A score indicating the user's mindfulness

Abstract

A computer-implemented method for determining a score indicative of a user's mindfulness is provided. The method includes acquiring heart rate variability data of a user during a guided breathing exercise in which the user inhales and exhales to mimic breathing rates associated with mindfulness. The method includes filtering the heart rate variability data to generate filtered heart rate variability data. The method includes determining a first standard deviation of heart rate intervals included in a first segment of the filtered heart rate variability data spanning a discrete time interval and indicative of respiratory sinus arrhythmia. The method includes determining a second standard deviation of all heart rate intervals included in the first segment of the filtered heart rate variability data. The method includes determining a score indicative of the user's mindfulness based on the first standard deviation and the second standard deviation.

Description

[Technical Field]

[0001] Priority claims This application claims priority to U.S. patent application Ser. No. 63 / 395,594, entitled "Score Indicator of Mindfulness of a User," having a filing date of August 5, 2022, which is incorporated herein by reference.

[0002] The present disclosure relates generally to wearable computing devices, and more particularly to a method for determining a score indicative of a user's mindfulness during a meditation session based on biometric data obtained from a wearable computing device worn by the user. [Background technology]

[0003] Guided breathing exercises can be useful for managing stress. For example, a wearable computing device that can be worn on a user's wrist can include a software application (e.g., an app) that can facilitate guided breathing exercises. For example, the software application can cause the wearable computing device to display a notification prompting the user to breathe in and out at a pace that promotes mindfulness (e.g., meditation). Summary of the Invention

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the description that follows, or may be learned from the description, or may be learned by practice of the embodiments.

[0005] In one aspect, a computer-implemented method for determining a score indicative of mindfulness of a user performing a guided breathing exercise is provided. The method includes acquiring heart rate variability data of a user performing a guided breathing exercise. The method includes filtering the heart rate variability data to generate filtered heart rate variability data. The method includes determining a first standard deviation of heart rate intervals indicative of respiratory sinus arrhythmia and included in a first segment spanning a discrete time interval of the filtered heart rate variability data. The method includes determining a second standard deviation of all heart rate intervals included in the first segment of the filtered heart rate variability data. The method includes determining a score indicative of the user's mindfulness during the discrete time interval based at least in part on the first standard deviation and the second standard deviation.

[0006] In some implementations, the method includes causing a display screen of a wearable computing device worn by the user to display a score indicative of the user's mindfulness during the discrete time intervals.

[0007] In some embodiments, determining a score indicative of the user's mindfulness during the discrete time interval includes determining the score based on a ratio of a first standard deviation of heart rate intervals indicative of respiratory sinus arrhythmia and included in the first segment of filtered heart rate variability data to a second standard deviation of all heart rate intervals included in the first segment of filtered heart rate variability data.

[0008] In some embodiments, determining a first standard deviation of cardiac intervals indicative of respiratory sinus arrhythmia includes applying a Fourier transform to a first segment of the filtered heart rate variability data to obtain a normalized power spectral density function, and determining the first standard deviation further includes determining a peak amplitude of the normalized power spectral density function, fitting a Gaussian function to the peak amplitude, and determining the first standard deviation based at least in part on the Gaussian function.

[0009] In some implementations, determining the first standard deviation based at least in part on a Gaussian function includes integrating the Gaussian function over discrete time intervals to determine an area under the Gaussian function.

[0010] In some embodiments, the method includes causing a display screen of a wearable computing device worn by the user to display a score indicative of the user's mindfulness for the discrete time intervals and the user's breathing rate for the discrete time intervals.

[0011] In some implementations, filtering the heart rate data includes applying a low pass filter to the heart rate variability data.

[0012] In some embodiments, the discrete time interval is about 2 minutes. In some embodiments, the score indicative of the user's mindfulness during the discrete time interval ranges from 0 to 100. Further, in such embodiments, if the score is greater than a first number, the score indicates that the user was mindful during the discrete time interval. Further, if the score is less than a second number that is less than the first number, the score indicates that the user was stressed during the discrete time interval.

[0013] In another aspect, a wearable computing device is provided. The wearable computing device includes one or more biometric sensors. The wearable computing device further includes one or more processors. The one or more processors are configured to acquire, via the one or more biometric sensors, heart rate variability data of a user wearing the wearable computing device and performing a guided breathing exercise. The one or more processors are configured to filter the heart rate variability data. The one or more processors are configured to determine a first standard deviation of heart rate intervals indicative of respiratory sinus arrhythmia and included in a first segment of the filtered heart rate variability data. The one or more processors are configured to determine a second standard deviation of all heart rate intervals included in the first segment of the filtered heart rate variability data. The method includes determining a score indicative of the user's mindfulness during the discrete time interval based at least in part on the first standard deviation and the second standard deviation.

[0014] In some implementations, the one or more processors are configured to determine the user's mindfulness during the discrete time interval based at least in part on a ratio of the first standard deviation to the second standard deviation.

[0015] In some implementations, the one or more processors are further configured to cause the display screen to display a score indicative of the user's mindfulness during the discrete time intervals.

[0016] In some embodiments, to determine the first standard deviation, the one or more processors are configured to apply a Fourier transform to the first segment of the filtered heart rate variability data to obtain a normalized power spectral density function. The one or more processors are further configured to determine a peak amplitude of the normalized power spectral density function, fit a Gaussian function to the peak amplitude, and determine the first standard deviation based at least in part on the Gaussian function.

[0017] In some implementations, to determine the first standard deviation, the one or more processors are configured to integrate a Gaussian function over discrete time intervals to determine an area under the Gaussian function.

[0018] In some embodiments, the one or more processors are configured to cause the display screen to display a score indicative of the user's mindfulness during the discrete time intervals and to display the user's breathing rate during the discrete time intervals.

[0019] In some implementations, the one or more processors are configured to apply a low pass filter to the heart rate variability data to obtain filtered heart rate variability data.

[0020] In some embodiments, the score indicative of the user's mindfulness during the discrete time interval ranges from 0 to 100. Further, in such embodiments, if the score is greater than a first number, the score indicates that the user was mindful during the discrete time interval. Further, if the score is less than a second number that is less than the first number, the score indicates that the user was stressed during the discrete time interval.

[0021] In yet another aspect, a computer-implemented method for assessing the mindfulness of a user performing a guided breathing exercise is provided. The method includes acquiring heart rate variability data of the user performing the guided breathing exercise. The method includes determining a first standard deviation of heart rate intervals indicative of respiratory sinus arrhythmia and included in a first segment of the heart rate variability data over a discrete time interval. The method includes determining a second standard deviation of all heart rate intervals included in the first segment of the heart rate variability data. The method includes determining a score indicative of the user's mindfulness during the discrete time interval based at least in part on the first standard deviation and the second standard deviation.

[0022] In some embodiments, determining a score indicative of the user's mindfulness during the discrete time interval includes determining a score based on a ratio of a first standard deviation of heart rate intervals indicative of respiratory sinus arrhythmia and included in the first segment of heart rate variability data to a second standard deviation of all heart rate intervals included in the first segment of filtered heart rate variability data.

[0023] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following detailed description and the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the detailed description, serve to explain associated principles.

[0024] Detailed descriptions of embodiments directed to those skilled in the art are set forth herein with reference to the accompanying drawings. [Brief explanation of the drawings]

[0025] [Figure 1] 1 illustrates a wearable electronic device according to an embodiment of the present disclosure. [Figure 2] FIG. 1 illustrates a rear perspective view of a wearable electronic device according to an embodiment of the present disclosure. [Figure 3A] 1 illustrates a graphical representation of a user's heart rate while the user is under stress, according to an embodiment of the present disclosure. [Figure 3B] 1 illustrates a graphical representation of a user's heart rate while the user is engaged in meditation, according to an embodiment of the present disclosure. [Figure 4] 1 illustrates a flow diagram of a method for determining a score indicative of a user's mindfulness, according to an embodiment of the present disclosure. [Figure 5] 1 illustrates a graphical representation of a user's heart rate variability data over a time interval, according to an embodiment of the present disclosure. [Figure 6] 6 shows a graphical representation of the power spectral density of the heart rate variability data of FIG. 5 in accordance with an embodiment of the present disclosure. [Figure 7A] The ABI or scores for each of the different groups during meditation and at rest are shown. [Figure 7B] The root mean square of successive differences (RMSSD) for each of the different groups during meditation and at rest are shown. [Figure 7C] Heart rates for each of the different groups during meditation and at rest are shown. [Figure 7D] 1 shows the breathing rate for each of the different groups during meditation and at rest. [Figure 8] 1 illustrates devices capable of communicating with each other, according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0026] Reference will now be made in detail to the embodiments of the present disclosure, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the disclosure, and not as a limitation thereof. Indeed, it will be apparent to those skilled in the art that various modifications and variations can be made in the present disclosure without departing from the scope or spirit of the disclosure. For example, features illustrated or described as part of one embodiment can be used with other embodiments to yield still further embodiments. It is therefore intended that the present invention cover modifications and variations of the present disclosure that come within the scope of the appended claims and their equivalents.

[0027] An exemplary aspect of the present disclosure is directed to a wearable computing device. The wearable computing device may be worn, for example, on a user's wrist. The wearable computing device may include one or more biometric sensors (e.g., optical sensors). For example, the one or more biometric sensors may be configured to acquire heart rate variability (HRV) data of a user wearing the wearable computing device. Additionally, the wearable computing device may be configured to prompt the user to perform a guided breathing exercise. For example, the wearable computing device may display a notification prompting the user to inhale for a predetermined time and then exhale for a predetermined time so that the user reaches a breathing rate (e.g., approximately three breaths per minute) required to achieve "mindfulness" during the guided breathing exercise. As used herein, the term "mindfulness" refers to a state in which the parasympathetic branch of the user's autonomic nervous system (ANS) dominates over the sympathetic branch of the user's ANS. It should be understood that breathing at a desired breathing rate required to achieve mindfulness results in the user's heart rate varying rhythmically at the breathing frequency.

[0028] An exemplary aspect of the present disclosure is directed to determining an autonomic balance index (ABI) or score indicative of a user's mindfulness during a guided breathing exercise. The score can be determined from HRV data acquired via one or more biometric sensors of a wearable computing device worn by the user during the guided breathing exercise. In some embodiments, the HRV data from which the score is determined can be preprocessed to remove noise therefrom. For example, in some embodiments, the HRV data can be low-pass filtered (e.g., using a median filter). It should be appreciated that data points in the HRV data that are a predetermined number of standard deviations (e.g., approximately 3) from the length of the low-pass filter can be labeled as outliers and removed from the filtered HRV data. It should also be appreciated that the HRV data can be iteratively low-pass filtered until all data points comprising the filtered HRV data are less than a predetermined number of standard deviations from the length of the low-pass filter.

[0029] The filtered HRV data may be divided into multiple segments, each of which may span a discrete time interval during which the user's breathing rate may be considered substantially constant. For example, in some embodiments, the discrete time interval may be approximately two minutes.

[0030] In some embodiments, a score indicative of the user's mindfulness can be determined for each of multiple segments of the filtered HRV data. In this manner, a score indicative of the user's mindfulness can be determined for each successive discrete time interval (e.g., approximately 2 minutes). As used herein, the term "about" when used in conjunction with a stated value refers to a range of values within 10% of the stated value.

[0031] In some embodiments, the first segment of filtered HRV data may be passed through a smoothing filter (e.g., a Hann filter). Alternatively, or additionally, a Fourier transform may be performed on the first segment of filtered HRV data to generate a power spectral density (PSD) of the first segment of filtered HRV data. From the PSD of the first segment of filtered HRV data, the peak amplitude of the PSD may be determined. The peak amplitude of the PSD may correspond to a respiratory frequency associated with respiratory sinus arrhythmia, which is a reliable measure of the parasympathetic branch of the autonomic nervous system.

[0032] In some embodiments, a Gaussian function may be fitted to the peak amplitude of the PSD. Furthermore, the area under the Gaussian function may indicate the standard deviation of the beat-to-beat intervals included in the first segment of filtered HRV data indicative of respiratory sinus arrhythmia. Thus, the Gaussian function may be integrated over discrete time intervals to determine the standard deviation of the beat-to-beat intervals included in the first segment of filtered HRV data indicative of respiratory sinus arrhythmia.

[0033] A score indicative of the user's mindfulness during a time interval (e.g., 2 minutes) associated with a first segment of filtered HRV data can be determined based, at least in part, on a ratio of the standard deviation of the heartbeat intervals included in the first segment of filtered HRV data that indicate respiratory sinus arrhythmia to the standard deviation of all heartbeat intervals included in the first segment of filtered HRV data. For example, the score can include a numerical value ranging from 0 to 100. If the score exceeds a first numerical value (e.g., approximately 75), the score may indicate that the user achieved mindfulness during the discrete time interval. Conversely, if the score is less than a second numerical value (e.g., approximately 50) that is less than the first numerical value, the score may indicate that the user was stressed during the discrete time interval and therefore did not achieve mindfulness. In some embodiments, if the score is greater than or equal to a second numerical value (e.g., approximately 50) but less than the first numerical value (e.g., approximately 75), the score may indicate that the user is asleep or in a distracted state and has entered a meditation zone.

[0034] In some embodiments, a score indicative of the user's mindfulness can be determined for each successive discrete time interval (e.g., approximately every two minutes) by processing each successive segment of filtered HRV data. In this way, it can be determined how many minutes of the guided breathing exercise were "mindful minutes" during which the user achieved mindfulness.

[0035] In some embodiments, a score indicative of mindfulness may be displayed on the display screen of the wearable computing device in real time or near real time while the user is performing the guided breathing exercise. In this way, the user can receive real time or near real time feedback as to whether the user is achieving mindfulness during the guided breathing exercise. Additionally, in some embodiments, the user's breathing rate may be displayed on the display screen of the wearable computing device. In this way, the user can know whether their breathing rate is too fast, preventing them from achieving mindfulness during the guided breathing exercise.

[0036] In some embodiments, a score indicating the user's mindfulness at each successive discrete time interval (e.g., approximately every two minutes) may be stored in one or more memory devices. In this manner, the user may access the scores stored in the one or more memory devices to monitor the user's mindfulness throughout the guided breathing exercise. For example, the user may gain insight into how mindful the user was at various points (e.g., the beginning, middle, and end) of the guided breathing exercise.

[0037] In some embodiments, scores indicative of mindfulness for each different segment of filtered heart rate variability data may be stored locally on the wearable electronic device. In this way, a user may access the scores without needing a network connection (e.g., an internet connection). In alternative embodiments, the one or more memory devices may be located on a device (e.g., a server, a mobile computing device, etc.) that is remote to the wearable electronic device. In this way, memory on the wearable electronic device may be conserved.

[0038] Exemplary aspects of the present disclosure provide numerous technical effects and advantages. For example, filtering heart rate variability data can remove noise associated with heart rate variability obtained from sensors (e.g., electrodes) on a wearable electronic device, thereby improving the accuracy of a score indicative of a user's mindfulness. Thus, the score can be a more reliable measure of how mindful a user is during a guided breathing exercise. Furthermore, displaying the user's respiration rate along with the score can provide further insight into the user's score for a particular segment of filtered heart rate data. For example, the respiration rate may be high when the score indicative of mindfulness is low. Thus, a user can see that a low score for a particular segment is at least in part due to the user breathing faster than the respiration rate at which the user can achieve mindfulness.

[0039] 1 and 2 illustrate a wearable electronic device 100 according to some embodiments of the present disclosure. The wearable electronic device 100 can be worn, for example, on a user's arm (e.g., wrist). The wearable electronic device 100 can include a housing 110 and one or more electronic components (e.g., disposed on a printed circuit board) disposed within an interior cavity defined by the housing 110. Additionally, the wearable electronic device 100 can include a battery (not shown) disposed within the interior cavity of the housing 110.

[0040] In some embodiments, the wearable electronic device 100 may include a first band 120 coupled to the housing 110 at a first location and a second band 122 coupled to the housing 110 at a second location. The first band 120 and the second band 122 may be coupled to one another to secure the housing 110 to a user's arm. For example, the first band 120 may include a buckle or clasp (not shown). Additionally, the second band 122 may define a plurality of openings 124 spaced apart along the length of the second band 122. In such embodiments, a protrusion of a buckle associated with the first band 120 may extend through one of the plurality of openings defined by the second band 122 to couple the first band 120 to the second band 122.

[0041] It should be appreciated that any suitable type of fastener may be used to couple first band 120 to second band 122. For example, in some embodiments, first band 120 and second band 122 may include magnets. In such embodiments, first band 120 and second band 122 may be magnetically coupled to one another to secure housing 110 to user's arm 102.

[0042] In some implementations, wearable electronic device 100 may include a display 130 configured to display content for a user to view (e.g., time, date, biometric information, notifications, etc.). For example, display 130 may include a plurality of pixels. In some embodiments, display 130 may include an organic light-emitting diode (OLED) display. However, it should be understood that display 130 may include any suitable type of display.

[0043] The wearable electronic device 100 may include a first electrode 140 and a second electrode 142. It should be understood that the wearable electronic device 100 may include more or fewer electrodes. As shown, the first electrode 140 and the second electrode 142 may be disposed on a lower portion (e.g., the side facing the wrist) of the housing 110 in some embodiments. More specifically, the electrodes 140, 142 may be disposed within respective openings (e.g., cutouts) defined by the lower portion of the housing 110. In this manner, the first electrode 140 and the second electrode 142 may each contact (e.g., touch) the user's skin (e.g., wrist) when the user is wearing the wearable electronic device 100. Furthermore, because the electrodes 140 and 142 contact the user's skin, the electrodes 140 and 142 may be used to measure one or more biometrics of the user (e.g., electrodermal activity, electrocardiogram).

[0044] In some implementations, the wearable electronic device 100 may include one or more optical sensors (e.g., photoplethysmography (PPG) sensors) disposed within an interior cavity defined by the housing 110 in some embodiments. The optical sensor(s) may be configured to emit an optical signal (e.g., infrared light) that passes through the transparent portion 114 of the housing 110. For example, in some embodiments, the transparent portion 114 of the housing 110 may be transparent and may include one or more windows disposed at different locations (e.g., cutouts) on the housing 110. It should be understood that the optical signal exits the interior cavity of the housing 110 through the transparent portion 114 of the housing 110, penetrates the user's skin and blood vessels, and returns to the optical sensor(s) as a reflected optical signal. It should be understood that using the disclosure provided herein, one or more biometrics associated with a user may be determined based, at least in part, on the reflected optical signal.

[0045] 3A and 3B, graphical representations of a user's heart rate are provided, according to embodiments of the present disclosure. More specifically, FIG. 3A shows a time-varying signal 200 indicating a user's heart rate over a period (e.g., 300 seconds) while the user is experiencing stress. Conversely, FIG. 3B shows a time-varying signal 202 indicating a user's heart rate as a function of time while the user is meditating (e.g., performing a guided breathing exercise). As shown, the user's heart rate during meditation exhibits consistent rhythmic fluctuations required to achieve mindfulness and thereby reduce stress. As described below, exemplary aspects of the present disclosure are directed to determining a score indicative of a user's mindfulness during a meditation session (e.g., a guided breathing exercise) to provide insight into the effectiveness of the meditation session in reducing the user's stress.

[0046] Referring now to FIG. 4, a flow diagram of an exemplary method 300 for determining a score indicative of a user's mindfulness during a guided breathing exercise is provided. Method 300 may be performed, for example, by one or more processors. In some embodiments, the one or more processors may be part of the wearable computing device described above with reference to FIGS. 1 and 2. In alternative embodiments, the one or more processors may be distributed between the wearable computing device and a computing device that is separate from the wearable computing device and communicatively coupled to the wearable computing device via a wireless network. FIG. 3 shows steps performed in a particular order for purposes of illustration and description. One skilled in the art, with the disclosure provided herein, will understand that the various steps of method 300, or any of the other methods disclosed herein, may be adapted, modified, rearranged, performed simultaneously, or modified in various ways without departing from the scope of the present disclosure.

[0047] At (302), method 300 may include acquiring heart rate variability data of a user while the user performs a guided breathing exercise in which the user breathes in for a predetermined time and exhales for a predetermined time to mimic a breathing rate associated with mindfulness. For example, the heart rate variability data may be determined based at least in part on data acquired via one or more biometric sensors (e.g., optical sensors) of the wearable computing device.

[0048] At 304, method 300 may include filtering the heart rate variability data to generate filtered heart rate variability data. For example, in some embodiments, one or more processors may low-pass filter the heart rate variability data obtained at 202 to generate the filtered heart rate variability data. In some embodiments, the heart rate variability may be passed through a median filter of length n. In such embodiments, data points in the filtered heart rate variability data that are greater than n standard deviations from the mean of the median filter may be flagged as outliers and removed from the filtered heart rate variability data. In some embodiments, multiple iterations of filtering the heart rate variability data may be performed until all data points in the filtered heart rate variability data are less than n standard deviations from the mean of the median filter.

[0049] At 306, method 300 may include dividing the heart rate variability data into a plurality of segments. Each of the plurality of segments may span a discrete time interval. For example, the discrete time interval may correspond to a time during which the user's breathing rate is substantially constant. In some embodiments, the discrete time interval may be approximately two minutes.

[0050] At 308, the method 300 may include determining a first standard deviation of cardiac intervals included in a first segment of the plurality of segments and indicative of respiratory sinus arrhythmia. For example, a Fourier transform may be performed on the first segment of filtered HRV data to generate a power spectral density (PSD) of the first segment of filtered HRV data. From the PSD of the first segment of filtered HRV data, a peak amplitude of the PSD may be determined. The peak amplitude of the PSD may correspond to a respiratory frequency associated with respiratory sinus arrhythmia, which is a reliable measure of the parasympathetic branch of the autonomic nervous system.

[0051] In some embodiments, a Gaussian function may be fitted to the peak amplitude of the PSD. Furthermore, the area under the Gaussian function may indicate the standard deviation of the beat-to-beat intervals included in the first segment of filtered HRV data indicative of respiratory sinus arrhythmia. Thus, the Gaussian function may be integrated to determine the standard deviation of the beat-to-beat intervals included in the first segment of filtered HRV data indicative of respiratory sinus arrhythmia.

[0052] At 310, the method 300 may include determining a second standard deviation of all heart beat intervals included in the first segment of HRV data.

[0053] At 312, method 300 may include determining a score indicative of the user's mindfulness during a time interval (e.g., 2 minutes) associated with the first segment of filtered HRV data. The score may be determined based at least in part on the standard deviation of the heart beat intervals included in the first segment of filtered HRV data indicative of respiratory sinus arrhythmia and the standard deviation of all heart beat intervals included in the first segment of filtered HRV data, e.g., based on the ratio of the standard deviation of the heart beat intervals included in the first segment of filtered HRV data indicative of respiratory sinus arrhythmia to the standard deviation of all heart beat intervals included in the first segment of filtered HRV data. For example, the score may include a numerical value ranging from 0 to 100. If the score is greater than a first numerical value (e.g., approximately 75), the score may indicate that the user achieved mindfulness during the time interval. Conversely, if the score is less than a second numerical value (e.g., approximately 50) that is less than the first numerical value, the score may indicate that the user was stressed and therefore did not achieve mindfulness. In some embodiments, if the score is greater than or equal to the second number (e.g., about 50) but less than the first number (e.g., about 75), the score may indicate that the user is asleep or has entered the meditation zone in a distracted state.

[0054] At (314), method 300 may include displaying the score determined at (212) on a display screen of a wearable computing device worn by the user performing the guided breathing exercise. For example, display 130 of wearable computing device 100 described above with reference to FIG. 1 is shown displaying a score of 87, indicating that the user is achieving mindfulness while performing the guided breathing exercise. Additionally, display 130 of wearable computing device 100 is shown displaying the user's breathing rate (i.e., 6.1 breaths per minute). In doing so, the user may better understand the relationship between breathing rate and mindfulness.

[0055] In some embodiments, a score indicative of the user's mindfulness may be determined for each successive time interval (e.g., approximately every two minutes) by processing each successive segment of filtered HRV data. In such embodiments, the score displayed at (214) may be updated to reflect the most recent score indicative of mindfulness.

[0056] 5, a graphical representation of a segment of filtered HRV data for a user is provided, in accordance with an embodiment of the present disclosure. As shown, the segment of filtered HRV data spans a time interval during which the user's breathing rate is substantially constant (i.e., two minutes). It should be understood that the segment of filtered HRV includes the beat-to-beat intervals detected over that time interval.

[0057] Referring now to FIG. 6, a graphical representation of the power spectral density of the filtered HRV of FIG. 5 is provided, in accordance with an embodiment of the present disclosure. As shown, the peak amplitude Ao of the power spectral density indicates the user's respiratory rate. Furthermore, as described above with reference to FIG. 4, a Gaussian function (shown by the dashed line) can be fitted to the peak amplitude Ao, and the integral of the Gaussian function can be taken to determine the standard deviation of the beat-to-beat intervals in a segment of HRV data indicative of respiratory sinus arrhythmia. For example, the area under the Gaussian function can indicate the standard deviation of the beat-to-beat intervals in a segment of HRV data indicative of respiratory sinus arrhythmia. Optionally, the integral of the Gaussian function can be taken to determine the standard deviation of the beat-to-beat intervals indicative of respiratory sinus arrhythmia.

[0058] In some embodiments, one or more processors performing the method described above with reference to FIG. 4 can be configured to determine whether the peak amplitude Ao of the power spectral density is actually a peak. For example, the one or more processors can be configured to subtract a fitted Gaussian function from the power spectral density function to obtain a residual function. The one or more processors can then determine the largest peak in the residual function in a frequency range that includes the frequency of the peak amplitude Ao of the power spectral density function. The one or more processors can be configured to determine the ratio of the peak amplitude Ao of the power spectral density function to the peak amplitude of the residual function. If the ratio exceeds a threshold, the peak amplitude Ao of the power spectral density function is considered valid. Otherwise, the segment of HRV data is discarded because the respiration rate is not clearly defined.

[0059] 7A-7D, graphical representations of the effectiveness of different metrics in determining mindfulness for multiple different groups or breathing patterns during both meditation and rest are provided, according to embodiments of the present disclosure. FIG. 7A shows the ABI or score for each of the different groups during meditation and rest. FIG. 7B shows the root mean square of successive differences (RMSSD) for each of the different groups during meditation and rest. FIG. 7C shows the heart rate for each of the different groups during meditation and rest. FIG. 7D shows the respiration rate for each of the different groups during meditation and rest. As can be seen, the ABI (FIG. 7A) and respiration rate (FIG. 7D) are more sensitive than the RMSSD (FIG. 7B) and heart rate (FIG. 7C).

[0060] 8, components of an exemplary computing system 400 of the wearable computing device 100 that can be utilized in accordance with various embodiments are illustrated. Notably, as shown, the computing system 400 may also include at least one controller 402. Furthermore, in one embodiment, the controller(s) 402 may be a central processing unit (CPU) or a graphics processing unit (GPU) for executing instructions that may be stored in a memory device 404, such as flash memory or DRAM, among other options. For example, in one embodiment, the memory device 404 may include RAM, ROM, FLASH memory, or other non-transitory digital data storage, and may include a control program that includes sequences of instructions that, when loaded from the memory device 404 and executed using the controller(s) 402, cause the controller(s) 402 to perform the functions described herein.

[0061] Computing system 400 can include many types of memory, data storage, or computer-readable media, such as data storage for program instructions for execution by a controller or any suitable processor. The same or separate storage can be used for images or data, removable memory can be available for sharing information with other devices, and any number of communication approaches can be utilized for sharing with other devices. Additionally, as shown, computing system 400 includes display 130, which can be a touchscreen, organic light-emitting diode (OLED), or liquid crystal display (LCD). However, the device could communicate information through other means, such as through audio speakers, a projector, or by casting the display or streaming data to another device, such as a mobile phone, where an application on the mobile phone displays the data.

[0062] The computing system 400 may include one or more wireless network components 412 operable to communicate with one or more electronic devices within communication range of a particular wireless channel. The wireless channel may be any suitable channel used to allow devices to communicate wirelessly, such as a Bluetooth, cellular, NFC, ultra-wideband (UWB), or Wi-Fi channel. It should be understood that the computing system 400 may have one or more conventional wired communication connections known in the art.

[0063] Computing system 400 also includes one or more energy storage devices 408 operable to be recharged via a conventional plug-in approach. In some embodiments, computing system 400 also includes at least one additional input / output device 410 capable of receiving conventional input from a user. This conventional input may include, for example, push buttons, a touchpad, a touchscreen, a wheel, a joystick, a keyboard, a mouse, a keypad, or any other such device or element by which a user can enter commands into computing system 400. In some embodiments, input / output device(s) 410 may also be connected by a wireless infrared or Bluetooth or other link. In some embodiments, computing system 400 may include a microphone or other audio capture element that accepts voice or other audio commands. In some embodiments, input / output device(s) 410 may include one or more electrodes, optical sensors, barometric pressure sensors (e.g., altimeters, etc.), etc.

[0064] The computing system 400 may include a driver 414 and at least some combination of one or more emitters 416 and multiple detectors 418 for measuring data of one or more metrics of a human body, such as a person wearing one or more wearable computing devices 100. In some embodiments, this may include at least one imaging element, such as one or more cameras capable of capturing images of the surrounding environment and imaging a user, people, or objects in the vicinity of the device. The image capture element may include any suitable technology, such as a CCD image capture element having sufficient resolution, focusing range, and viewing area to capture images of a user as the user operates the device. Additional image capture elements may also include depth sensors. Methods for capturing images using camera elements with computing devices are well known in the art and will not be described in detail herein. It should be understood that image capture may be performed using a single image, multiple images, periodic imaging, continuous image capture, image streaming, etc. Additionally, the computing system 400 may include the ability to start and / or stop image capture, for example, upon receiving a command from a user, an application, or another device.

[0065] The emitter 416 and detector 418 may also be used to obtain optical photoplethysmogram (PPG) measurements, in one example. Some PPG techniques rely on detecting light at a single spatial location or adding signals obtained from two or more spatial locations. Both of these approaches result in a single spatial measurement from which a heart rate (HR) estimate (or other physiological metric) can be determined. In some embodiments, the PPG device uses a single light source (i.e., a single optical path) coupled to a single detector. Alternatively, the PPG device may use multiple light sources coupled to a single detector or multiple detectors (i.e., two or more optical paths). In other embodiments, the PPG device uses multiple detectors coupled to a single light source or multiple light sources (i.e., two or more optical paths). In some cases, the light source(s) may be configured to emit one or more of green, red, infrared (IR) light, and any other suitable wavelengths in the spectrum (e.g., long IR for metabolic monitoring). For example, the PPG device may use a single light source and two or more photodetectors, each configured to detect a specific wavelength or range of wavelengths. In some cases, each detector is configured to detect a different wavelength or wavelength range from the others. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In still other cases, one or more detectors are configured to detect a particular wavelength or wavelength range from one or more other detectors. In embodiments using multiple optical paths, the PPG device may determine an average of signals resulting from the multiple optical paths before determining an HR estimate or other physiological metric.

[0066] Additionally, in one embodiment, the emitter 416 and the detector 418 may be coupled directly or indirectly to the controller 402 using driver circuits that enable the controller 402 to drive the emitter 416 and obtain signals from the detector 418. The host computer 422 may communicate with the wireless network component 412 via one or more networks 420, which may include one or more local area networks, wide area networks, UWB, and / or internetworks using either terrestrial or satellite links. In some embodiments, the host computer 422 executes control programs and / or applications.

[0067] While the subject matter of the present disclosure has been described in detail with reference to various specific exemplary embodiments thereof, each example is provided for purposes of explanation and not limitation of the present disclosure. Those skilled in the art, upon understanding the foregoing, will readily be able to make modifications, variations, and equivalents to such embodiments. Accordingly, the present disclosure does not exclude the inclusion of such modifications, variations, and / or additions to the subject matter as would be readily apparent to one skilled in the art. For example, features illustrated or described as part of one embodiment can be used with other embodiments to yield yet a further embodiment. Accordingly, the present disclosure is intended to cover such modifications, variations, and equivalents.

Claims

1. 1. A computer-implemented method for determining the mindfulness of a user performing a guided breathing exercise, comprising: acquiring heart rate variability data of the user performing the guided breathing exercise; filtering the heart rate variability data to generate filtered heart rate variability data; determining a first standard deviation of cardiac intervals indicative of respiratory sinus arrhythmia within a first segment spanning a discrete time interval of the filtered heart rate variability data; determining a second standard deviation of all heart rate intervals included in the first segment of the filtered heart rate variability data; determining a score indicative of the user's mindfulness during the discrete time interval based at least in part on the first standard deviation relative to the second standard deviation; A computer-implemented method comprising:

2. The computer-implemented method of claim 1 , further comprising causing the score to be displayed on a display screen of a wearable computing device worn by the user.

3. 2. The computer-implemented method of claim 1, wherein determining the score indicative of the user's mindfulness during the discrete time interval comprises determining the score based at least in part on a ratio of the first standard deviation to the second standard deviation.

4. Determining the first standard deviation comprises: applying a Fourier transform to the first segment of the filtered heart rate variability data to obtain a normalized power spectral density function; determining a peak amplitude of the normalized power spectral density function; the peak amplitude being indicative of the user's breathing rate; Determining the first standard deviation further comprises: fitting a Gaussian function to the peak amplitude of the normalized power spectral density function; and determining the first standard deviation based at least in part on the Gaussian function.

5. displaying the score on a display screen of a wearable computing device worn by the user; causing the display screen of the wearable computing device to display the respiration rate of the user; The method of claim 4 further comprising:

6. 5. The computer-implemented method of claim 4, wherein determining the first standard deviation based at least in part on the Gaussian function comprises integrating the Gaussian function to determine an area under the Gaussian function.

7. The computer-implemented method of claim 1 , wherein filtering the heart rate variability data comprises applying a low pass filter to the heart rate variability data.

8. 2. The computer-implemented method of claim 1, wherein the discrete time interval is approximately two minutes.

9. if the score is greater than or equal to a first numerical value, the score indicates that the user is being mindful; The computer-implemented method of claim 1 , wherein if the score is less than a second numerical value that is less than the first numerical value, the score indicates that the user is under stress.

10. 1. A wearable computing device, comprising: one or more biometric sensors; one or more processors, wherein the one or more processors: acquiring heart rate variability data of a user wearing the wearable computing device and performing a guided breathing exercise via the one or more biometric sensors; filtering the heart rate variability data to generate filtered heart rate variability data; determining a first standard deviation of cardiac intervals indicative of respiratory sinus arrhythmia within a first segment spanning a discrete time interval of the filtered heart rate variability data; determining a second standard deviation of all heart rate intervals included in the first segment of the filtered heart rate variability data; determining a score indicative of the user's mindfulness during the discrete time interval based at least in part on the first standard deviation relative to the second standard deviation; 1. A wearable computing device configured to:

11. 11. The wearable computing device of claim 10, wherein to determine the score indicative of the user's mindfulness, the one or more processors are configured to determine the score indicative of the user's mindfulness during the discrete time interval based at least in part on a ratio of the first standard deviation to the second standard deviation.

12. The one or more processors further comprise: The wearable computing device of claim 10 , configured to cause a display screen to display the score indicative of the user's mindfulness during the discrete time interval.

13. To determine the first standard deviation, the one or more processors: applying a Fourier transform to the first segment of the heart rate variability data to obtain a normalized power spectral density function; determining a peak amplitude of the normalized power spectral density function; the peak amplitude being indicative of the user's breathing rate; The one or more processors further comprise: fitting a Gaussian function to the peak amplitude of the normalized power spectral density function; and determining the first standard deviation based at least in part on the Gaussian function.

14. The one or more processors further comprise:

14. The wearable computing device of claim 13, configured to cause a display screen to display the score indicative of the user's mindfulness during the discrete time interval and the user's breathing rate during the discrete time interval.

15. 14. The wearable computing device of claim 13, wherein to determine the first standard deviation, the one or more processors are configured to integrate the Gaussian function over the discrete time intervals to determine an area under the Gaussian function.

16. The wearable computing device of claim 10 , wherein to filter the heart rate variability data, the one or more processors are configured to apply a low pass filter to the heart rate variability data.

17. The wearable computing device of claim 10 , wherein the score indicating the user's mindfulness during the discrete time interval ranges from 0 to 100.

18. if the score is greater than or equal to a first numerical value, the score indicates that the user is being mindful; 17. The wearable computing device of claim 16, wherein if the score is less than a second number that is less than the first number, the score indicates that the user is under stress.

19. 1. A computer-implemented method for determining the mindfulness of a user performing a guided breathing exercise, comprising: acquiring heart rate variability data of the user performing the guided breathing exercise; determining a first standard deviation of cardiac intervals included in a first segment spanning a discrete time interval of the heart rate variability data and indicative of respiratory sinus arrhythmia; determining a second standard deviation of all heartbeat intervals included in the first segment of the heart rate variability data; determining a score indicative of the user's mindfulness during the discrete time interval based at least in part on the first standard deviation relative to the second standard deviation; A computer-implemented method comprising:

20. 20. The computer-implemented method of claim 19, wherein determining the score indicative of the user's mindfulness during the discrete time interval comprises determining a ratio of the first standard deviation to the second standard deviation.

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