Score indicating user's mind

Abstract

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

Description

Priority declaration

[0001] This application claims priority to U.S. patent application No. 63 / 395,594, filed on August 5, 2022, entitled “Score Indicative of Mindfulness of a User,” which is incorporated herein by reference. Technical Field

[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 meditative relaxation based on biometric data obtained from a wearable computing device worn by the user. Background Art

[0003] Guided breathing exercises can aid in the management of stress. A wearable computing device that can be worn, for example, 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 inhale and exhale at a rate that is conducive to 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 following description, or may be learned from the description, or may be learned through 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 obtaining 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 beat intervals indicative of respiratory sinus arrhythmia and included in a first segment of the filtered heart rate variability data spanning a discrete time interval. The method includes determining a second standard deviation of all heart beat intervals included in the first segment of the filtered heart rate variability data. The method includes determining a score indicative of mindfulness of the user over 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 over discrete time intervals.

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

[0008] In some implementations, determining a first standard deviation of heart beat intervals indicative of a 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. Additionally, determining the first standard deviation includes determining a peak magnitude of the normalized power spectral density function, fitting a Gaussian function to the peak magnitude, 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 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 over the discrete time interval and the user's breathing rate over the discrete time interval.

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

[0012] In some implementations, the discrete time interval is approximately 2 minutes.

[0013] In some implementations, the score indicating the user's mindfulness during the discrete time interval ranges from 0 to 100. Additionally, in such implementations, when the score is greater than a first value, the score indicates that the user is mindful during the discrete time interval. Additionally, when the score is less than a second value, the score indicates that the user is stressed during the discrete time interval, the second value being less than the first value.

[0014] 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 obtain, 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 beat intervals that are 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 beat intervals included in the first segment of the filtered heart rate variability data. The method includes determining a score indicating mindfulness of the user over a discrete time interval based at least in part on the first standard deviation and the second standard deviation.

[0015] In some implementations, the one or more processors are configured to determine the mindfulness of the user over the discrete time interval based at least in part on a ratio of the first standard deviation to the second standard deviation.

[0016] 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 over discrete time intervals.

[0017] In some implementations, 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.

[0018] In some implementations, to determine the first standard deviation, the one or more processors are configured to integrate the Gaussian function over discrete time intervals to determine an area under the Gaussian function.

[0019] In some implementations, the one or more processors are configured to cause the display screen to display a score indicating the user's mindfulness during the discrete time interval and to display the user's breathing rate during the discrete time interval.

[0020] 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.

[0021] In some implementations, the score indicating the user's mindfulness during the discrete time interval ranges from 0 to 100. Additionally, in such implementations, when the score is greater than a first value, the score indicates that the user is mindful during the discrete time interval. Additionally, when the score is less than a second value, the score indicates that the user is stressed during the discrete time interval, the second value being less than the first value.

[0022] In yet another aspect, a computer-implemented method for determining mindfulness of a user performing a guided breathing exercise is provided. The method includes obtaining heart rate variability data of a user performing a guided breathing exercise. The method includes determining a first standard deviation of heart beat intervals indicative of respiratory sinus arrhythmia and included in a first segment of the heart rate variability data spanning a discrete time interval. The method includes determining a second standard deviation of all heart beat intervals included in the first segment of the heart rate variability data. The method includes determining a score indicative of mindfulness of the user over the discrete time interval based at least in part on the first standard deviation and the second standard deviation.

[0023] In some implementations, determining a score indicating mindfulness of a user over discrete time intervals includes determining a score based on a ratio of a first standard deviation of heart beat intervals indicative of respiratory sinus arrhythmia and included in a first segment of heart rate variability data to a second standard deviation of all heart beat intervals included in the first segment of filtered heart rate variability data.

[0024] These and other features, aspects and advantages of various embodiments of the present disclosure will be better understood with reference to the following description and appended claims.The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the relevant principles. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] A detailed discussion of the embodiments for those skilled in the art is set forth in this specification with reference to the accompanying drawings, in which:

[0026] Figure 1 A wearable electronic device according to an embodiment of the present disclosure is depicted.

[0027] Figure 2 A rear perspective view of a wearable electronic device according to an embodiment of the present disclosure is depicted.

[0028] Figure 3A Depicted is a graphical representation of a user's heart rate when the user is stressed, in accordance with an embodiment of the present disclosure.

[0029] Figure 3B Depicted is a graphical representation of a user's heart rate while the user engages in meditation in accordance with an embodiment of the present disclosure.

[0030] Figure 4 Depicted is a flow chart of a method for determining a score indicative of a user's mindfulness in accordance with an embodiment of the present disclosure.

[0031] Figure 5 Depicted is a graphical representation of heart rate variability data for a user over a time interval in accordance with an embodiment of the present disclosure.

[0032] Figure 6 Describes an embodiment of the present disclosure Figure 5 Graphical representation of the power spectral density of heart rate variability data in .

[0033] Fig. 7A The ABI or scores of each of the different groups during meditation and at rest are depicted.

[0034] Figure 7B The root mean square difference (RMSSD) of the running values ​​during meditation and at rest is depicted for each of the different groups.

[0035] Figure 7C Heart rates during meditation and at rest for each of the different groups are depicted.

[0036] Fig.7D The breathing rate of each of the different groups during meditation and at rest is depicted.

[0037] Figure 8 Devices capable of communicating with each other according to embodiments of the present disclosure are depicted. DETAILED DESCRIPTION

[0038] Reference will now be made in detail to embodiments of the present disclosure, one or more examples of which are shown in the accompanying drawings. Each example is provided by way of explanation of the present disclosure rather than by way of limitation. In fact, it will be apparent to those skilled in the art that various modifications and variations may be made in the present disclosure without departing from the scope or spirit of the present disclosure. For example, a feature shown or described as part of one embodiment may be used together with another embodiment to produce yet further embodiments. Therefore, the present invention is intended to encompass such modifications and variations within the scope of the appended claims and their equivalents.

[0039] Example aspects of the present disclosure relate to a wearable computing device. The wearable computing device may be, for example, worn 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 obtain heart rate variability (HRV) data of a user wearing the wearable computing device. In addition, the wearable computing device may be configured to instruct 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 amount of time and then exhale for a predetermined amount of time to reach a breathing rate (e.g., approximately 3 breaths per minute) required for the user 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) is more dominant than the sympathetic branch of the user's ANS. It should be understood that breathing at the desired breathing rate required to achieve mindfulness causes rhythmic oscillations of the user's heart rate at the respiratory rate.

[0040] Example aspects of the present disclosure relate to determining an autonomic balance index (ABI) or score indicating the mindfulness of a user during a guided breathing exercise. The score can be determined from HRV data obtained via one or more biometric sensors of a wearable computing device worn by a user during the guided breathing exercise. In some embodiments, the HRV data can be preprocessed to remove noise therefrom, and the score is determined from the HRV data. For example, in some embodiments, the HRV data can be low-pass filtered (e.g., using a median filter). It should be understood 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 marked as outliers and removed from the filtered HRV data. In addition, it should be understood that the HRV data can be iteratively low-pass filtered until all data points constituting the filtered HRV data are less than a predetermined number of standard deviations from the length of the low-pass filter.

[0041] The filtered HRV data may be divided into a plurality of segments. In addition, each of the segments 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 2 minutes.

[0042] In some embodiments, a score indicative of the user's mindfulness may be determined for each of a plurality of segments of filtered HRV data. In this manner, a score indicative of the user's mindfulness may be determined for each consecutive discrete time interval (e.g., approximately 2 minutes). As used herein, the term "approximately" when used in conjunction with a stated numerical value refers to a range of values ​​within 10% of the stated numerical value.

[0043] In some embodiments, the first segment of the filtered HRV data can be passed through a smoothing filter (e.g., a Hann filter). Alternatively or additionally, a Fourier transform can be performed on the first segment of the filtered HRV data to generate a power spectral density (PSD) of the first segment of the filtered HRV data. From the PSD of the first segment of the filtered HRV data, the peak amplitude of the PSD can be determined. The peak amplitude of the PSD can correspond to the respiratory rate associated with respiratory sinus arrhythmia, which is a reliable measure of the parasympathetic branch of the autonomic nervous system.

[0044] In some embodiments, a Gaussian function can be fitted to the peak amplitude of the PSD. Additionally, the area under the Gaussian function can indicate the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia included in the first segment of the filtered HRV data. Thus, the Gaussian function can be integrated over discrete time intervals to determine the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia included in the first segment of the filtered HRV data.

[0045] A score indicating the mindfulness of the user during a time interval (e.g., 2 minutes) associated with the first segment of the filtered HRV data may be determined at least in part based on, for example, the standard deviation of the heartbeat intervals indicating respiratory sinus arrhythmia included in the first segment of the filtered HRV data and the standard deviation of all heartbeat intervals included in the first segment of the filtered HRV data. For example, the score may include a numerical value ranging from 0 to 100. When the score is higher than a first numerical value (e.g., approximately 75), the score may indicate that the user has achieved mindfulness within the discrete time interval. Conversely, when the score is less than a second numerical value (e.g., approximately 50), the score may indicate that the user is stressed and therefore has not achieved mindfulness within the discrete time interval, and the second numerical value is less than the first numerical value. In some embodiments, when the score is greater than or equal to the 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 meditation zone.

[0046] In some embodiments, a score indicative of the user's mindfulness may be determined for each consecutive discrete time interval (e.g., approximately every 2 minutes) by processing each consecutive segment of filtered HRV data. In this way, it may be determined how many minutes of the guided breathing exercise were "mindfulness minutes," during which the user achieved mindfulness.

[0047] In some embodiments, a score indicating mindfulness may be displayed on a 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 may receive real time or near real time feedback on 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 may know if his or her breathing rate is too fast and therefore preventing the user from achieving mindfulness during the guided breathing exercise.

[0048] In some embodiments, a score indicating the user's mindfulness at each consecutive discrete time interval (e.g., approximately every 2 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.

[0049] In some embodiments, a score indicating mindfulness for each of the different segments of filtered heart rate variability data may be stored locally on the wearable electronic device. In this manner, the score may be accessed by the user without a network connection (e.g., an internet connection). In alternative embodiments, one or more memory devices may be on a device (e.g., a server, a mobile computing device, etc.) remote from the wearable electronic device. In this manner, memory on the wearable electronic device may be conserved.

[0050] Example aspects of the present disclosure provide multiple technical effects and benefits. For example, filtering the heart rate variability data can remove noise associated with the heart rate variability obtained from sensors (e.g., electrodes) on a wearable electronic device, and thus can improve the accuracy of the score indicating the user's mindfulness. Therefore, the score can be a more reliable indicator of how mindful the user is during a guided breathing exercise. In addition, displaying the user's breathing rate in conjunction with the score can provide additional insight into the user's score for a particular segment of the filtered heart rate data. For example, when the score indicating mindfulness is low, the breathing rate can be high. Therefore, the user can see that their score for a particular segment is low at least in part due to the user breathing at a faster breathing rate than the user can achieve mindfulness.

[0051] Now referring to the accompanying drawings, Figure 1 and Figure 2A wearable electronic device 100 according to some embodiments of the present disclosure is depicted. The wearable electronic device 100 can be worn, for example, on an arm (e.g., a wrist) of a user. The wearable electronic device 100 can include a housing 110 and one or more electronic components disposed within an interior cavity defined by the housing 110 (e.g., disposed on a printed circuit board). In addition, the wearable electronic device 100 can include a battery (not shown) disposed within the interior cavity of the housing 110.

[0052] In some embodiments, the wearable electronic device 100 may include a first band 120 coupled to the housing 110 at a first position and a second band 122 coupled to the housing 110 at a second position. The first band 120 and the second band 122 can be coupled to each other to secure the housing 110 to the user's arm. For example, the first band 120 may include a buckle or a clasp (not shown). Additionally, the second band 122 may define a plurality of holes 124 spaced apart from each other along the length of the second band 122. In such embodiments, the prongs of the buckle associated with the first band 120 may extend through one of the plurality of openings defined by the second band 132 to couple the first band 120 to the second band 122.

[0053] It should be understood that any suitable type of fastener may be used to couple the first strap 120 to the second strap 122. For example, in some embodiments, the first strap 120 and the second strap 122 may include magnets. In such embodiments, the first strap 120 and the second strap 122 may be magnetically coupled to each other to secure the housing 110 to the user's arm.

[0054] In some embodiments, the wearable electronic device 100 may include a display 130 configured to display content (e.g., time, date, biometrics, notifications, etc.) for viewing by a user. For example, the display 130 may include a plurality of pixels. In some embodiments, the display 130 may include an organic light emitting diode (OLED) display. However, it should be understood that the display 130 may include any suitable type of display.

[0055] 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 in the figure, in some embodiments, the first electrode 140 and the second electrode 142 may be positioned on the lower portion of the housing 110 (e.g., the side facing the wrist). More specifically, the electrodes 140, 142 may be positioned in corresponding holes (e.g., cutouts) defined by the lower portion of the housing 110. In this way, when the user is wearing the wearable electronic device 100, the first electrode 140 and the second electrode 142 may each contact (e.g., touch) the user's skin (e.g., wrist). In addition, since the electrodes 140, 142 contact the user's skin, the electrodes 140, 142 may be used to measure one or more biometric features of the user (e.g., skin electrical activity, electrocardiogram).

[0056] In some implementations, the wearable electronic device 100 may include one or more optical sensors (e.g., photoplethysmography (PPG) sensors) disposed within an internal cavity defined by the housing 110. The optical sensor may be configured to emit an optical signal (e.g., infrared light) that passes through a transparent portion 114 of the housing 110. For example, in some embodiments, the transparent portion 114 of the housing 110 may include one or more windows that are transparent and positioned at different locations (e.g., cutouts) on the housing 110. It should be understood that the optical signal exits the internal cavity of the housing via the transparent portion 114 of the housing 110, penetrates the skin and blood vessels of the user, and returns to the optical sensor as a reflected optical signal. It should be understood that using the disclosure provided herein, one or more biometric features associated with the user may be determined based at least in part on the reflected optical signal.

[0057] Reference now Figure 3A and Figure 3B According to an embodiment of the present disclosure, a graphical representation of a user's heart rate is provided. More specifically, Figure 3A A time-varying signal 200 is shown that indicates the heart rate of a user during a period of time (e.g., 300 seconds) during which the user is stressed. Figure 3B A time-varying signal 202 is shown that indicates the 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 oscillations required to achieve mindfulness and thus reduce stress. As will now be discussed, example aspects of the present disclosure relate to determining a score indicating the user's mindfulness during meditative relaxation (e.g., a guided breathing exercise) to provide insight into the effectiveness of meditative relaxation in reducing stress for the user.

[0058] Reference now Figure 4, which is a flow chart of an example method 300 for determining a score indicating a user's mindfulness during a guided breathing exercise. The method 300 may be implemented, for example, by one or more processors. In some embodiments, the one or more processors may be the one or more processors described above with reference to Figure 1 and Figure 2 300 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.

[0059] At (302), method 300 may include obtaining heart rate variability data of a user while the user performs a guided breathing exercise in which the user inhales for a predetermined amount of time and exhales for a predetermined amount of 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 obtained via one or more biometric sensors (e.g., optical sensors) of the wearable computing device.

[0060] 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 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 marked 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 are less than n standard deviations from the mean of the median filter.

[0061] 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 an amount of time during which the user's breathing rate is substantially constant. In some embodiments, the discrete time interval may be approximately 2 minutes.

[0062] At (308), 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 the filtered HRV data to generate a power spectral density (PSD) of the first segment of the filtered HRV data. From the PSD of the first segment of the filtered HRV data, a peak amplitude of the PSD may be determined. The peak amplitude of the PSD may correspond to a respiratory rate associated with respiratory sinus arrhythmia, which is a reliable measure of the parasympathetic branch of the autonomic nervous system.

[0063] In some embodiments, a Gaussian function can be fitted to the peak amplitude of the PSD. Additionally, the area under the Gaussian function can indicate the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia included in the first segment of the filtered HRV data. Thus, the Gaussian function can be integrated to determine the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia included in the first segment of the filtered HRV data.

[0064] At (310), method 300 may include determining a second standard deviation of all heart beat intervals included in the first segment of the HRV data.

[0065] At (312), method 300 may include determining a score indicating the user's mindfulness during a time interval (e.g., 2 minutes) associated with a first segment of filtered HRV data. The score may be determined based at least in part on a standard deviation of cardiac intervals indicative of respiratory sinus arrhythmia included in the first segment of filtered HRV data and a standard deviation of all cardiac intervals included in the first segment of filtered HRV data, such as based on a ratio of the standard deviation of cardiac intervals indicative of respiratory sinus arrhythmia included in the first segment of filtered HRV data to the standard deviation of all cardiac intervals included in the first segment of filtered HRV data. For example, the score may include a value ranging from 0 to 100. When the score is above a first value (e.g., approximately 75), the score may indicate that the user achieved mindfulness during the time interval. Conversely, when the score is less than a second value (e.g., approximately 50), the second value may indicate that the user is stressed and therefore has not achieved mindfulness, the second value being less than the first value. In some embodiments, when the score is greater than or equal to the second value (eg, approximately 50) but less than the first value (eg, approximately 75), the score may indicate that the user is asleep or in a distracted meditation zone.

[0066] At (314), method 300 may include causing a display screen of a wearable computing device worn by a user performing the guided breathing exercise to display the score determined at (212). Figure 1The display 130 of the wearable computing device 100 discussed is shown as displaying a score of 87 to indicate that the user is achieving mindfulness while performing the guided breathing exercise. Additionally, the display 130 of the wearable computing device 100 is shown as displaying the user's breathing rate (i.e., 6.1 breaths per minute). By doing so, the user can better understand the relationship between breathing rate and mindfulness.

[0067] In some embodiments, a score indicative of the user's mindfulness may be determined for each consecutive time interval (e.g., approximately every 2 minutes) by processing each consecutive 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.

[0068] Reference now Figure 5 , according to an embodiment of the present disclosure, a graphical representation of a segment of filtered HRV data for a user is provided. As shown, the segment of filtered HRV data spans a time interval (i.e., 2 minutes) during which the user's breathing rate is substantially constant. It should be understood that the segment of filtered HRV includes the heart beat intervals detected within the time interval.

[0069] Reference now Figure 6 According to an embodiment of the present disclosure, there is provided Figure 5 A graphical representation of the power spectral density of the filtered HRV. As shown, the peak amplitude Ao of the power spectral density indicates the user's breathing rate. Figure 4 As discussed, a Gaussian function (shown as a dashed line) can be fit to the peak amplitude Ao, and the integral of the Gaussian function can be taken to determine the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia in the segment of the HRV data. For example, the area under the Gaussian function can indicate the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia in the segment of the HRV data. Therefore, the integral of the Gaussian function can be taken to find the standard deviation of the cardiac intervals indicative of respiratory sinus arrhythmia.

[0070] In some embodiments, performing the above reference Figure 4One or more processors of the method discussed may be configured to determine whether the peak amplitude Ao of the power spectral density is actually a peak. For example, one or more processors may be configured to subtract a fitted Gaussian function from the power spectral density function to obtain a residual function. Then, the one or more processors may determine the maximum peak in the residual function over a frequency range having a frequency including the peak amplitude Ao of the power spectral density function. The one or more processors may 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, since the respiratory rate is not explicitly defined, the segment of the HRV data is discarded.

[0071] Reference now FIG. 7A to FIG. 7D , according to embodiments of the present disclosure, provides a graphical illustration of the effectiveness of different metrics in determining mindfulness for a number of different groups or breathing patterns both during meditation and at rest. Fig. 7A The ABI or scores of each of the different groups during meditation and at rest are depicted. Figure 7B The root mean square difference (RMSSD) of the running values ​​during meditation and at rest is depicted for each of the different groups. Figure 7C Heart rates during meditation and at rest for each of the different groups are depicted. Fig.7D The breathing rate of each of the different groups during meditation and at rest is depicted. As can be seen, the ABI ( Fig. 7A ) and respiratory rate ( Fig.7D ) is better than RMSSD( Figure 7B ) and heart rate ( Figure 7C ) is more sensitive.

[0072] Reference now Figure 8 , illustrates components of an example computing system 400 of a wearable computing device 100 that may be utilized in accordance with various embodiments. In particular, as shown, the computing system 400 may also include at least one controller 402. Furthermore, in embodiments, the controller 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 such options). For example, in embodiments, 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 a sequence of instructions that, when loaded from the memory device 404 and executed using the controller 402, causes the controller 402 to perform the functions described herein.

[0073] The computing system 400 may include many types of memory, data storage, or computer-readable media, such as data storage for program instructions executed by a controller or any suitable processor. The same or separate storage may be used for images or data, removable memory may be used to share information with other devices, and any number of communication methods may be used to share with other devices. In addition, as shown, the computing system 400 includes a display 130, which may be a touch screen, an organic light emitting diode (OLED), or a liquid crystal display (LCD), but the device may communicate information via other means, such as through an audio speaker, a projector, or projecting a display or streaming data to another device such as a mobile phone, where an application on the mobile phone displays the data.

[0074] The computing system 400 may include one or more wireless networking components 412 that are operable to communicate with one or more electronic devices within the communication range of a particular wireless channel. The wireless channel may be any suitable channel for enabling 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.

[0075] The computing system 400 also includes one or more energy storage devices 408, which are operable to be charged by conventional plug-in methods. In some embodiments, the computing system 400 may also include at least one additional I / O device 410 capable of receiving conventional input from a user. The conventional input may include, for example, a button, a touch pad, a touch screen, a roller, a joystick, a keyboard, a mouse, a keypad, or any other such device or element, whereby a user can input commands to the computing system 400. In some embodiments, the I / O device 410 may be connected by wireless infrared or Bluetooth or other links in some embodiments. In some embodiments, the computing system 400 may include a microphone or other audio capture element that accepts voice or other audio commands. In some embodiments, the I / O device 410 may include one or more electrodes, an optical sensor, a barometric pressure sensor (e.g., an altimeter, etc.), etc.

[0076] The computing system 400 may include a driver 414 and at least some combination of one or more emitters 416 and one or more detectors 418 for measuring data of one or more indicators of a human body (such as a person wearing the wearable computing device 100). In some embodiments, for example, this may involve at least one imaging element, such as one or more cameras capable of capturing images of the surrounding environment and capable of imaging a user, person, or object near the device. The image capture element may include any appropriate technology, such as a CCD image capture element with sufficient resolution, focal range, and visual area to capture an image of the user when the user operates the device. Further image capture elements may also include a depth sensor. Methods for capturing images using camera elements with computing devices are well known in the art and will not be discussed 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. Further, the computing system 400 may include capabilities such as starting and / or stopping image capture when a command from a user, application, or other device is received.

[0077] In one example, the emitter 416 and the detector 418 may also be capable of obtaining an optical photoplethysmogram (PPG) measurement. Some PPG techniques rely on detecting light at a single spatial location, or adding signals acquired from two or more spatial locations. Both approaches produce a single spatial measurement from which a heart rate (HR) estimate (or other physiological indicator) can be determined. In some embodiments, the PPG device employs a single light source (i.e., a single light path) coupled to a single detector. Alternatively, the PPG device may employ multiple light sources (i.e., two or more light paths) coupled to a single detector or multiple detectors. In other embodiments, the PPG device employs multiple detectors coupled to a single light source or multiple light sources (i.e., two or more light paths). In some cases, the light source may be configured to emit one or more of green light, red light, infrared (IR) light, and any other suitable wavelength in the spectrum (such as long IR for metabolic monitoring). For example, the PPG device may employ a single light source and two or more light detectors, each of which is configured to detect a specific wavelength or wavelength range. In some cases, each detector is configured to detect a wavelength or wavelength range that is different from each other. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In yet another case, one or more detectors are configured to detect a specific wavelength or wavelength range that is different from one or more other detectors. In embodiments employing multiple light paths, the PPG device can determine an average of the signals generated from the multiple light paths before determining an HR estimate or other physiological indicator.

[0078] In addition, in an embodiment, the transmitter 416 and the detector 418 can be coupled to the controller 402 directly or indirectly using a driver circuit, through which the controller 402 can drive the transmitter 416 and obtain a signal from the detector 418. The host computer 422 can communicate with the wireless networking component 412 via one or more networks 420, which can include one or more local area networks, wide area networks, UWB and / or the Internet using any terrestrial or satellite links. In some embodiments, the host computer 422 executes a control program and / or application.

[0079] Although the present subject matter has been described in detail with respect to various specific example embodiments of the present subject matter, each example is provided by way of explanation rather than limitation of the present disclosure. After obtaining an understanding of the foregoing, those skilled in the art can easily produce changes, modifications, and equivalents to such embodiments. Therefore, the present disclosure does not exclude such modifications, changes, and / or additions to the present subject matter that will be readily understood by those of ordinary skill in the art. For example, a feature shown or described as a part of an embodiment can be used together with another embodiment to produce yet another embodiment. Therefore, the present disclosure is intended to cover such changes, changes, and equivalents.

Claims

1. A computer-implemented method for determining the mindfulness of a user performing a guided breathing exercise, the computer-implemented method comprising: obtaining 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 heart beat intervals included in a first segment of the filtered heart rate variability data and indicative of a respiratory sinus arrhythmia, the first segment spanning a discrete time interval; determining a second standard deviation of all heart beat intervals included in the first segment of the filtered heart rate variability data; and A score indicative of mindfulness of the user over the discrete time interval is determined based at least in part on the first standard deviation and the second standard deviation.

2. The computer-implemented method of claim 1 , further comprising: A display screen of a wearable computing device worn by the user is caused to display the score.

3. The computer-implemented method of claim 1 , wherein determining the score indicative of the user's mindfulness over 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. The computer-implemented method of claim 1 , wherein determining the first standard deviation comprises: applying a Fourier transform to said first segment of said filtered heart rate variability data to obtain a normalized power spectral density function; determining a peak magnitude of the normalized power spectral density function, the peak magnitude being indicative of a breathing rate of the user; fitting a Gaussian function to the peak amplitude of the normalized power spectral density function; and The first standard deviation is determined based at least in part on the Gaussian function.

5. The method of claim 4, further comprising: causing a display screen of a wearable computing device worn by the user to display the score; as well as The display screen of the wearable computing device is caused to display the breathing rate of the user.

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

9. The computer-implemented method of claim 1, wherein: When the score is greater than or equal to a first value, the score indicates that the user is mindful; The score indicates that the user is stressed when the score is less than a second value, the second value being less than the first value.

10. A wearable computing device comprising: one or more biometric sensors; as well as One or more processors configured to: obtaining, 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; filtering the heart rate variability data to generate filtered heart rate variability data; determining a first standard deviation of heart beat intervals included in a first segment of the filtered heart rate variability data and indicative of a respiratory sinus arrhythmia, the first segment spanning a discrete time interval; determining a second standard deviation of all heart beat intervals included in the first segment of the filtered heart rate variability data; and A score indicative of mindfulness of the user over the discrete time interval is determined based at least in part on the first standard deviation and the second standard deviation.

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

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

13. The wearable computing device of claim 10, wherein to determine the first standard deviation, the one or more processors are configured to: 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 magnitude of the normalized power spectral density function, the peak magnitude being indicative of a breathing rate of the user; fitting a Gaussian function to the peak amplitude of the normalized power spectral density function; and The first standard deviation is determined based at least in part on the Gaussian function.

14. The wearable computing device of claim 13, wherein the one or more processors are further configured to: causing 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. 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 interval 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 indicative of the user's mindfulness over the discrete time interval ranges from 0 to 100.

18. The wearable computing device of claim 16, wherein: When the score is greater than or equal to a first value, the score indicates that the user is mindful; The score indicates that the user is stressed when the score is less than a second value, the second value being less than the first value.

19. A computer-implemented method for determining the mindfulness of a user performing a guided breathing exercise, the method comprising: obtaining heart rate variability data of the user performing the guided breathing exercise; determining a first standard deviation of heart beat intervals included in a first segment of the heart rate variability data and indicative of a respiratory sinus arrhythmia, the first segment spanning a discrete time interval; determining a second standard deviation of all heart beat intervals included in the first segment of the heart rate variability data; and A score indicative of mindfulness of the user over the discrete time interval is determined based at least in part on the first standard deviation and the second standard deviation.

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

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