding to vibrating objects that may be present in a background of the target in the field of view of the radar sensor. The vibrating may include a switching back and forth between positive and negative directions of movement through space. In other words, the vibrating may be switching back and forth between having a positive velocity in a positive direction and a negative velocity in a negative direction, which may be recorded as part of the time-domain data <NUM> and may disturb the signal processing of the time-domain data <NUM>. For example, air conditioning is well-known as a vibrating object that may interfere with signal processing techniques of radar systems, among other possible causes.

Whereas FIR filters may conventionally or usually be used for filtering (i.e. filtering by data processing) of time-domain data before a Doppler-DFT, the processing circuitry <NUM> may be specifically configured to apply the FIR filter after the Doppler-DFT to data of the generated frequency spectrum <NUM>-<NUM> (i.e. the time-domain data <NUM> in processed form as part of the frequency spectrum <NUM>-<NUM>) to prevent a disturbance in signal processing by a vibrating object. For example, for a wave signal emitted by a frequency-modulated continuous wave, FMCW, radar, a frequency component in a Doppler-DFT analysis may correspond to a speed or velocity of an object in the field of view of the radar sensor, as derived from the Doppler Effect, describing how the frequency of an emitted and reflected wave may change when reflected off an object with a relative motion from the radar sensor.

While an FIR filter, such as an FIR-high-pass filter, may filter data before a Doppler-DFT in the time-domain according to a high speed or a particular speed component within the signal, an FIR filter applied to filter the data after a Doppler-DFT in the frequency domain may filter a high change of speed, or a particular change-of-speed component within the data. As such, a disturbance in the data by a vibration, which may correspond to a particular change of speed that causes movement in the previously described positive and negative directions, may be filtered. In general, various versions of the FIR filter may be applied to filter the influence of various vibrating objects in the field of view of the radar sensor, particular for vibration ranges of common household objects, such as an air-conditioning unit.

A second optional processing step shown in <FIG> relates to check the validity <NUM> of the frequency spectrum <NUM>-<NUM> and the averaged-frequency-spectrum <NUM>-<NUM>. In a first check, the processing circuitry <NUM> may be configured to determine the breathing rate frequency by detection of a first frequency corresponding to a maximum amplitude value of the frequency spectrum <NUM>-<NUM> and detection of a second frequency corresponding to a local maximum of amplitude values in the frequency spectrum <NUM>-<NUM> within a pre-specified frequency range from twice the first frequency (e.g. a first harmonic). Referring now back to <FIG> and the depicted (original) frequency spectrum <NUM>-<NUM>, the breathing rate frequency may be detected at its maximum amplitude <NUM>-<NUM>, corresponding to the breathing rate (e.g., <NUM> breaths per minute), while the following local maximum may be detected within a pre-specified range of twice the breathing rate frequency (e.g., <NUM> breaths per minute). In this particular case, the pre-specified frequency range for the following local maximum may be between <NUM> and <NUM> breaths per minute, or another frequency range based on accuracy or precision requirements, which may be adjustable.

Furthermore, the processing circuitry <NUM> may be configured to detect a third frequency corresponding to another local maximum of amplitude values in the frequency spectrum, specifically within a second pre-specified frequency range from three times the first frequency (i.e. second harmonic). In the (original) frequency spectrum <NUM>-<NUM> of <FIG>, the second following local maximum from the breathing rate frequency may be detected at <NUM> breaths per minute or within the second pre-specified range therefrom. In this particular case, the second pre-specified range may be <NUM> to <NUM> breathes per minute, or another frequency range that may also be adjustable based on accuracy or precision requirements. Particularly for the second harmonic of the breathing rate frequency, the pre-specified range may be narrow enough to distinguish the second harmonic from the local maximum corresponding to the heart rate frequency. If either the frequency values of the first or second harmonics are not within the respective pre-specified ranges, the processing circuitry <NUM> may be configured to label the time-domain data <NUM> as invalid and cancel further processing of the time-domain data <NUM> and begin the procedure for recording a new iteration of time-domain data.

Such validity tests may also be performed related to the averaged-frequency-spectrum <NUM>-<NUM> in <FIG>. For example, the processing circuitry <NUM> may be configured to verify that a maximum amplitude value of the averaged-frequency-spectrum <NUM>-<NUM> corresponds to a frequency that is within a pre-specified frequency range from the breathing rate frequency. Such a check may provide an accurate assessment of whether or not the breathing rate frequency that is determined based on the maximum amplitude of the (original) frequency spectrum <NUM>-<NUM> is accurate. In a case where the breathing rate frequency is not accurate, the time-domain data <NUM> may not have been divided into its plurality of sections with an adequately accurate time interval, being the inverse of a somewhat inaccurate breathing rate frequency. This may have led to a shift in the maximum-amplitude frequency to a frequency in the averaged-frequency-spectrum <NUM>-<NUM> away from the breathing rate frequency in the (original) frequency spectrum <NUM>-<NUM>. For example, such a pre-specified range may be between <NUM> and <NUM> breaths per minute, or it may adjustable to another range, depending on accuracy and precision requirements. If the maximum amplitude value of the averaged-frequency-spectrum <NUM>-<NUM> corresponds to a frequency that is within its pre-specified frequency range from the breathing rate frequency, then the later processing steps towards determining the difference spectrum and estimating the heart rate may be performed.

If this is not the case, the processing circuitry <NUM> may be configured to determine a new breathing rate frequency based on a neighboring local maximum of amplitude values in the (original) frequency spectrum <NUM>-<NUM>. For example, a highly localized spike may be present in the determined (original) frequency spectrum <NUM>-<NUM>, which may have caused the assignment of a somewhat inaccurate value for the breathing rate frequency. To prevent this, the processing circuitry <NUM> may be configured to accept a breathing rate frequency that corresponds to an amplitude within a certain pre-specified range. If the breathing rate frequency is determined to be inaccurate (for any reason), the processing circuitry <NUM> may be configured to repeat its previously performed calculations for a following or previous local maximum from the previously labeled breathing rate frequency (wherein local minima or local maxima of a very narrow frequency range may be neglected, as previously described).

Such a local maximum may also be searched for within a customized frequency range that may correspond to a breathing rate that corresponds to the physical characteristics of the target. For example, a target that is a human newborn infant may have a known range for breathing rate frequency, given certain assumptions (e.g., being at rest). A newborn infant may have such a search range vary between <NUM> and <NUM> breaths per minute, an older baby/toddler between <NUM> and <NUM> breaths per minute, and an adult between <NUM> and <NUM> breaths per minute. Other physical characteristics besides the age of the target may also affect the customized range. Such configurations may be adjustable. Adjustable configurations may be adjusted by a user input received through a user interface, among other possible means.

The processing circuitry <NUM> may be configured to repeat the same processing steps for the new breathing rate frequency, such as for a following or previous local maximum, to obtain a new averaged-frequency-spectrum based on the new breathing rate frequency. Furthermore, the previously described checks for the maximum amplitude value of the new averaged-frequency-spectrum may be verified to correspond to a frequency that is within its pre-specified frequency range from the new breathing rate frequency. If this is not the case, another local maximum of the (original) frequency spectrum <NUM>-<NUM> may be checked if it is within the customized frequency range based on the target's physical characteristics, with the previously described steps repeated. If no local maximum within the customized frequency range meet the required checks, then the processing circuitry <NUM> may be configured to label the time-domain data <NUM> as invalid and to cancel further processing thereof, as well as to begin the procedure for recording a new iteration of time-domain data.

While multiple examples related to calculation thresholds have been explained in depth, the processing circuitry <NUM> may be configured to apply a variety of thresholds for ensuring that an accurate value of the breathing rate frequency and heart rate frequency is determined/estimated. Such thresholds may be applied in various combinations and at various stages of processing. As depicted in <FIG>, further examples of processing of the time-domain data <NUM> to improve accuracy and/or precision of the heart rate estimation, and possibly the breathing rate estimation, is the application of correlation functions <NUM> (to be discussed with reference to <FIG>) and using a recursive model of heart rate estimations <NUM> (to be discussed with reference to <FIG>).

<FIG> depicts an exemplary (original) frequency spectrum <NUM>-<NUM> (with a heart rate component) and an exemplary averaged-frequency-spectrum <NUM>-<NUM> (without a heart rate component) plotted together. They are each provided in the context of an exemplary non-correlated spectrum <NUM>-<NUM>. If an accurate value for the breathing rate frequency is selected, then the maximum amplitudes of the two spectra, as well the local maxima of the respective first and second harmonics, highly overlap, as shown in the non-correlated spectrum <NUM>-<NUM>. However, in order to be able to further increase the accuracy and/or precision of the heart rate estimation, both the (original) frequency spectrum <NUM>-<NUM> and the averaged-frequency-spectrum <NUM>-<NUM> may undergo a correlation operation.

More specifically, the processing circuitry <NUM> may be configured to perform a first correlation operation for the (original) frequency spectrum <NUM>-<NUM> to obtain a correlated (original) frequency spectrum (in <FIG> as <NUM>-3a) and to perform a second correlation operation for the averaged-frequency-spectrum <NUM>-<NUM> (in <FIG> as <NUM>-3b) to obtain a correlated averaged-frequency-spectrum (in <FIG> as <NUM>-<NUM>). Each correlation operation may be between an original version and a windowed version of the respective spectrum, wherein the respective windowed version is altered by applying a first Gaussian window centered on a first frequency corresponding to a maximum amplitude of the spectrum, applying a second Gaussian window centered on a second frequency that is two times the first frequency, applying a third Gaussian window centered on a third frequency that is three times the first frequency, and nullifying all amplitude values outside of the three Gaussian windows.

For example, in <FIG>, the (original) frequency spectrum <NUM>-<NUM> is depicted with its first, second, and third Gaussian windows. The first Gaussian window for the (original) frequency spectrum <NUM>-<NUM> is determined corresponding to the depicted vertical lines <NUM>-1a and <NUM>-1b. Each are located at a frequency corresponding to its immediately previous and immediately following local minima from the maximum amplitude, respectively. The second Gaussian window for the (original) frequency spectrum <NUM>-<NUM> is provided by the depicted vertical lines <NUM>-2a and <NUM>-2b, also by its immediately previous and immediately following local minima from the second highest local maximum within the (original) frequency spectrum <NUM>-<NUM>. The third Gaussian window for the (original) frequency spectrum <NUM>-<NUM> is provided by the depicted vertical lines <NUM>-3a and <NUM>-3b and are determined in an analogous fashion.

The first, second, and/or third Gaussian windows may also be determined by a different means, such as by a pre-specified frequency range from the respective maximum, which may be customized. While respective first, second, and third Gaussian windows of the averaged-frequency-spectrum <NUM>-<NUM> may also be used in its correlation operation, they are not depicted for the sake of clarity in <FIG>.

Once the first, second, and third Gaussian windows have been determined, they may be applied to generate a windowed (original) frequency spectrum <NUM>-<NUM>, depicted in <FIG>. The first correlation operation may be between the (original) frequency spectrum <NUM>-<NUM> and the windowed (original) frequency spectrum <NUM>-<NUM>. Generally, in a correlation operation, two data points from the two frequency spectra being correlated, respectively, may be multiplied and added to a recursive sum in a first correlation iteration of the correlation operation. This process may be repeated for a second iteration of the correlation operation, wherein one of the spectra is shifted with respect to the other spectrum to determine new sets of data points to be correlated, with new multiplication products determined and added to the recursive sum. Such iterations may be repeated according to further equivalent shifts to determine a first correlation spectrum <NUM>-3a, which may depict a result of the first correlation operation, performed between the (original) frequency spectrum <NUM>-<NUM> and the windowed (original) frequency spectrum <NUM>-<NUM>.

Furthermore, the above-described techniques may be repeated for the previously described second correlation operation. The second correlation operation may be between the averaged-frequency spectrum <NUM>-<NUM> and a windowed version of the averaged-frequency-spectrum (not depicted, analogous to <NUM>-<NUM>) that is analogous to the windowed version of the (original) frequency spectrum <NUM>-<NUM>. In other words, the averaged-frequency spectrum may also have a first, second, and third Gaussian window applied (not depicted, analogous to <NUM>-1a; <NUM>-1b; <NUM>-2a; <NUM>-2b; <NUM>-3a; <NUM>-3b), as previously described, to form its windowed version. The correlation operation may then be performed, also as previously described, to obtain a respective correlation spectrum <NUM>-3b.

<FIG> depicts an exemplary correlated spectrum <NUM>-<NUM>, including the exemplary correlated frequency spectrum <NUM>-3a and the exemplary correlated averaged-frequency-spectrum <NUM>-3b after respective correlation operations. Once the two correlated frequency spectra <NUM>-3a; <NUM>-3b are determined, they may comprise greater similarities within the windowed portions, while portions of the frequency spectra <NUM>-3a; <NUM>-3b outside of the windowed portions may be suppressed toward lower amplitude values, as can be seen with a comparison between the non-correlated spectra of <NUM>-<NUM> in <FIG> and the correlated spectra of <NUM>-<NUM> in <FIG>. The result of the correlated spectrum <NUM>-<NUM> shows that the correlated (original) frequency spectrum <NUM>-3a and the correlated averaged-frequency-spectrum <NUM>-3b overlap more precisely, which may enable canceling the respective breathing rate components more effectively in the difference spectrum. As such the heart rate component <NUM> in <FIG> may be determined more accurately.

<FIG> provides the same exemplary correlated spectrum <NUM>-<NUM> in <FIG>, but with a difference spectrum <NUM>-<NUM>. The difference spectrum <NUM>-<NUM> is determined by subtracting the averaged-frequency-spectrum <NUM>-<NUM> from the (original) frequency spectrum <NUM>-<NUM>. In particular, the processing circuitry <NUM> may be configured to subtract the correlated averaged-frequency-spectrum <NUM>-3b from the correlated (original) frequency spectrum <NUM>-3a to obtain the difference spectrum <NUM>-<NUM>, wherein the subtraction of one spectrum from another follows the previous definition provided for <FIG>. Since the respective correlated spectra emphasize the frequency components corresponding to the breathing rate frequency, as well as its first and second harmonic components, it may enable the difference spectrum <NUM>-<NUM> to be determined more accurately compared to not using the correlated spectra. Furthermore, the heart rate may be estimated as corresponding to a maximum amplitude <NUM>-<NUM> of the difference spectrum <NUM>-<NUM>, as depicted. The difference spectrum <NUM>-<NUM> is depicted with a zoomed-in scale and shows the heart rate component at a frequency of the heart rate (shown with greater precision to be approximately <NUM> beats per minute). This is depicted by the heart component <NUM> in <FIG>.

Additionally, the processing circuitry <NUM> may be configured to apply a window to the difference spectrum <NUM>-<NUM>, and to search for the maximum amplitude <NUM>-<NUM> within the window of the difference spectrum <NUM>-<NUM> for estimation of the heart rate frequency. The window of the difference spectrum <NUM>-<NUM> may specify a frequency range corresponding to a range of possible heart rates of the target, which may also be customized (e.g., from a user input) according to the target in the field of view of the radar sensor.

The processing circuitry <NUM> may also be configured to record multiple heart rate estimations within a single recording iteration or briefly separated recording iterations. For example, given a time window of <NUM> seconds or a similar value (e.g., <NUM> to <NUM> seconds) for a measurement iteration, approximately <NUM> to <NUM> estimations for the heart rate of the target may be performed within one minute of time. If the target is adequately still with minimal random body movement, then all estimations of the respective measurement iterations may be determined to be valid. Alternatively, if too much random body movement has occurred in a time window, thereby preventing a measurement of valid data for the heart rate estimation, the time-domain data corresponding to that particular time window may be discarded and further measurement iterations may continue. For example, the processing circuitry <NUM> may be configured to stop measuring and immediately begin a new measurement iteration (e.g. of <NUM> seconds) if a threshold related to random body movement has been surpassed, which may include the checks and corresponding thresholds described in <FIG>.

In particular, the processing circuitry <NUM> may be configured to perform multiple heart rate estimations, wherein each estimation is applied to an iteration of a recursive model for obtaining a refined heart rate estimate. For example, a first estimated value for the heart rate of the target may be based on a first heart rate estimate with a first range of error. Given a second heart rate estimate of the same target immediately following the first heart rate estimate, wherein both estimates are based on respective time-domain data <NUM> under the same circumstances, the second heart rate may increase confidence of the previous estimate. Furthermore, the recursive model may determine that the heart rate of the target is a value within a smaller range of error.

The recursive model may continue to be applied, wherein further, e.g., five or more, estimated values for the heart rate of the target may be given. Based on each successive estimation, the recursive model may determine an increased probability that the heart rate of the target is within a narrowed range. Generally, such a narrowed range may be part of the refined heart rate estimate, wherein the final estimate is considered to be more accurate and/or precise.

In addition to a refined heart rate estimate, the apparatus <NUM> may also to be used to increase confidence in a breathing rate estimate (i.e. breathing rate determination). Based on many possible checks applying the breathing rate frequency and its first and second harmonics, a higher confidence in the breathing rate may be obtained. Furthermore, other possible checks (e.g., checks related to the heart rate), which may not even be possible without an accurate value of the breathing rate, may also be done and verified, further increasing confidence in the breathing rate.

In general, the apparatus <NUM>, used in accordance with one of the various above-described configurations may provide a particular benefit of long-term monitoring biological signs of the same target. In the particular case of a newborn baby, heart rates and breathing rates may be measured and recorded multiple times and performed over many different days (or longer time spans), which may provide caregivers with more reliable information related to biological activity and overall health of the newborn baby. Such a benefit is also possible for older children and adults. A further benefit may be to better understand patterns of such biological activity in a variety of target types (e.g., human babies or adults, or also animals) and how they may be affected, particularly if other events occurring within an associated time span are also recorded. Generally, the apparatus <NUM> provides a new means for examining biological activity and health by a new technique using radar systems.

<FIG> illustrates an example of a radar system <NUM> comprising an apparatus <NUM> as described herein, such as apparatus <NUM>, and a radar sensor <NUM>, configured to generate the receive signal. The radar sensor <NUM> may, for instance, be an FMCW (Frequency-Modulated Continuous Wave) radar sensor.

Although the apparatus <NUM> and the radar sensor <NUM> are depicted as separate blocks in <FIG>, in other examples, the apparatus <NUM> may in part or in entirety be included in the radar sensor <NUM>. Thus, the radar sensor <NUM> may comprise the apparatus <NUM>. In case the apparatus <NUM> is only partially included in the radar sensor <NUM>, the radar system <NUM> may perform distributed processing carrying out respective parts of processing steps. In case the apparatus <NUM> is integrated in the radar sensor <NUM>, the control circuitry and the radar sensor <NUM> may be jointly integrated (embedded) in a single semiconductor chip, or in more than one semi-conductor chip.

More details and aspects of the radar system <NUM> are explained in connection with the proposed technique or one or more examples described above, e.g., with reference to <FIG>. The radar system <NUM> may comprise one or more additional optional features corresponding to one or more aspects of the proposed technique, or one or more examples described above.

<FIG> illustrates a flowchart of an example of a (e.g., computer-implemented) method <NUM>. The method <NUM> may be performed by an apparatus described herein, such as apparatus <NUM>. The method <NUM> comprises determining a frequency spectrum <NUM> corresponding to time-domain data based on a receive signal. The method <NUM> further comprises determining a breathing rate frequency <NUM> based on a maximum amplitude of the frequency spectrum. Furthermore, the method <NUM> comprises determining a plurality of section-frequency-spectra <NUM>, each corresponding to a respective section of the time-domain data, based on the breathing rate frequency, and determining an average <NUM> of the section-frequency-spectra to obtain an averaged-frequency-spectrum. The method further comprises subtracting <NUM> the averaged-frequency-spectrum from the frequency spectrum to obtain a difference spectrum and estimating <NUM> a heart rate of the target based on a maximum amplitude of the difference spectrum.

More details and aspects of the method <NUM> are explained in connection with the proposed technique or one or more examples described above, e.g., with reference to <FIG>. The method <NUM> may comprise one or more additional optional features corresponding to one or more aspects of the proposed technique, or one or more examples described above.

Furthermore, in further examples, a single step, function, process or operation may include and/or be broken up into several sub-steps, - functions, -processes or -operations.

Claim 1:
An apparatus (<NUM>) for measuring a heart rate of a target in a field of view of a radar sensor, the apparatus (<NUM>) comprising processing circuitry (<NUM>) configured to:
determine a frequency spectrum (<NUM>-<NUM>) corresponding to time-domain data (<NUM>) based on a receive signal generated by the radar sensor;
determine a breathing rate frequency based on a maximum amplitude (<NUM>-<NUM>) of the frequency spectrum (<NUM>-<NUM>);
determine a plurality of section-frequency-spectra, each corresponding to a respective section of the time-domain data (<NUM>), based on the breathing rate frequency;
determine an average of the section-frequency-spectra to obtain an averaged-frequency-spectrum (<NUM>-<NUM>);
subtract the averaged-frequency-spectrum (<NUM>-<NUM>) from the frequency spectrum (<NUM>-<NUM>) to obtain a difference spectrum (<NUM>-<NUM>); and
estimate a heart rate of the target based on a maximum amplitude (<NUM>-<NUM>) of the difference spectrum (<NUM>-<NUM>).