Circadian rhythm adaptation

Abstract

A method for estimating adaptation of a circadian rhythm of a user, the method comprising: measuring a circadian phase of the user by using at least one of a skin temperature sensor, a cardiac activity sensor, and a motion sensor; measuring a sleep-wake rhythm of the user by using at least one of the cardiac activity sensor and the motion sensor, and detecting a change in the sleep-wake rhythm of the user based on the measurement; determining a setpoint phase of a circadian phase based on the altered sleep-wake rhythm; determining a target day and night phase based on the position of the user; determining a diurnal adaptation rate based on a first adaptation rate if the setpoint phase and the target phase are in the same direction with respect to the current diurnal phase, and determining a diurnal adaptation rate based on a second adaptation rate if the setpoint phase and the target phase are in opposite directions from the current diurnal phase, wherein the second adaptation rate is smaller than the first adaptation rate; and outputting, via an interface, a derived parameter of the determined circadian adaptation or the calculated circadian adaptation to be presented to a user.

Description

Technical Field

[0001] The present invention relates to the adaptation of estimating a user's circadian rhythm. Background Art

[0002] A circadian rhythm (equivalent to a day-night process, a day-night cycle) can be understood as an internal physiological process of the human body that regulates a person's natural sleep-wake rhythm. The circadian rhythm repeats approximately every 24 hours. The day-night cycle includes day-night phases of timing that can be understood as reflecting different states of the circadian rhythm during a (calendar) day. Different people have different natural day-night phases, and many daily factors affect a person's ability and willingness to follow the natural day-night phase.

[0003] The circadian rhythm is a function of a person's sleep rhythm. The sleep-wake rhythm provides a set point for the circadian rhythm. A person may need to change the sleep-wake rhythm in various situations, such as traveling across time zones, changing to daylight-saving time / winter time, shift work, social activities, and other changes in the sleep rhythm and / or environment. The change causes circadian misalignment between the circadian rhythm and the sleep-wake rhythm, which even reduces alertness for several days. The term circadian misalignment refers to the phase shift between a person's internal circadian rhythm and the actual sleep-wake rhythm. When a person travels rapidly across more than two time zones, the person's internal clock takes time to adjust to the new day-night schedule at the person's destination. After arrival, the person's circadian rhythm is out of sync with the sleep-wake rhythm of the new destination but will gradually adjust to the new time zone. It is generally believed that flying east causes more severe jet lag symptoms than flying west. This is because a person's body adapts to staying up late faster than going to bed earlier than normal. In several countries, twice a year, there is a transition into and out of daylight-saving time. Therefore, people experience minor jet lag due to the loss of synchronization between their internal circadian rhythm, sleep-wake rhythm, and the environmental light-dark rhythm. Many people engage in shift work, and they experience minor jet lag every week. In addition, even more people voluntarily shift their bedtime and wake-up time between weekdays and weekends, resulting in recurrent circadian misalignment, called social jet lag.

[0004] US10,448,829 discloses a method for detecting, estimating, and displaying disorders of a user's biological rhythm, where the disorder is caused by travel, irregular sleep habits, or shift work. The method also estimates the recovery time from the phase shift. For example, although the method identifies daylight as a factor affecting the circadian rhythm, the method does not consider this factor as a factor related to recovery from the disorder. Summary of the Invention

[0005] According to one aspect, there is an adapted method for estimating a user's circadian rhythm, the method comprising: measuring the user's circadian phase by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; measuring the user's sleep-wake rhythm by using at least one of a heart activity sensor and a motion sensor; detecting a change in the user's sleep-wake rhythm based on the measurements; determining a set point phase of the circadian phase based on the changed sleep-wake rhythm; determining a target circadian phase based on the user's location; determining a circadian adaptation rate based on a first adaptation rate if the set point phase and the target phase are in the same direction relative to the current circadian phase, and determining a circadian adaptation rate based on a second adaptation rate if the set point phase and the target phase are in opposite directions from the current circadian phase, wherein the second adaptation rate is less than the first adaptation rate; and outputting, via an interface, the determined circadian adaptation rate or a derived parameter of the determined circadian adaptation rate to be presented to the user.

[0006] According to one aspect, there is an adapted apparatus for estimating a user's circadian rhythm, the apparatus comprising: means for measuring the user's circadian phase by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; means for measuring the user's sleep-wake rhythm by using at least one of a heart activity sensor and a motion sensor; means for detecting a change in the user's sleep-wake rhythm based on the measurements; means for determining a set point phase of the circadian phase based on the changed sleep-wake rhythm; means for determining a target circadian phase based on the user's location; means for determining a circadian adaptation rate based on a first adaptation rate if the set point phase and the target phase are in the same direction relative to the current circadian phase, and means for determining a circadian adaptation rate based on a second adaptation rate if the set point phase and the target phase are in opposite directions from the current circadian phase, wherein the second adaptation rate is less than the first adaptation rate; and means for outputting, via an interface, the determined circadian adaptation rate or a derived parameter of the determined circadian adaptation rate to be presented to the user.

[0007] According to one aspect, there is provided a computer program product embodied on a computer-readable distributed medium and including instructions that, when loaded into a device, perform: measuring a user's circadian phase by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; measuring a user's sleep-wake rhythm by using at least one of a heart activity sensor and a motion sensor; detecting a change in the user's sleep-wake rhythm based on the measurement; determining a set point phase of the circadian phase based on the changed sleep-wake rhythm; determining a target circadian phase based on the user's location; determining a circadian adaptation rate based on a first adaptation rate if the set point phase and the target phase are in the same direction relative to the current circadian phase, and determining a circadian adaptation rate based on a second adaptation rate if the set point phase and the target phase are in opposite directions from the current circadian phase, where the second adaptation rate is less than the first adaptation rate; and outputting via an interface the determined circadian adaptation rate or a derived parameter of the determined circadian adaptation rate to be presented to the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Hereinafter, the present invention will be described in more detail by way of preferred embodiments with reference to the accompanying drawings, in which:

[0009] Figure 1 illustrates an example system to which embodiments of the present invention may be applied;

[0010] Figure 2 illustrates an embodiment of a circadian rhythm detection method;

[0011] Figure 3 illustrates an embodiment of a circadian rhythm detection method in which such skin temperature measurement data is excluded from fitting to a cosine waveform for which the corresponding skin contact measurement data indicates no contact with the skin;

[0012] Figure 4 illustrates an embodiment of a circadian rhythm detection method in which the amount of skin temperature measurement is checked against a first threshold;

[0013] Figure 5 illustrates changing the phase of a cosine waveform;

[0014] Figure 6 illustrates an embodiment of a circadian rhythm detection method in which a circadian phase is selected between a first phase value and a combination of the first phase value and a second phase value;

[0015] Figure 7 illustrates an embodiment of determining a circadian phase via a sleep-wake rhythm;

[0016] Figure 8 illustrates an embodiment of a device for detecting a circadian rhythm and for adaptation of the circadian rhythm;

[0017] Figure 9 Illustrates an embodiment of a method for circadian adaptation;

[0018] Figure 10 Illustrates an embodiment of a method for circadian adaptation, wherein the circadian adaptation rate is determined using the user's light exposure;

[0019] Figure 11 Illustrates an embodiment of a method for circadian adaptation, wherein the circadian adaptation rate is determined depending on a set - point phase to the west or east; and

[0020] Figure 12 Illustrates an embodiment of an iterative algorithm for measuring the circadian phase. Detailed Description

[0021] The following embodiments are illustrative. Although the specification may refer to "one", "a", or "some" embodiments in several places in the text, this does not necessarily mean that each reference is to the same embodiment, or that a particular feature applies only to a single embodiment. Individual features of different embodiments may also be combined to provide other embodiments.

[0022] Figure 1 Illustrates a measurement system including a sensor device that can be used in the context of some embodiments of the present invention. User 20 may wear a wearable device, such as a wrist - worn device 11. The wrist - worn device 11 may be, for example, a smartwatch, a smart device, a sports watch, and / or an activity - tracking device (e.g., a bracelet, an armband, a wristband, a mobile phone, glasses). In an embodiment, the wrist - worn device 11 is an activity - tracking device or a wearable activity - tracking device. This may mean that the device can be worn on the wrist of the user 20 or on other parts, such as but not limited to the forearm, bicep area, neck, forehead, and / or leg. The measurement system may also include a strap or band for attaching the wearable device to the user (e.g., to the wrist). Data retrieved from the wrist - worn device 11 can also be used to determine the user's 20 sleep start time and sleep end time as well as skin temperature. The embodiments described herein use wrist skin temperature as well as sleep start and wake - up times to determine circadian rhythm characteristics. As is known in the literature, body core temperature represents the circadian rhythm in great detail, and it peaks during the day and is lowest at night. Thus, the circadian rhythm can be measured by measuring body core temperature. Wrist skin temperature shows the opposite pattern, lowest during the day and highest at night. However, they both follow the same rhythm, and thus the circadian rhythm can also be measured from skin temperature. The skin temperature sensor can be implemented in the wearable device as one of the sensors 12 of the wearable device.

[0023] In Figure 1 In the example illustrated in Figure 1 , the measurement system includes at least a processing circuitry configured to analyze measurement data 13 measured from user 20, e.g., by performing the method described in more detail below. The processing circuitry may be implemented in a wearable device worn by the user, or the processing circuitry may be implemented in a user device 10 such as a smart phone or a tablet computer, or the processing circuitry may be implemented in a server computer such as a cloud server. The measurement data 13 may be provided by at least one sensor device 12, which may be included in the wearable device 11, or the sensor device 12 may be external to the wearable device 11 but have a data transmission capability with the wearable device 11. The wearable device 11, the user device 102, the server computer, and / or the sensor device 12 may be connectable via one or more networks, via a short-range wireless connection such as Bluetooth, or via a Universal Serial Bus (USB) connection.

[0024] The sensors of the sensor device 12 may employ one or more measurement techniques for measuring the activities and skin temperature of the user 20. Activity measurement may be based on the use of a cardiac activity sensor. At least one sensor device 12 may be configured to measure skin temperature, and optical or bioimpedance methods may be used to measure skin contact such that temperature samples measured when the sensor is not worn can be detected and optionally excluded from the estimation of the circadian rhythm. The sensor device 12 may measure one or more of the following characteristics from the user: movement, electrocardiogram (ECG), photoplethysmogram (PPG), bioimpedance, galvanic skin response, body temperature. To measure movement, the sensor device 12 may include inertial sensors such as an accelerometer and / or a gyroscope, or a magnetometer, or sensor fusion of any combination of these motion sensors, and the sensor device 12 may output movement measurement data. The sensor device 12 for measuring ECG or PPG may output cardiac activity measurement data 13. The sensor device 12 may include one or more electrodes attachable to the skin of the user 20 to measure the electric property from the skin, which can be processed into an ECG signal, a bioimpedance signal, or a galvanic skin response signal through appropriate signal processing techniques. In some techniques, the cardiac activity measurement data 13 may represent the occurrence of the electrical cardiac pulse R wave. In PPG measurement, light emitted by a light-emitting diode or a similar light source and reflected from the skin of the user 20 is sensed by using a photodiode or a similar light-sensing component. Then, the sensed light is converted into an electrical measurement signal in the light-sensing component, and signal processing is used to detect the desired signal component from the electrical measurement signal. For example, in PPG measurement, a P wave may be detected, which enables the calculation of the PP interval and heart rate.

[0025] The user device 10 refers to a computing device (equipment, device), and it may also be referred to as a user terminal, user device, mobile device, or mobile terminal. A portable computing device (device) includes a wireless mobile communication device operating with or without a subscriber identity module (SIM) in hardware or software, including but not limited to the following types of devices: mobile phones, smart phones, personal digital assistants (PDAs), cell phones, laptop computers and / or touchscreen computers, tablets (tablet computers), multimedia devices, wearable computers (such as smart watches), and other types of wearable devices, such as clothing and accessories incorporating computers and advanced electronic technologies. The user device 102 may include one or more user interfaces. The one or more user interfaces may be any kind of user interface, such as a screen, keyboard, speaker, microphone, touch user interface, integrated display device, and / or external display device.

[0026] Figure 2 An embodiment is illustrated in which there is a method for detecting the circadian rhythm of a user 20, the method comprising: obtaining 201 measurement data indicative of sleep time and determining the sleep-wake rhythm of the user 20 based on the measurement data, and further determining 202 a first phase value based on the sleep-wake rhythm, and further obtaining 203 skin temperature measurement data for at least one 24-hour measurement period, and further fitting 204 the skin temperature measurement data to a cosine waveform, and performing an assessment 205 of the quality of the fit, and if the quality is above a threshold, determining 207 a second phase value based on the fit, and if the quality of the fit is above a threshold, determining 208 the circadian phase of the user 20 as a combination of the first phase value and the second phase value, and if the quality of the fit is below the threshold, determining 206 the circadian phase of the user 20 based on the first phase value without the second phase value, and outputting 209 via an interface the calculated circadian phase or a derived parameter of the calculated circadian phase to be presented to the user 20. The second phase value may be determined before or immediately after the assessment 205 of the fit 204.

[0027] The measurement data obtained in block 201 may be obtained by using at least one of a heart activity sensor and a motion sensor of the sensor device 12. Detection of the sleep start time and the sleep end time may be performed according to the prior art. Detection of the sleep start time and the sleep end time by using a heart activity sensor and / or a motion sensor is itself well known and there are several commercially available products for performing this, such as the PolarVantage series from Polar Electro.

[0028] Figure 2 An embodiment provides for verifying the effect of the second phase value based on skin temperature measurement. Measuring skin temperature requires skin contact between the sensor device 12 and the skin of the user 20. The user may wish to wear the sensor device in a comfortable placement manner without pressing the sensor device against the skin. The drawback is that the skin contact may not be optimal. In addition, the skin contact may change during the user's daily activities as well as during the night. In addition, although fitting skin temperature measurement data with a cosine function (also known as cosinor analysis or cosine method fitting in the literature) is commonly used for circadian phase detection, the fit may fail for several reasons. Therefore, although the second estimate for the circadian phase improves the accuracy of the circadian phase estimate, it is advantageous to verify the second estimate before combining it with the first estimate.

[0029] In Figure 2During the process, the first phase value is used, either alone or in combination with the second phase value, to estimate the circadian phase. If the quality of the second phase value is determined to be high enough (e.g., the fit is considered successful), then the second phase value can be calculated, or a previously calculated phase value can be considered eligible for combination.

[0030] The measurement period should be at least one sleep-wake cycle (i.e., 24 hours), but longer measurement periods of several sleep-wake cycles also provide better quality data. A measurement period of one week (i.e., seven sleep-wake cycles) has provided very reliable data.

[0031] In an embodiment, the cosine method fit in block 204 is performed by using least squares (LS) fitting, which is known per se in the art. The LS fit finds the cosine waveform (phase) that provides the least square error with respect to the measured data. Then, the phase of the fitted cosine waveform represents the second phase value.

[0032] In an embodiment, Figure 2 the process includes excluding from the fit to the cosine waveform such skin temperature measurement data for which the corresponding skin contact measurement data indicates no contact with the skin. Figure 3 This embodiment is illustrated, where skin temperature measurement data and skin contact measurement data for at least one 24-hour measurement period are acquired together 301, and such skin temperature measurement data 302 for which the skin contact measurement data indicates no contact with the skin is excluded from the fit to the cosine waveform in 204. As described above, due to the user 20 not wearing the device at all for some time during the measurement period, or the device not having actual contact with the skin of the user 20, a portion of the skin temperature measurement data is lost. Temperature measurement data associated with the lack of skin contact will indicate an ambient temperature that will degrade the performance of the fit.

[0033] Regarding validating the skin temperature measurement and the second phase value, Figure 4 An embodiment of the method is illustrated. Referring to Figure 4, if, during at least one 24-hour skin temperature measurement period, the amount of valid skin temperature measurement data associated with skin contact measurement data indicating skin contact is less than a first threshold, then process 401 excludes the fitting of the skin temperature measurement data to the cosine waveform. In an embodiment, the valid skin temperature measurement data covers at least 80% of the maximum skin temperature measurement data. This means that skin contact should be present for at least 80% (at least 19.2 hours) of the 24-hour cycle. Having less than 80% of the skin temperature measurement data reduces the reliability of circadian rhythm detection using skin temperature measurements. In other embodiments, different thresholds, such as 70%, 75%, or 85%, may be employed, depending on how much error tolerance is acceptable and how accurately the first phase value is determined.

[0034] In another embodiment, the skin temperature measurement is verified by evaluating the performance of the cosine method fitting of block 204. This can be achieved by arranging a phase shift between the measurement data and the cosine waveform after the fitting and evaluating the change in an error metric indicating the error between the measurement data and the cosine waveform. In other words, the phase shift is used to determine whether there is a better fit than the one selected in 204. Figure 5 Illustrates the waveform used in the quality assessment of the second phase value after fitting the skin temperature measurement data to the cosine waveform. In this embodiment, evaluating the quality of the fit includes the following steps:

[0035] · Determine a first corresponding metric of the skin temperature measurement data relative to the cosine waveform 500 after the fitting. The first corresponding metric indicates the error between the skin temperature measurement data and the cosine waveform 500 after the fitting.

[0036] · Arrange a forward phase shift 502 and / or a backward phase shift 501 between the cosine waveform 500 and the skin temperature measurement data fitted with the cosine waveform 500, and determine at least a second corresponding metric of the skin temperature measurement data relative to the cosine waveform after the (one or more) phase shifts 501, 502. In the case of performing multiple phase shifts, multiple corresponding metrics are calculated.

[0037] · If the second corresponding metric (and additionally the (one or more) corresponding metrics)

[0038] indicates a greater error than the first corresponding metric, then determine that the fit quality is higher than the threshold, and if the second corresponding metric (or at least one of the additional (one or more) corresponding metrics) indicates a smaller error than the first corresponding metric, then determine that the fit quality is lower than the threshold.

[0039] In other words, if the phase shift results in a fit associated with a lower fitting error, the fit performed in 204 is not optimal, and the circadian phase can be determined in block 206 by using only the sleep-wake rhythm. The calculation of the corresponding metric can be done by calculating the root mean square error (RMSE) of the fit (i.e., the RMSE between the skin temperature measurement data and the fitted cosine waveform). Similarly, in the case of the second corresponding metric, the RMSE between the skin temperature measurement data and the offset cosine waveform can be calculated. When the phase of the cosine waveform changes, the RMSE should increase, thus indicating that the fit is optimal, and the phase of the fitted cosine waveform can be verified as the circadian phase estimate. The absolute value of the RMSE may vary greatly among different users, but critically, the RMSE should increase when the fitted phase value is changed forward or backward. Different calculations of the corresponding metric can also be used to perform a quality assessment, such as the mean absolute error (MAE). The corresponding metric is preferably different from the metric used for the fit in block 204.

[0040] If there are too few temperature samples with verified skin contact available, or if the quality assessment analysis of the fitted second phase value (e.g., calculation based on RMSE) indicates that the fit is not very accurate, the result from the second phase value can be ignored or the calculation of this phase value can be omitted.

[0041] Figure 6 Yet another embodiment for verifying the second phase value is illustrated, where the method further includes checking 601 if the difference between the first phase value and the second phase value is less than a second threshold, then determining 602 the circadian phase as a combination of the first phase value and the second phase value. The combination can be the average or weighted average of the first phase value and the second phase value. In another embodiment, the first phase value is offset based on the difference between the first phase value and the second phase value. A maximum limit has been defined for the offset, for example, four hours. The offset can be different from the averaging 25 operation and can be linear or non-linear with respect to the difference between the first phase value and the second phase value. If the difference between the first phase value and the second phase value is greater than the second threshold, the circadian phase is determined as the first phase value. The second threshold can be defined according to the time offset between the first phase value and the second phase value. Example values for the second threshold can range from one hour to even ten hours, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 hours. Combining this with the embodiment of offsetting the first phase value based on the difference, if the second threshold is 10 hours and the maximum offset is 4 hours, when the difference is 10 hours, the first phase value can be offset by 4 hours towards the second phase value. If the first phase value 35 and the second phase value are equal, no offset of the phase value is performed. When the difference is between zero and 10 hours, the 10 offset can be mapped in a linear or non-linear manner.

[0042] As described above, the first phase value is calculated from the sleep-wake rhythm, which can be based on cardiac activity measurement data and / or motion measurement data, while the second phase value is calculated based on skin temperature measurement data fitted with a cosine waveform. Although both phase values indicate the circadian phase, due to the different measurement data measured and different physiological characteristics, these two phase values are in different domains in nature. Therefore, the embodiments include offsetting one of the first phase value and the second phase value by a constant value so that the first phase value and the second phase value are comparable.

[0043] Conventional methods for estimating the circadian phase from the sleep-wake rhythm are based on determining the sleep midpoint as the midpoint between the detected sleep start time and sleep end time from the measurement data. This provides an accurate circadian phase estimate when the sleep rhythm of user 20 is regular. However, if the user goes to bed late and wakes up at the same time to reduce the sleep duration, the sleep midpoint is delayed and the circadian phase is also shifted (delayed). The problem is that in such a case, the circadian phase of user 20 has not actually changed, but the user experiences sleep deprivation. To overcome the problem of user 20 changing his / her sleep time (e.g., reducing the sleep amount but waking up at the same time), the embodiments omit the sleep start time from the estimation of the first phase value. Instead, the first phase value is calculated based on the target sleep time (the required sleep amount, which can be estimated, for example, using a sleep assistance algorithm, general sleep amount guidelines, or can be received as user input) and the sleep end time to estimate what the sleep midpoint should be if user 20 had slept sufficiently before waking up. This method provides a more accurate circadian phase estimate in cases where the user has sleep deprivation due to his / her sleep habits. This can be achieved by obtaining the sleep end time from the measurement data indicating the sleep time of user 20, and also obtaining the target sleep time of user 20, determining the sleep midpoint of user 20 based on the sleep end time and the target sleep time, and calculating the sleep-wake rhythm based on the sleep midpoint. Figure 7 The flowchart of an embodiment for calculating the sleep midpoint for the purpose of estimating the circadian rhythm is illustrated. Refer Figure 7 , in 700, sleep measurement data is obtained. The sleep measurement data can include cardiac activity measurement data and / or motion measurement data that enable the detection of at least the sleep end time. In 702, the sleep end time and the target sleep time are determined. The target sleep time may have been pre-stored in the memory, and the sleep end time can be determined from the sleep measurement data obtained in 700. In 704, according to the following equation, the sleep midpoint can be determined as the midpoint between the sleep end time and the sleep end time minus half of the target sleep time:

[0044] Sleep midpoint estimate = sleep end time - target sleep time / 2 (1)

[0045] The purpose of calculating the day-night phase can be to provide the user with information related to the day-night phase. Such information can include, for example, recommending a bedtime to the user. As is known in the art, the circadian rhythm represents an internal clock and thus carries information about the natural bedtime for the user. If the user follows his / her natural rhythm, health benefits can be achieved. Another example of a derived parameter of the day-night phase is the chronotype of user 20. The calculation of the day-night phase enables the classification of user 20's sleep chronotype based on the day-night phase and the output of the sleep chronotype as a parameter via the interface. The chronotype classification can be based on observing the day-night phase of user 20 over a long period (e.g., at least one week). For example, the day-night phase of a night owl may be delayed relative to that of an early bird. Yet another example of a derived parameter of the day-night phase is an alert of reduced alertness output to the user. For example, if the day-night phase estimation indicates that the circadian rhythm of user 20 has changed due to traveling across time zones or due to shift work, the user can be warned that this has consequences in the form of reduced alertness. For example, the parameters listed above can be displayed to the user via the user interface of the wearable device 11 or the user device 10.

[0046] The above-described embodiments for determining the day-night phase can be used to directly infer the day-night phase or the set point of the day-night phase. If the user leads a regular daily rhythm including a regular sleep cycle by going to sleep and waking up at substantially the same time every day, the above-described embodiments can directly indicate the day-night phase of user 20. However, if the user has changed his / her sleep-wake rhythm due to traveling across time zones, shift work, etc., the day-night phase takes time to adapt to the new sleep-wake rhythm. In such a case, the above-described embodiments can define a set point for the day-night phase, and the day-night phase approaches this set point at a certain daily adaptation rate. In such a case, today's day-night phase can be determined based on the set point, the adaptation rate, and yesterday's day-night phase. Embodiments for determining the adaptation rate are described below.

[0047] The above-described embodiments for estimating the day-night phase can be used for the purpose of detecting the alertness of user 20, providing guidance related to the health, fitness, or training of user 20, or can also be used to detect changes in the day-night phase. As described in the background art, changes in the day-night phase can be caused by various reasons affecting the sleep-wake rhythm of user 20, and it would be beneficial to estimate and output to the user information about how much time the body of user 20 needs to adapt to the changed sleep-wake rhythm. However, it should be understood that in the following embodiments, the day-night phase can be determined by using other methods (e.g., methods according to the prior art).

[0048] Figure 9 An embodiment of a method for estimating adaptation of a user's circadian rhythm is illustrated, where the method includes: measuring 901 the circadian phase of user 20 by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; measuring 902 the sleep-wake rhythm of user 20 by using at least one of a heart activity sensor and a motion sensor, and detecting a change in the sleep-wake rhythm of user 20 based on the measurement; determining 903 a set point phase of the circadian phase based on the changed sleep-wake rhythm; determining 904 a target circadian phase based on the location of user 20; determining 906 the circadian adaptation rate based on a first adaptation rate if the set point phase and the target phase are in the same direction relative to the current circadian phase; and determining 907 the circadian adaptation rate based on a second adaptation rate if the set point phase and the target phase are in opposite directions from the current circadian phase, where the second adaptation rate is less than the first adaptation rate; and outputting 908 via an interface the determined circadian adaptation rate or a derived parameter of the determined circadian adaptation rate to be presented to the user.

[0049] Using the target circadian phase based on the location of user 20 as a reference point for the adaptation rate of the circadian phase provides a more accurate estimate of the adaptation rate than conventional solutions, especially when the user changes his / her sleep-wake rhythm away from the target circadian phase.

[0050] In an embodiment, the adaptation rate is visually indicated to the user via a user interface of a wearable device (such as a wrist device) or a user device. The visual output may additionally include an indicator of the current circadian phase relative to the set point. In addition, the target circadian phase may be displayed to the user. A derived parameter of the calculated circadian adaptation rate is an indicator of the distance between the current circadian phase and the set point phase. The (daily) evolution of such an indicator is also an indicator of the adaptation rate. Yet another derived parameter of the adaptation rate is an intelligent instruction to the user. For example, if the adaptation rate is non-zero, then the Figure 9 device of the process may output an indication that the circadian phase is currently adapting, and adapt the user's training schedule such that the intensity of the training schedule is reduced during at least a portion of the duration in which the circadian phase is adapting. Thus, the device may modify at least one exercise plan in the training schedule by reducing the intensity of at least one exercise. Yet another example of a parameter is the sleep time recommended to the user. When the circadian phase is shifting, the optimal bedtime for the user is also shifting, and the recommended sleep time is a function of the determined adaptation rate. For example, the new sleep time may be adapted in the same direction as the direction in which the circadian phase is adapting and by an amount of the (daily) adaptation rate.

[0051] In an embodiment, a temperature sensor is also used to determine the circadian phase. In an embodiment, the circadian phase is determined by using the same sensors as those used to determine the sleep-wake rhythm, such as any combination of a heart activity sensor, a motion sensor, and a skin temperature sensor.

[0052] The circadian rhythm adapts to changes in the sleep-wake rhythm. For example, if there is a rapid and significant change in the sleep timing (such as a time zone change), the circadian rhythm starts to gradually shift towards the new changed sleep timing instead of immediately jumping to follow the new sleep rhythm. The adaptation rate is a function of the changed sleep-wake rhythm and additionally a function of the natural circadian rhythm of the (possibly new) geographical location of the user. Different geographical locations on Earth have different daylight rhythms, and as described in the background art, the daylight rhythm affects the natural circadian rhythm of a particular location. In addition, the daylight rhythm at the user's location affects the adaptation rate, and the embodiments described herein quantify this characteristic, thus providing a more accurate estimate of the circadian phase adaptation rate.

[0053] In addition, the method uses wrist skin temperature and sleep-wake times to determine circadian rhythm characteristics. The 24-hour profile of wrist skin temperature provides a viable measure of the circadian phase in a free-living environment. It is related to the secretion pattern of melatonin (also known as the "sleep hormone"). The sleep-wake rhythm can be calculated from heart activity measurement data and / or activity measurement data. There are several commercially available solutions and products for detecting the sleep start time and sleep end time based on such measurement data, and thus their detailed description is omitted.

[0054] In some embodiments, for example, in the absence of wrist skin temperature data or as a supplement to wrist skin temperature data, the circadian rhythm characteristics can be derived from the measurement data used to determine the sleep-wake rhythm, such as as described above in connection with Figure 2 what has been described. For the purpose of estimating the circadian phase based solely on the sleep-wake rhythm, previous values of the circadian phase can also be utilized. As described above, the circadian phase does not immediately adapt to the changed sleep-wake rhythm. Other methods can be used to estimate the circadian rhythm characteristics (especially the circadian phase).

[0055] In an embodiment, the target circadian phase is determined independently of the measured sleep-wake rhythm of the user 20. As described above, the target circadian phase is related to the location of the user, while the sleep-wake rhythm is determined based on the heart activity and / or motion measurement data measured about the user. Figure 10An embodiment is illustrated in which the target phase is determined 1001 by the light exposure of user 20 at the current location of user 20. The light exposure can be exposure to sunlight or artificial light. In the case of sunlight, the local sunlight rhythm information at the location of user 20 is used to estimate the adaptation rate. In particular, the target circadian phase is determined by using the information of the local sunlight rhythm, and since the adaptation direction relative to the target circadian phase affects the adaptation rate, the target circadian phase is used to determine the adaptation rate. The local sunlight rhythm can be defined using, for example, the user location information based on the Global Navigation Satellite System (GNSS) or through the current time zone information. The current time zone information can be retrieved, for example, from the wrist device 11 itself or from the service telecommunications network operator or from the Internet service.

[0056] In another embodiment, the light exposure of user 20 is estimated by the location in the longitude and latitude coordinates of user 20, where the light exposure is different for different latitude coordinates associated with the same longitude coordinate. In addition to the longitude coordinate, using the latitude coordinate can improve the accuracy of the target circadian phase, making the circadian phase better match the local sunlight rhythm. The light exposure estimate can also take into account the time of year together with the longitude and latitude coordinates, because the light exposure also depends on the time of year. Similarly, the coordinates can be retrieved using, for example, the GNSS-based location solution of the wrist device 11 or from the service telecommunications network operator or from the Internet service.

[0057] In another embodiment, the light exposure is determined based on the time of year. The method can take into account when there are transitions into and out of daylight saving time. The time of year information can be retrieved from the internal clock of the wrist device 11 or from the service telecommunications network operator or from the Internet service.

[0058] In another embodiment, a light sensor is used to determine the light exposure.

[0059] In another embodiment, the light exposure of user 20 is mainly estimated based on the measured light exposure, and if the measured light exposure is not available, it is estimated based on the location of user 20 in the longitude and latitude coordinates. Therefore, the measured light exposure can take precedence over the geographical location.

[0060] In the embodiment based on the sunlight rhythm at the location of user 20, the device performing Figure 2 the process can store the definition of how to convert the location and / or light exposure information into the target circadian phase. The definition can follow the logic that the user should sleep in the dark and wake up during the period of the strongest light exposure, which follows the logic of the natural circadian rhythm. However, depending on the light exposure at the location, the exact bedtime and wake-up time may vary.

[0061] Figure 11 Another embodiment is illustrated, in which if the setpoint phase is clockwise with respect to the current circadian phase, the first adaptation rate is determined 1102 as a westward adaptation rate; and if the setpoint phase is counterclockwise with respect to the current circadian phase, the first adaptation rate is determined 1103 as an eastward adaptation rate. The clockwise direction can be understood such that the setpoint phase decays (gets later) with respect to the current circadian phase (the circadian phase is decaying towards the setpoint phase), and the counterclockwise direction means that the current circadian phase advances (gets earlier) with respect to the setpoint phase (the circadian phase is advancing towards the setpoint phase). The direction of the first adaptation rate is checked at 1101. The user 20 adapts faster in a "westward" change (a change from an "earlier" time zone to a "later" time zone, e.g., from UTC to UTC-4) compared to an eastward change. The westward adaptation rate is preferably greater than the eastward adaptation rate. In an embodiment, the westward adaptation rate (clockwise direction) is 1 hour per day or about 1 hour per day, while the eastward adaptation rate (counterclockwise direction) is less than 1 hour per day, e.g., 0.5 to 0.7 hours per day. The adaptation rate away from the target circadian phase can be less than the eastward adaptation rate, e.g., less than 0.5 hours per day, such as between 0.3 and 0.4 hours per day.

[0062] If the circadian phase adaptation is away from the target circadian phase, the adaptation rate can be the same for the eastward direction as for the westward direction.

[0063] In another embodiment, the method including the measurement is performed in a wrist computer or a wrist device.

[0064] The above process of determining the circadian phase and the adaptation rate can be performed as a combined iterative process that is repeated (e.g., daily). Figure 12 An embodiment of such a process is illustrated. As described above, the setpoint phase can be calculated based on the sleep-wake rhythm of the user 20. In the case where the sleep-wake rhythm remains unchanged over a certain period such that the circadian rhythm is aligned with the sleep-wake rhythm, the setpoint phase is equal to the circadian phase. A change in the sleep-wake rhythm triggers a change in the circadian phase.

[0065] Reference Figure 12, a device (e.g., a wearable device or a user device) can maintain in 1200 a target circadian phase for the current location of user 20 (e.g., for the local time zone). In 1202, the device can maintain information about the circadian phase and setpoint based on the sleep-wake rhythm. Block 1204 evaluates the mismatch (difference) between the circadian phase and the setpoint phase. The threshold can be determined based on the adaptation rate to be, for example, between 0.3 and 1 hour. If the difference is below the determined threshold, the process can return to 1202. If the difference is above the determined threshold, adaptation of the circadian phase can be triggered and the process can continue to 1206. In 1206, the sign of the adaptation direction is determined based on the target circadian phase. This sign indicates whether the adaptation of the circadian phase is towards or away from the target circadian phase. In 1208, the adaptation rate is determined based on the direction of adaptation towards the setpoint phase (clockwise or counterclockwise) and the sign determined in 1206. If the sign indicates adaptation away from the target circadian phase, the process can proceed to 907 where the slowest adaptation rate is selected. If the sign indicates adaptation towards the target circadian phase in the counterclockwise direction, the fastest adaptation rate (1102) can be selected. If the sign indicates adaptation towards the target circadian phase in the clockwise direction, the second-fastest (or second-slowest) adaptation rate among the three adaptation rates can be selected (1103). After selecting the adaptation rate, a new circadian phase 1210 can be calculated based on the circadian phase used to detect the mismatch in 1204 and the selected adaptation rate. Thereafter, the process can return to 1202 to wait for new measurement data and a new value of the setpoint phase.

[0066] Figure 8 An embodiment of a device configured to perform at least some of the above functions in detecting the circadian rhythm of user 20 is illustrated. The device can include an electronic device that includes at least one processor 100 and at least one memory 110. The processor 100 can form a processing circuit system or be part of a processing circuit system. The device can also include a user interface 103 that includes a display screen or another display unit, input devices such as one or more buttons and / or a touch-sensitive surface, and an audio output device such as a speaker.

[0067] The processor 100 can include measurement signal processing circuitry 101 that is configured to detect the circadian rhythm of user 20 by performing Figure 9 the process or any of the above embodiments. In an embodiment, the measurement signal processing circuitry is also configured to perform Figure 2 the process or any of the above embodiments.

[0068] The device may include a communication circuitry 102 connected to a processor 100. The processor 100 may use the communication circuitry 102 to send and receive frames according to the supported wireless communication protocol. In some embodiments, the processor 100 may use the communication circuitry 102 to send data regarding the circadian rhythm, sleep data, and / or other parameters of the user 20 to another device, such as to a cloud server storing the user account of the user 20.

[0069] In an embodiment, the device includes at least one skin temperature sensor 120. Additionally, the device may communicate with at least one skin contact sensor 121. The skin contact sensor 121 may use an optical or bioimpedance method. In an embodiment, the device includes a light sensor 122 to measure light exposure and further determine the diurnal rhythm of the location of the user 20.

[0070] In an embodiment, the device may include a global positioning system or circuitry 104 (such as a global positioning system or another satellite navigation system) to provide location information for determining the location of the user 20. The device may also receive location information from the Internet using the communication circuitry 102.

[0071] The memory 110 may store a computer program product 111 circadian rhythm detection algorithm that the processor executes when reading the computer program. The memory may also store a user profile 113 of the user 20 that stores the personal characteristics of the user 20. The memory may also store a measurement database 112 that includes the measurement history of the sleep-wake rhythm, circadian rhythm, and the preferences or plans of the user 20 related to scheduled events (such as cross-time zone travel, daylight saving time transition, or shift work schedule).

[0072] As used in this application, the term "circuitry" refers to all of the following: (a) only hardware circuit implementations, such as implementations only in analog and / or digital circuitry; and (b) combinations of circuits and software (and / or firmware), such as, where applicable: (i) combinations of one or more processors; or (ii) portions of one or more processors / software, including one or more digital signal processors, software, and one or more memories, which work together to enable a device to perform various functions; and (c) circuits, such as one or more microprocessors or portions of one or more microprocessors, which require software or firmware for operation, even if the software or firmware is not physically present. This definition of "circuitry" applies to all uses of this term in this application. As a further example, as used in this application, the term "circuitry" will also cover implementations consisting of only one processor (or more processors) or a portion of a processor and software and / or firmware attached to that processor (or those processors). For example and where applicable to a particular element, the term "circuitry" will also cover a baseband integrated circuit or an application processor integrated circuit for a mobile phone, or a similar integrated circuit in a server, a cellular network device, or other network device.

[0073] In an embodiment, at least some of the processes described in conjunction with Figure 2-4 , 6 - 7, and 9 - 12 can be performed by a device including corresponding components for performing at least some of the described processes. Some example components for performing these processes can include at least one of the following: a detector, a processor (including dual - core and multi - core processors), a digital signal processor, a controller, a receiver, a transmitter, an encoder, a decoder, a memory, a RAM, a ROM, software, firmware, a display, a user interface, display circuitry, user interface circuitry, user interface software, display software, circuits, and circuitry. In an embodiment, the at least one processor 100, the memory 110, and the computer program code 118 form a processing component, or include one or more portions of computer program code, for performing one or more operations in accordance with Figure 2-4 , any one of the embodiments in 6 - 7 and 9 - 12, or the operations of these embodiments.

[0074] The techniques and methods described herein can be implemented by various components. For example, these techniques can be implemented in hardware (one or more devices), firmware (one or more devices), software (one or more modules), or a combination thereof. For a hardware implementation, the (one or more) apparatuses in the embodiments can be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic units designed to perform the functions described herein, or a combination of these. For firmware or software, the implementation can be performed by modules (e.g., procedures, functions, etc.) of at least one chipset that perform the functions described herein. The software code can be stored in a memory unit and executed by a processor. The memory unit can be implemented inside or outside the processor. In the latter case, it can be communicatively coupled to the processor via various components known in the art. Additionally, the components of the systems described herein can be rearranged and / or supplemented by additional components to facilitate the effects, etc. of the various aspects described regarding them, and they are not limited to the exact configurations shown in a given figure, as will be understood by those skilled in the art.

[0075] The described embodiments can also be executed in the form of a computer process defined by a computer program or a part thereof. Embodiments of the methods described in conjunction with Figure 2-4 、6 - 7 and 9 - 12 can be executed by performing at least a part of a computer program that includes corresponding instructions. The computer program can be in source code form, object code form, or some intermediate form, and the computer program can be stored in some kind of carrier, which can be any entity or device capable of carrying the program. For example, the computer program can be stored on a computer program distribution medium readable by a computer or a processor. For example, the computer program medium can be (e.g., but not limited to) a recording medium, a computer memory, a read only memory, an electrical carrier signal, a telecommunication signal, and a software distribution package. The computer program medium can be a non - transient medium. The software coding for performing the embodiments shown and described is entirely within the scope of those of ordinary skill in the art.

[0076] It will be clear to those skilled in the art that, as technology advances, the inventive concept can be implemented in various ways. The present invention and its embodiments are not limited to the above examples, but can vary within the scope of the claims.

Claims

1. An adaptation method for estimating a user's circadian rhythm, the method comprising: Measuring the user's circadian phase by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; Measuring the user's sleep-wake rhythm by using at least one of the heart activity sensor and the motion sensor, and detecting a change in the user's sleep-wake rhythm based on the measurement; Determining a set point phase of the circadian phase based on the changed sleep-wake rhythm; Determining a target circadian phase based on the user's location; If the set point phase and the target phase are in the same direction relative to the current circadian phase, determining a circadian adaptation rate based on a first adaptation rate, and If the set point phase and the target phase are in opposite directions from the current circadian phase, determining the circadian adaptation rate based on a second adaptation rate, wherein the second adaptation rate is less than the first adaptation rate; And Outputting via an interface a determined circadian adaptation rate or a derived parameter of a calculated circadian adaptation rate to be presented to the user.

2. The method according to claim 1, wherein the target circadian phase is determined independently of the user's measured sleep-wake rhythm.

3. The method according to claim 2, wherein the target phase is determined by the user's light exposure at the user's current location.

4. The method according to claim 3, wherein the user's light exposure is estimated by a position in the user's longitude coordinates and latitude coordinates, wherein for different latitude coordinates associated with the same longitude coordinate, the light exposure is different.

5. The method according to claim 4, wherein the light exposure is further determined based on the time of year.

6. The method according to any one of the preceding claims 3-5, further comprising using a light sensor to measure the user's light exposure.

7. The method according to claim 6 when dependent on claim 4, wherein the user's light exposure is estimated based on the measured light exposure, and if the measured light exposure is not available, is estimated based on a position in the user's longitude coordinates and latitude coordinates.

8. The method according to any one of the preceding claims, wherein, If the set point phase is in a counterclockwise direction relative to the current circadian phase, the first adaptation rate is a westward adaptation rate; and if the set point phase is in a clockwise direction relative to the current circadian phase, the first adaptation rate is an eastward adaptation rate, wherein the eastward adaptation rate is less than the westward adaptation rate.

9. The method according to any one of the preceding claims, wherein the method including the measurement is executed in a wrist computer.

10. An apparatus for estimating an adaptation of a user's circadian rhythm, comprising: Measuring the user's circadian phase by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; Measuring the user's sleep-wake rhythm by using at least one of the heart activity sensor and the motion sensor, and detecting a change in the user's sleep-wake rhythm based on the measurement; Determine a set point phase of the circadian phase based on a changed sleep-wake rhythm; Determine a target circadian phase based on the user's location; If the set point phase and the target phase are in the same direction relative to the current circadian phase, determine a circadian adaptation rate based on a first adaptation rate, and If the set point phase and the target phase are in opposite directions from the current circadian phase, determine the circadian adaptation rate based on a second adaptation rate, where the second adaptation rate is less than the first adaptation rate; And Output, via an interface, the determined circadian adaptation rate or a derived parameter of the calculated circadian adaptation rate to be presented to the user.

11. The apparatus according to claim 10, comprising components for performing the method according to any one of the preceding claims 2-8.

12. A wrist computer, comprising the apparatus according to claim 10 or 11, and an attachment mechanism configured to attach the apparatus to the wrist.

13. A computer program product embodied on a computer-readable distribution medium and comprising instructions which, when loaded into a device, perform: Measure the user's circadian phase by using at least one of a skin temperature sensor, a heart activity sensor, and a motion sensor; Measure the user's sleep-wake rhythm by using at least one of the heart activity sensor and the motion sensor, and detect a change in the user's sleep-wake rhythm based on the measurement; Determine a set point phase of the circadian phase based on the changed sleep-wake rhythm; Determine a target circadian phase based on the user's location; If the set point phase and the target phase are in the same direction relative to the current circadian phase, determine a circadian adaptation rate based on a first adaptation rate, and If the set point phase and the target phase are in opposite directions from the current circadian phase, determine the circadian adaptation rate based on a second adaptation rate, where the second adaptation rate is less than the first adaptation rate; And Output, via an interface, the determined circadian adaptation rate or a derived parameter of the determined circadian adaptation rate to be presented to the user.

14. The computer program product according to claim 13, embodied on a computer-readable distribution medium and comprising instructions which, when loaded into a device, perform the method according to any one of the preceding claims 2-9.

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