Circadian rhythm detection

Abstract

A method for detecting a circadian rhythm of a user, the method comprising: acquiring measurement data indicative of a sleep time of the user, and determining a sleep-wake rhythm of the user based on the measurement data; determining a first phase value based on a sleep-wake rhythm, acquiring skin temperature measurement data for at least one 24-hour measurement period; the skin temperature measurement data is fitted to a cosine waveform and an evaluation of the fitting quality is performed and a second phase value is determined based on the fitting if the quality is above a threshold, a diurnal phase of the user is determined based on a combination of the first phase value and the second phase value if the fitting quality is above a threshold, and a diurnal phase of the user is determined based on a combination of the first phase value and the second phase value if the fitting quality is below a threshold. Determining a diurnal phase of the user based on the first phase value and not based on the second phase value; and outputting the calculated diurnal phase or a parameter derived from the calculated diurnal phase via an interface to present to a user.

Description

Technical Field

[0001] The present invention relates to detecting a user's circadian rhythm, i.e., the user's sleep-wake rhythm. Background Art

[0002] The circadian rhythm (equivalent to the day-night process, the 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 a circadian phase, which can be understood as the timing that reflects the different states of the circadian rhythm during the (calendar) day. Different people have different natural circadian phases, and many daily factors affect a person's ability and willingness to follow the natural circadian phase. In terms of the natural circadian phase, one person may be an "early bird", while another person may be a "night owl". A night owl may have a job that forces them to wake up earlier than their natural waking time, while an early bird may have social activities that extend their bedtime beyond their natural bedtime. Due to the natural circadian phase and the fact that the user can affect the change of the natural circadian phase, it is crucial to be able to measure the circadian phase. The automated measurement and detection of the circadian phase can be used as the basis for intelligent guidance features that are beneficial to the user's health. Summary of the Invention

[0003] According to one aspect, there is a method for detecting a user's circadian rhythm, the method comprising: obtaining measurement data indicating the user's sleep time, and determining the user's sleep-wake rhythm based on the measurement data; determining a first phase value based on the sleep-wake rhythm; obtaining skin temperature measurement data for at least one 24-hour measurement period; fitting the skin temperature measurement data to a cosine waveform, performing an evaluation of the fitting quality, and, if the quality is higher than a threshold, then determining a second phase value based on the fitting; if the fitting quality is higher than the threshold, then determining the user's circadian phase based on a combination of the first phase value and the second phase value; if the fitting quality is lower than the threshold, then determining the user's circadian phase based on the first phase value and not based on the second phase value; and outputting via an interface the calculated circadian phase or a parameter derived from the calculated circadian phase for presentation to the user.

[0004] According to one aspect, there is a device for detecting a user's circadian rhythm, the device comprising: obtaining measurement data indicating the user's sleep time, and determining the user's sleep-wake rhythm based on the measurement data; determining a first phase value based on the sleep-wake rhythm; obtaining skin temperature measurement data for at least one 24-hour measurement period; fitting the skin temperature measurement data to a cosine waveform and performing an evaluation of the fitting quality, and if the quality is higher than a threshold, then determining a second phase value based on the fitting; if the fitting quality is higher than the threshold, then determining the user's circadian phase based on a combination of the first phase value and the second phase value; if the fitting quality is lower than the threshold, then determining the user's circadian phase based on the first phase value and not based on the second phase value; and presenting to the user, via an interface, the calculated circadian phase or a parameter derived from the calculated circadian phase.

[0005] According to one aspect, there is a computer program product embodied on a computer-readable distribution medium and comprising instructions which, when loaded into a device, perform detecting a user's circadian rhythm, the computer program product comprising: obtaining measurement data indicating the user's sleep time, and determining the user's sleep-wake rhythm based on the measurement data; determining a first phase value based on the sleep-wake rhythm; obtaining skin temperature measurement data for at least one 24-hour measurement period; fitting the skin temperature measurement data to a cosine waveform and performing an evaluation of the fitting quality, and if the quality is higher than a threshold, then determining a second phase value based on the fitting; if the fitting quality is higher than the threshold, then determining the user's circadian phase based on a combination of the first phase value and the second phase value; if the fitting quality is lower than the threshold, then determining the user's circadian phase based on the first phase value and not based on the second phase value; and presenting to the user, via an interface, the calculated circadian phase or a parameter derived from the calculated circadian phase. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The present invention will be described in more detail below by way of preferred embodiments with reference to the accompanying drawings.

[0007] In the drawings:

[0008] Figure 1 illustrates an example system to which embodiments of the present invention can be applied;

[0009] Figure 2 illustrates an embodiment of the method;

[0010] Figure 3 illustrates an embodiment of the method in which corresponding skin contact measurement data indicating non-contact of the skin is excluded from the cosine waveform fitting;

[0011] such skin temperature measurement data that does not contact the skin is excluded from the cosine waveform fitting;

[0012] Figure 4Illustrates an embodiment of a method, in which the measured skin temperature is checked according to a first threshold

[0013] is checked;

[0014] Figure 5 Illustrates the change in the phase of the cosine waveform;

[0015] Figure 6 Illustrates another embodiment of a method, in which the circadian phase is selected between a first phase value and

[0016] a combination of the first phase value and a second phase value;

[0017] Figure 7 Illustrates an embodiment of determining the circadian phase via the sleep-wake rhythm; and

[0018] Figure 8 Illustrates an embodiment of a device for detecting the circadian rhythm; Detailed Description

[0019] The following embodiments are for illustration only. Although the present specification may mention "one", "a" or "some" embodiments in several places in the text, this does not necessarily mean that each mention refers to the same (one or more) embodiments, nor does it mean that a particular feature applies only to a single embodiment. The individual features of different embodiments can also be combined to provide other embodiments.

[0020] Figure 1Illustrated is a measurement system including a sensor device 12, which can be used in the context of some embodiments of the present invention. A user 20 can wear a wearable device, such as a wrist device 11. The wrist device 11 can 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 device 11 is an activity tracking device or a wearable activity tracking device. This can mean that the device can be worn on the wrist of the user 20 or other parts, such as but not limited to the forearm, the biceps area, the neck, the forehead, and / or the leg. The data retrieved from the wrist device 11 can also be used to determine the user's 20 sleep start time and sleep end time, as well as the 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, the body core temperature represents the circadian rhythm in great detail, and it peaks during the day and is lowest at night. Therefore, the circadian rhythm can be measured by measuring the body core temperature. The wrist skin temperature, on the other hand, presents the opposite pattern, being lowest during the day and highest at night. However, they both follow the same rhythm, and therefore, the circadian rhythm can also be measured by the skin temperature. A skin temperature sensor can be implemented in the wrist device 11 as one of the sensors 12 of the wrist device 11.

[0021] In Figure 1 the example illustrated, the measurement system includes at least a processing circuitry configured to analyze measurement data 13 measured from the user 20, for example, by performing the methods described in more detail below. The processing circuitry can be implemented in the wrist device 11 worn by the user, or the processing circuitry can be implemented in the user device 10 (such as a smartphone or a tablet computer), or the processing circuitry can be implemented in a server computer (such as a cloud server). The measurement data 13 can be provided by at least one sensor device 12, which can be included in the wrist device 11, or the sensor device 12 can be located outside the wrist device 11 but be provided with data transfer capabilities with the wrist device 11. The wrist device 11, the user device 102, the server computer, and / or the sensor device 12 can be connected via one or more networks, via a short-range wireless connection (such as Bluetooth), or via a Universal Serial Bus (USB) connection.

[0022] 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 also measure skin contact using optical or bioimpedance methods such that those temperature samples measured when the sensor is not worn can be detected and optionally excluded from the circadian rhythm estimation. The sensor device 12 may measure one or more of the following characteristics from the user: motion, electrocardiogram (ECG), photoplethysmogram (PPG), bioimpedance, skin conductance response, body temperature. To measure motion, the sensor device 12 may include inertial sensors (such as accelerometers and / or gyroscopes), or magnetometers, or any sensor fusion as any combination of these motion sensors, and the sensor device 12 may output motion 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 electrical properties from the skin, which can be processed into an ECG signal, a bioimpedance signal, or a skin conductance response signal by appropriate signal processing techniques. In some techniques, the cardiac activity measurement data 13 may represent the occurrence of the R wave of the electrocardiographic pulse. 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 components from the electrical measurement signal. In PPG measurement, the P wave may be detected, which enables the calculation of, for example, the PP interval and the heart rate.

[0023] The user device 10 refers to a computing device (equipment, device), and it may also be referred to as a user terminal, a user device, a mobile device, or a 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 form, including but not limited to the following types of devices: mobile phones, smartphones, personal digital assistants (PDAs), handheld devices, laptop computers and / or touchscreen computers, tablets (tablet computers), multimedia devices, wearable computers (such as smartwatches), 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, a keyboard, a speaker, a microphone, a touch user interface, an integrated display device, and / or an external display device.

[0024] Figure 2An embodiment in which there is a method for detecting the circadian rhythm of user 20 is illustrated, where the method includes: obtaining measurement data indicating sleep time at 201, and determining the sleep-wake rhythm of user 20 based on the measurement data, and further determining a first phase value at 202 based on the sleep-wake rhythm, and further obtaining skin temperature measurement data for at least one 24-hour measurement period at 203, and further fitting the skin temperature measurement data to a cosine waveform at 204, and performing an evaluation of the fitting quality at 205, and, if the quality is higher than a threshold, then determining a second phase value at 207 based on the fitting, and if the fitting quality is higher than the threshold, then determining the circadian phase of user 20 at 208 as a combination of the first phase value and the second phase value, and if the fitting quality is lower than the threshold, then determining the circadian phase of user 20 at 206 based on the first phase value, and outputting at 209 the calculated circadian phase or a parameter derived from the calculated circadian phase via an interface for presentation to user 20. The second phase value can also be determined immediately after fitting at 204 and before the evaluation of the fitting at 205.

[0025] The measurement data obtained in block 201 can be obtained by using at least one of the heart activity sensor and the motion sensor in the sensor device 12. Detection of the sleep start time and the sleep end time can 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 generally known per se, and there are several commercially available products for performing such detection, for example, the Polar Vantage series from Polar Electro.

[0026] Figure 2 The embodiment provides the advantage of verifying the second phase value based on skin temperature measurement. Measuring skin temperature requires skin contact between the sensor device 12 and user 20. The user may desire that the sensor device 12 be comfortable to wear and not press the sensor device 12 against the skin. The disadvantage 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 it is common to fit skin temperature measurement data with a cosine function (also referred to as cosine analysis or cosine fitting in the literature) for circadian phase detection, the fitting may fail for several reasons. Therefore, although the second estimate of the circadian phase improves the accuracy of the circadian phase estimate, it is advantageous to verify it before combining it with the first estimate.

[0027] During Figure 2 the process, the first phase value is used to estimate the circadian phase, either alone or in combination with the second phase value. If the quality of the second phase value is determined to be high enough, for example, considered to be a successful fitting, then the second phase value can be calculated, or the calculated phase value can be considered suitable for combination.

[0028] 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.

[0029] In an embodiment, the cosine fitting in block 204 is performed by using least squares (LS) fitting known in the art. The LS fitting finds the cosine waveform (phase) that provides the minimum square error for the measured data. Then, the phase of the fitted cosine waveform represents the second phase value.

[0030] In an embodiment, Figure 2 the process includes excluding such skin temperature measurement data indicating non-contact with the skin from the cosine waveform fitting for the corresponding skin contact measurement data. Figure 3 This embodiment is illustrated, where skin contact measurement data is acquired 301 together with skin temperature measurement data over at least one 24-hour measurement period, and such skin temperature measurement data indicating non-contact with the skin in the skin contact measurement data is excluded 302 from the cosine waveform fitting in 204. As described above, since the user 20 has a period of time during the measurement period when the device is not worn at all, or the device does not actually contact the skin of the user 20, a part of the skin temperature measurement data may be lost. The temperature measurement data associated with the lack of skin contact may indicate an ambient temperature that degrades the fitting performance.

[0031] 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 the process exclusion 401 will not fit the skin temperature measurement data to a cosine waveform. In an embodiment, the valid skin temperature measurement data covers at least 80% of the most skin temperature measurement data. This means that there should be skin contact for at least 80% of the measurements within a 24 - hour period. For example, if the sampling rate of temperature measurement is one measurement every five minutes (288 measurement samples per 24 - hour period), then there should be at least 231 verified measurement samples per 24 - hour period. Less than 80% of the skin temperature measurement data can be considered to reduce the reliability of using skin temperature measurement for circadian rhythm detection. In other embodiments, a lower percentage can be allowed, depending on how much error is tolerated and how accurate the first phase value is determined. According to the Nyquist criterion, at least two samples are required per 24 - hour period, but in practice, to improve the accuracy of the second estimate, it is preferred to have a larger number of measurement samples per 24 - hour period. Instead of a percentage value, an absolute value of the minimum number of verified measurements detecting skin contact can be defined. The absolute value can be, for example, 50, 100, 150, or 200 per 24 - hour period.

[0032] In another embodiment, the skin temperature measurement is verified by evaluating the cosine fitting performance of block 204. This can be done by arranging a phase shift between the measurement data and the cosine waveform after 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 if there is a better fit than the fit 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 a cosine waveform. In this embodiment, evaluating the fit quality includes the following steps:

[0033] · Determine a first corresponding metric of the skin temperature measurement data relative to the cosine waveform 500 after fitting. The first corresponding metric indicates the error between the skin temperature measurement data and the cosine waveform 500 after fitting.

[0034] · 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.

[0035] · If the second corresponding metric (and additional corresponding metric(s)) indicates a greater error than the first corresponding metric, then determine that the fit quality is above the threshold, and if the second corresponding metric (or at least one of the additional corresponding metric(s)) indicates a smaller error than the first corresponding metric, then determine that the fit quality is below the threshold.

[0036] In other words, if the phase shift results in a fit associated with a lower fitting error, then 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 shifted cosine waveform can be calculated. When the phase of the cosine waveform changes, the RMSE should increase, 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 can vary widely between different users, but the key is that the RMSE should increase when the fitted phase value changes forward or backward. Quality assessment can also be performed using different calculations of the corresponding metric, such as the mean absolute error (MAE). The corresponding metric is preferably different from the metric used for fitting in block 204.

[0037] If there are too few temperature samples of verified skin contact available, or if the quality assessment analysis of the fitted second phase value (e.g., based on the RMSE calculation) indicates that the fit is not very accurate, then the result from the second phase value can be ignored or its calculation can be omitted.

[0038] Figure 6Illustrated is another embodiment for verifying a second phase value, where the method further includes checking 601 that if the difference between the first phase value and the second phase value is less than a second threshold, then the circadian phase is determined 602 as a combination of the first phase value and the second phase value. The combination can be an average value or a weighted average value 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. The maximum limit of the offset can have been defined as, for example, four hours. The offset can be distinguished from the averaging 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, then 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. The range of example values of 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 where the first phase value is offset based on the difference, if the second threshold is 10 hours and the maximum offset is 4 hours, then 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 and the second phase value are equal, then no offset of the phase value is performed. The offset can be mapped in a linear or non-linear manner when the difference is between zero and 10 hours.

[0039] 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 of these phase values indicate the circadian phase, due to different measurement data and different physiological characteristics being measured, they are essentially in different domains. Therefore, the embodiment includes an offset constant value for one of the first phase value and the second phase value such that the first phase value and the second phase value are comparable.

[0040] Conventional methods for estimating the circadian phase from the sleep-wake rhythm are based on determining the midpoint of sleep as the midpoint between the detected sleep start time and sleep end time from the measurement data. When the sleep rhythm of user 20 is regular, this method provides an accurate circadian phase estimate. However, if the user reduces the sleep time by going to bed later and waking up at the same time, then the midpoint of sleep is delayed and the circadian phase is also shifted (delayed). The problem is that in this case, the circadian phase of user 20 has not actually changed, but the user is sleep deprived. To address the problem of user 20 changing his / her sleep time (e.g., reducing the amount of sleep but waking up at the same time), the embodiment omits the sleep start time from the estimation of the first phase value. Alternatively, the first phase value is calculated based on the target sleep time (which can be estimated, for example, using a sleep assistance algorithm, general sleep amount guidelines, or received as user input for the desired sleep amount) and the sleep end time to estimate what the midpoint of sleep should be if user 20 had sufficient sleep before waking up. This method provides a more accurate circadian phase estimate in cases where the user is sleep deprived 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, further obtaining the target sleep end time of user 20, determining the midpoint of sleep of user 20 based on the sleep end time and the target sleep time, and calculating the sleep-wake rhythm based on the midpoint of sleep. Figure 7 FIG. illustrates a flowchart of an embodiment for calculating the midpoint of sleep for estimating the circadian rhythm. Refer to Figure 7 , sleep measurement data is obtained at 700. The sleep measurement data may include heart activity measurement data and / or motion measurement data, which enable the detection of at least the sleep end time. At 702, the sleep end time and the target sleep time are determined. The target sleep time may be pre-stored in the memory, and the sleep end time can be determined from the sleep measurement data obtained at 700. At 704, according to the following formula, the midpoint of sleep can be determined as the midpoint between the sleep end time and the sleep end time minus half of the target sleep time:

[0041] Sleep midpoint estimate = sleep end time - target sleep time / 2 (1)

[0042] The purpose of calculating the circadian phase can be to provide the user with information related to the circadian 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 user's natural bedtime. If the user follows his / her natural rhythm, then health benefits can be obtained. Another example of a parameter derived from the circadian rhythm is the chronotype of the user 20. The calculation of the circadian phase enables the classification of the user 20's sleep chronotype based on the circadian phase and the output of the sleep chronotype as a parameter via the interface. The chronotype classification can be based on observing the circadian phase of the user 20 over a long period, such as at least one week. For example, the circadian phase of a night owl may be delayed relative to that of an early bird. Yet another example of a parameter derived from the circadian phase is an alert of reduced alertness output to the user. For example, if the circadian phase estimate indicates that the user 20's circadian rhythm has changed due to cross-time zone travel or shift work, then the user can be reminded of the consequences in the form of reduced alertness. For example, the above-listed parameters can be displayed to the user via the user interface of the wrist device 11 or the user device 10.

[0043] The above-described embodiments for determining the circadian phase can be used to directly infer the circadian phase or infer the set point of the circadian phase. If the user has a regular daily rhythm (including a regular sleep cycle) by going to bed and waking up at basically the same time every day, then the above-described embodiments can directly indicate the circadian phase of the user 20. However, if the user has changed his / her sleep-wake rhythm due to cross-time zone travel, shift work, etc., then the circadian phase takes time to adapt to the new sleep-wake rhythm. In this case, the above-described embodiments can define a set point for the circadian phase, and the circadian phase approaches this set point at a certain daily adaptation rate. In this case, the circadian phase today can be determined based on the set point, the adaptation rate, and the circadian phase of yesterday. Therefore, the above-described embodiments can be used to determine the circadian phase when the circadian phase is adapting. The adaptation rate can be a constant, or it can be a function of the clockwise (delayed) or counterclockwise (advanced) offset of the circadian phase. The clockwise adaptation rate (e.g., one hour per day) can be greater than the counterclockwise adaptation rate (e.g., 0.67 hours per day).

[0044] Figure 8The figure illustrates an embodiment of a device configured to perform at least some of the functions of detecting the circadian rhythm of user 20 as described above. The device may include an electronic device that includes at least one processor 100 and at least one memory 110. The processor 100 may form or be part of a processing circuitry. The device may also include a user interface 103 (including a display screen or another display unit), an input device (such as one or more buttons and / or a touch-sensitive surface), and an audio output device (such as a speaker).

[0045] The processor 100 may include a measurement signal processing circuitry 101 configured to detect the circadian rhythm of user 20 by performing Figure 2 the processes or any one of the embodiments described herein.

[0046] The device may include a communication circuitry 102 connected to the processor 100. The processor 100 may use the communication circuitry 102 to transmit and receive frames according to supported wireless communication protocols. In some embodiments, the processor 100 may use the communication circuitry 102 to transmit data about the circadian rhythm, sleep data, and / or other parameters of user 20 to another device, for example, to a cloud server storing the user account of user 20.

[0047] In an embodiment, the device includes at least one skin temperature sensor 120. Additionally, the device may include at least one skin contact sensor 121. The skin contact sensor 121 may use optical or bioimpedance methods. In an embodiment, the device includes a light sensor 122 for measuring the amount of light and further determining the location of user 20 for the circadian rhythm of daylight.

[0048] 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 (generally a global navigation satellite system, GNSS), to provide location information for determining the location of user 20. The device may also receive location information from the Internet using the communication circuitry 102.

[0049] The memory 110 may store a computer program product 111, and after the processor reads the computer program, it executes a circadian rhythm detection algorithm. The memory may also store a user profile 113 of user 20, which stores the personal characteristics of user 20. The memory may also store a measurement database 112 that contains the measurement history of the sleep-wake rhythm, circadian rhythm, and the preferences or schedules of user 20 related to events such as traveling across time zones, daylight saving time conversion, or shift work schedules.

[0050] As used in this application, the term "circuitry" refers to all of the following: (a) an implementation of pure hardware circuitry, such as an implementation that uses only analog and / or digital circuitry; and (b) a combination of circuitry and software (and / or firmware), such as, if applicable: (i) a combination 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 cause a device to perform various functions; and (c) circuitry that requires software or firmware to operate, such as (one or more) microprocessors or portions of (one or more) microprocessors, even if such software or firmware is not physically present. This definition of "circuitry" applies to all uses of the term in this application. As a further example, as used in this application, the term "circuitry" will also cover an implementation of only a processor (or processors) or a portion of a processor and its accompanying software and / or firmware. For example, and if 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.

[0051] In an embodiment, at least some of the processing described in connection with Figures 2 - 7 can be performed by a device including corresponding components for performing at least some of the described processing. 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, circuitry, and circuitry systems. In an embodiment, at least one processor 100, a memory 110, and 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 Figures 2 - 4 and Figure 6 any one embodiment or its operation in

[0052] 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 of the embodiments can be implemented within one or more application specific integrated circuit systems (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. For firmware or software, the implementation can be carried out 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 within the processor or can be implemented external to the processor. In the latter case, it can be communicatively coupled to the processor via various components, as is known in the art. Additionally, the components of the systems described herein can be rearranged and / or supplemented with additional components to facilitate the various aspects described herein, etc., and they are not limited to the exact configurations illustrated in the given figures, as will be recognized by those skilled in the art.

[0053] The described embodiments can also be executed in the form of computer processing defined by a computer program or a part thereof. In combination with Figures 2 - 4 and Figure 6 Embodiments of the methods described 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 it can be stored in some 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. The computer program medium can be, for example 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-transitory medium. The software coding for performing the illustrated and described embodiments is entirely within the scope of those of ordinary skill in the art.

[0054] It will be apparent to those skilled in the art that, as technology progresses, 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. A method for detecting a user's circadian rhythm, the method comprises: obtaining measurement data indicating the user's sleep time, and determining the user's sleep-wake rhythm based on the measurement data; determining a first phase value based on the sleep-wake rhythm; obtaining skin temperature measurement data for at least one 24-hour measurement period; fitting the skin temperature measurement data to a cosine waveform, and performing an evaluation of the fitting quality, and if the quality is higher than a threshold, then determining a second phase value based on the fitting; if the fitting quality is higher than the threshold, then determining the user's circadian phase based on a combination of the first phase value and the second phase value; if the fitting quality is lower than the threshold, then determining the user's circadian phase based on the first phase value and not based on the second phase value; and outputting via an interface the calculated circadian phase or a parameter derived from the calculated circadian phase for presentation to the user.

2. The method according to claim 1, further comprises: obtaining skin contact measurement data together with the skin temperature measurement data for the at least one 24-hour measurement period; excluding from the cosine waveform fitting such skin temperature measurement data for which the corresponding skin contact measurement data indicates no skin contact.

3. The method according to claim 2, further comprises: if, during the at least one 24-hour skin temperature measurement period, the amount of skin temperature measurement data associated with skin contact measurement data indicating skin contact is less than a first threshold, then excluding the fitting of the skin temperature measurement data.

4. The method according to claim 3, wherein the first threshold is 80% of the skin temperature measurement data.

5. The method according to any one of the preceding claims, wherein the performing the evaluation of the fitting quality comprises: determining a first corresponding metric of the skin temperature measurement data relative to the cosine waveform after the fitting; arranging a phase shift between the cosine waveform and the skin temperature measurement data fitted to the cosine waveform, and determining a second corresponding metric of the skin temperature measurement data relative to the cosine waveform after the phase shift; if the second corresponding metric indicates an error greater than the first corresponding metric, then determining that the fitting quality is higher than the threshold, and if the second corresponding metric indicates an error less than the first corresponding metric, then determining that the fitting quality is lower than the threshold.

6. The method according to claim 5, wherein the first corresponding metric represents the root mean square error or the mean absolute error between the skin temperature measurement data and the cosine waveform after the fitting; and the second corresponding metric represents the root mean square error or the mean absolute error between the skin temperature measurement data and the cosine waveform after the phase shift.

7. The method according to any one of the preceding claims, further comprises: if the difference between the first phase value and the second phase value is less than a second threshold, then determining the user's circadian phase as a combination of the first phase value and the second phase value, and if the difference between the first phase value and the second phase value is greater than the second threshold, then determining the user's circadian phase as the first phase value.

8. The method according to claim 7, wherein the combination is performed by shifting the first phase value towards the second phase value in linear or non-linear proportion to the difference between the first phase value and the second phase value.

9. The method according to any one of the preceding claims, further comprises: 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.

10. The method according to any one of the preceding claims, further comprises: obtaining a sleep end time from measurement data indicating a user's sleep time, and further obtaining a target sleep time of the user, determining a sleep midpoint of the user based on the sleep end time and the target sleep time, and calculating a sleep-wake rhythm based on the sleep midpoint.

11. The method according to claim 9, wherein the sleep midpoint is determined as the midpoint between the sleep end time and the sleep end time minus the target sleep time.

12. The method according to any one of the preceding claims, further comprises: classifying the user's chronotype based on the circadian phase, and outputting the chronotype as a parameter via an interface.

13. An apparatus for detecting a user's circadian rhythm, comprises: obtaining measurement data indicating a user's sleep time, and determining the user's sleep-wake rhythm based on the measurement data; determining a first phase value based on the sleep-wake rhythm; obtaining skin temperature measurement data for at least one 24-hour measurement period; fitting the skin temperature measurement data to a cosine waveform, and performing an evaluation of the fitting quality, and if the quality is higher than a threshold, then determining a second phase value based on the fitting; if the fitting quality is higher than the threshold, then determining the user's circadian phase as a combination of the first phase value and the second phase value; if the fitting quality is lower than the threshold, then determining the user's circadian phase based on the first phase value and not based on the second phase value; and outputting the calculated circadian phase or a parameter derived from the calculated circadian phase via an interface for presentation to the user.

14. The apparatus according to claim 13, comprising components for performing the method according to any one of the preceding claims 2-12.

15. A computer program product, the computer program product being embodied on a computer-readable distribution medium and comprising instructions which, when loaded into an apparatus, perform: detecting a user's circadian rhythm, comprising: obtaining measurement data indicating a user's sleep time, and determining the user's sleep-wake rhythm based on the measurement data; determining a first phase value based on the sleep-wake rhythm; obtaining skin temperature measurement data for at least one 24-hour measurement period; fitting the skin temperature measurement data to a cosine waveform, and performing an evaluation of the fitting quality, and if the quality is higher than a threshold, then determining a second phase value based on the fitting; if the fitting quality is higher than the threshold, then determining the user's circadian phase as a combination of the first phase value and the second phase value; if the fitting quality is lower than the threshold, then determining the user's circadian phase based on the first phase value and not based on the second phase value; and outputting the calculated circadian phase or a parameter derived from the calculated circadian phase via an interface for presentation to the user.

16. A computer program product according to claim 15, the computer program product being 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 2-12.

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