Patent Description:
It is known that sleep disorders such as insomnia, sleep-disordered breathing, and hypersomnia can be detrimental to health. In order to grasp the sleeping condition of a person, it is necessary to examine the actual condition of how the person actually sleeps, from one night to several days.

The all-night polysomnography (PSG) test, proposed in Patent Document <NUM> and the like, has been developed as a test to examine a person's sleep state. In PSG, a large number of electrodes and sensors are attached to the body of a subject, and each electrode and sensor is connected to a special measurement device to measure basic data such as electroencephalogram, electrocardiogram, electromyogram, and respiratory status, and then the state of sleep and wakefulness is examined based on the basic data. In Patent Document <NUM>, a device is introduced that recognizes the sleep state of a subject based on the respiratory motion state and the body motion state of the subject, which are determined through a set of time-series measurements performed with a wearable sensor. In Patent Document <NUM>, a machine-learning aided method is introduced to measure the quality of sleep of a subject through sleep staging, which is achieved through the measurement and processing of at least a physiological signal (e.g. an electrocardiogram) obtained with a wireless sensor. In Patent Document <NUM>, a system and method to determine the sleep state of a subject is provided. The determination is performed by processing the data obtained by an accelerometer embedded in a device worn by the user.

A sleep test using PSG, such as that described in Patent Document <NUM>, requires a large number of measurement devices and the location thereof is limited to hospitals and laboratories. This makes it difficult for many people to easily perform the test for longer than a few days. In addition, wearing a lot of electrodes and sensors on the body in an environment different from home causes stress and makes it difficult to sleep. As a result, it is difficult to test the normal sleep state correctly.

In light of the above circumstances, the present invention provides a sleep-wakefulness determination apparatus and program, which determine sleep-wakefulness with a sufficiently high level of accuracy using a small number of wearing devices.

According to one aspect of the present invention, there is provided a sleep-wakefulness determination apparatus configured to determine sleep and wakefulness of a user. The present sleep-wakefulness determination apparatus comprises a scalar value calculation unit, a feature value calculation unit, and a sleep-wakefulness determination unit. The scalar calculation unit is configured to calculate scalar value based on each component of the acceleration vector in a part of a body of the user. The feature value calculation unit is configured to calculate feature value for each epoch defined by a predetermined time based on the scalar value. The sleep-wakefulness determination unit is configured to determine the sleep and wakefulness of the user based on the feature value of a desired epoch and the feature value of peripheral epochs included in a plurality of epochs before and after the desired epoch in a time series. The apparatus is characterized in that the scalar value is an L2 norm based on each component of a time difference vector that is a difference vector of two of the acceleration vectors in a time series, the feature value is a histogram generated by dividing the L2 norm or logarithm thereof into classes with a plurality of threshold values, and the predetermined time is set to be equal or less than <NUM> minutes.

With the sleep-wakefulness determination apparatus, it is possible to determine sleep and wakefulness by the subject to be examined wearing only a bio-acceleration measuring device.

Various features described in the embodiment below can be combined with each other.

A program for realizing a software in the present embodiment may be provided as a non-transitory computer readable medium that can be read by a computer, or may be provided for download from an external server, or may be provided so that the program can be activated on an external computer to realize functions thereof on a client terminal (so-called cloud computing).

In the present embodiment, the "unit" may include, for instance, a combination of hardware resources implemented by circuits in a broad sense and information processing of software that can be concretely realized by these hardware resources. Further, although various information is adopted in the present embodiment, this information can be represented, for example, by physical signal values representing voltage and current, by high and low signal values as a bit set of binary numbers composed of <NUM> or <NUM>, or by quantum superposition (so-called quantum bit). In this way, communication/operation can be executed on a circuit in a broad sense.

Further, the circuit in a broad sense is a circuit realized by combining at least an appropriate number of a circuit, a circuitry, a processor, a memory, and the like. In other words, it is a circuit includes Application Specific Integrated Circuit (ASIC), Programmable Logic Apparatus (e.g., Simple Programmable Logic Apparatus (SPLD), Complex Programmable Logic Apparatus (CPLD), and Field Programmable Gate Array (FPGA)), and the like.

In Section <NUM>, the overall configuration of a sleep-wakefulness determination system <NUM> will be described. <FIG> shows an overview of a configuration of the sleep-wakefulness determination system <NUM>. The sleep-wakefulness determination system <NUM> is a system comprising a wearable device <NUM> and a sleep-wakefulness determination apparatus <NUM>, which can exchange information through electrical communication means.

<FIG> shows the hardware configuration of the sleep-wakefulness determination system <NUM> shown in <FIG>, and <FIG> shows the functional block diagram of a controller <NUM> in the sleep-wakefulness determination apparatus <NUM>. Further, <FIG> shows an example of the wearable device <NUM>. Hereinafter, the components of the sleep-wakefulness determination system <NUM> will be further explained with reference to these figures.

As shown in <FIG>, the wearable device <NUM> is a small device that can be worn on the arm of a user U, for example. As shown in <FIG>, the wearable device <NUM> comprises a communication unit <NUM>, a storage unit <NUM>, and an acceleration sensor <NUM>. These components are electrically connected via a communication bus <NUM> inside the wearable device <NUM>. Each of the components will be described further below.

Although wired communication means such as USB, IEEE1394, Thunderbolt, and wired LAN network communication are preferable, the communication unit <NUM> may include wireless LAN network communication, mobile communication such as <NUM>/LTE/<NUM>, Bluetooth (registered trademark) communication or the like as necessary. In particular, in the present embodiment, the communication section <NUM> is preferably configured to write information including time-series three-dimensional (<NUM>-D) acceleration vectors v(x, y, z) measured by the acceleration sensor <NUM> described below to external storage media M. The type and form of the storage media M are not particularly limited, and for example, flash memory, card-type memory, optical disk, etc. may be employed as appropriate.

The storage unit <NUM> stores various information defined by the aforementioned description. The storage unit <NUM> can be implemented, for instance, as a storage device such as a solid state drive (SSD), or as a memory such as a random access memory (RAM) that stores temporarily necessary information (argument, array, or the like) regarding program operation, or any combination thereof. In particular, the storage unit <NUM> can store information including time-series <NUM>-D acceleration vectors v(x, y, z) measured by the acceleration sensor <NUM> described below. It may be implemented to store the information directly in the aforementioned storage media M without going through the storage unit <NUM>.

The acceleration sensor <NUM> is configured to measure the acceleration of a part of a body (e.g., an arm) of the user U as <NUM>-D vector information. In other words, information including time-series <NUM>-D acceleration vectors v(x, y, z) can be acquired from the user U.

As shown in <FIG>, the sleep-wakefulness determination apparatus <NUM> comprises a communication unit <NUM>, a storage unit <NUM>, and a controller <NUM>, and these components are electrically connected via a communication bus <NUM> inside the sleep-wakefulness determination apparatus <NUM>. Each of the components will be described further below.

Although wired communication means such as USB, IEEE1394, Thunderbolt, and wired LAN network communication are preferable, the communication unit <NUM> may include wireless LAN network communication, mobile communication such as <NUM>/LTE/<NUM>, Bluetooth (registered trademark) communication or the like as necessary. In particular, in the present embodiment, it is preferable to implement the communication unit <NUM> as a storage media reading unit configured to read information stored in external storage media M. The storage media M stores information including time-series <NUM>-D acceleration vectors v(x, y, z) acquired from the user U by the wearable device <NUM>. As a result, the communication unit <NUM>, which is a storage media reading unit, can read the <NUM>-D acceleration vector v(x, y, z) stored in the storage media M.

The storage unit <NUM> stores various information defined by the aforementioned description. The storage unit <NUM> can be implemented, for instance, as a storage device such as a solid state drive (SSD), or as a memory such as a random access memory (RAM) that stores temporarily necessary information (argument, array, or the like) regarding program operation, or any combination thereof.

In particular, the storage unit <NUM> stores a scalar value calculation program for calculating a scalar value a based on each component (x, y, z) of the <NUM>-D acceleration vector v(x, y, z) at a part of the body of the user U. The storage unit <NUM> also stores a feature value calculation program for calculating a feature value f(N) for each epoch defined by a predetermined time based on the scalar value a. Further, the storage unit <NUM> stores a sleep-wakefulness determination program for determining the sleep and wakefulness of the user U based on the feature value f(N) of the desired epoch and the feature value f(N±δ) of peripheral epochs included in the plurality of epochs before and after the desired epoch in a time series. Further, the storage unit <NUM> stores various programs with respect to the sleep-wakefulness determination apparatus <NUM> executed by the controller <NUM>, etc. in addition to the above.

Further, the storage unit <NUM> stores a machine learning model allowed to learn correlation of the feature value f(N) of the desired epoch, the feature value f(N±δ) of the peripheral epoch and the sleep and wakefulness of the user U. Preferably, conventional algorithms can be employed for the algorithm for such machine learning as appropriate. For example, logistic regression, random forest, XGBoost, multilayer perceptron (MLP), or the like can be adopted. In addition, each time the sleep-wakefulness determination apparatus <NUM> is used, machine learning using the results thereof as training data can be further performed to update such machine learning model.

The controller <NUM> processes and controls overall operation regarding the sleep-wakefulness determination apparatus <NUM>. The controller <NUM> is implemented as, for instance, an unshown central processing unit (CPU). The controller <NUM> realizes various functions with respect to the sleep-wakefulness determination apparatus <NUM> by reading out a predetermined program stored in the storage unit <NUM>. Specifically, the scalar value calculation function for calculating a scalar value a based on each component (x, y, z) of the <NUM>-D acceleration vector v(x, y, z) in a part of the body of the user U, the feature value calculation function for calculating the feature value f(N) for each epoch defined by a predetermined time based on the scalar value a, the sleep and wakefulness determination function for determining the sleep and wakefulness of the user U based on the feature value f(N) of the desired epoch and the feature value f(N±δ) of peripheral epochs included in the plurality of epochs before and after the desired epoch in a time series, in such epochs or the like are included.

In other words, the information processing by software (stored in the storage unit <NUM>) is specifically realized by hardware (controller <NUM>), in such a manner that the controller <NUM> may be executed as a scalar value calculation unit <NUM>, a feature value calculation unit <NUM>, and a sleep-wakefulness determination unit <NUM> as shown in <FIG>. In <FIG>, although it is described as a single controller <NUM>, it is not limited thereto, and may be implemented with a plurality of controllers <NUM> for each function. Further, combination thereof may also be implemented. Hereinafter, the scalar value calculation unit <NUM>, the feature value calculation unit <NUM>, and the sleep-wakefulness determination unit <NUM> will be further described in detail.

The scalar value calculation unit <NUM> is configured to execute the information processing by software (stored in the storage unit <NUM>) specifically realized by hardware (controller <NUM>). The scalar value calculation unit <NUM> calculates a scalar value a based on each component (x, y, z) of the <NUM>-D acceleration vector v(x, y, z) at a part of the body of the user U. The scalar value a is the L2 norm (so-called magnitude) of v(x, y, z). Of course, the scalar value a can also be the L1 norm.

Further, the scalar value calculation unit <NUM> calculates the scalar value a (the L2 norm) based on each component of the time difference vector Δv(x, y, z) acquired from the <NUM>-D acceleration vector v(x, y, z). Here, the time difference vector Δv(x, y, z) is a difference vector between two <NUM>-D acceleration vectors v_1(x, y, z) and v_2 (x, y, z) in a time series. The two 3D acceleration vectors v(x, y, z) at adjacent times are more preferably employed. Due to this process, the effect of gravitational acceleration can be suppressed compared to the case in which the <NUM>-D acceleration vector v(x, y, z) is used directly.

Alternatively, the scalar value calculation unit <NUM> calculates the scalar value a (the L2 norm) based on each component of the n-th-order time derivative vector v^n(x, y, z) acquired from the <NUM>-D acceleration vector v(x, y, z). Here, the n-th-order time derivative vector v^n(x, y, z) is the n-th-order time derivative of the <NUM>-D acceleration vector where n is a natural number (n≥<NUM>). Due to this process, the effect of gravitational acceleration can be suppressed compared to the case in which the <NUM>-D acceleration vector v(x, y, z) is used directly.

The values of n are, for example, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and may be in the range between any two of the numbers indicated above.

The feature value calculation unit <NUM> is configured to execute the information processing by software (stored in the storage unit <NUM>) specifically realized by hardware (controller <NUM>). The feature value calculation unit <NUM> calculates the feature value f(N) for each epoch specified by the predetermined time based on the scalar value a calculated by the scalar value calculation unit <NUM>. These will be described in detail in Section <NUM>.

The sleep-wakefulness determination unit <NUM> is configured to execute the information processing by software (stored in the storage unit <NUM>) specifically realized by hardware (controller <NUM>). The sleep-wakefulness determination unit <NUM> determines the sleep and wakefulness of the user U based on the feature value f(N) of the desired epoch and the feature value f(N±δ) of the peripheral epochs included in the plurality of epochs before and after the desired epoch in a time series, in the epochs. At this time, the sleep-wakefulness determination unit <NUM> can determine such sleep and wakefulness based on the above-described machine learning model stored in the storage unit <NUM>.

<FIG> shows how the desired epoch and multiple peripheral epochs are applied to the machine learning model. The n-th epoch is the desired epoch, and ±3th epochs before and after the desired epoch are selected as peripheral epochs. In other words, using the feature value f(N) of the desired epoch and the feature value f(N±<NUM>, <NUM>, <NUM>) of the peripheral epochs included in the third before and after the desired epoch in a time series as input, the sleep-wakefulness determination unit <NUM> determines the sleep and wakefulness of the user U based on the aforementioned machine learning model stored in the storage unit <NUM>. The number of epochs is, of course, only an example and is not limited thereto.

That is, the number of epochs can be, for example, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and may be in the range between any two of the numbers indicated above.

The determination result conversion unit <NUM> is configured to execute the information processing by software (stored in the storage unit <NUM>) specifically realized by hardware (controller <NUM>). It is preferable that the controller <NUM> further comprises the determination result conversion unit <NUM>. The determination result conversion unit <NUM> converts the results determined by the sleep-wakefulness determination unit <NUM>. These will be described in detail in Section <NUM>.

Section <NUM> describes the details of the sleep-wakefulness determination system <NUM> with reference to the experimental data. In the experiment, the predetermined time to define the epoch was set to <NUM> seconds, but it should be noted that this is not the limit of the experiment.

<FIG> shows the L2 norm of the 3D acceleration vector v(x, y, z) per epoch. <FIG> shows the L2 norm of the time difference vector Δv(x, y, z) for each epoch. <FIG> is a hypnogram showing actual sleep. It is confirmed that the user U is awake at the timing when the numerical value of the L2 norm of the <NUM>-D acceleration vector v(x, y, z) or the time difference vector Δv(x, y, z) is large. In other words, the correlation between the acceleration at a part of the body of user U acquired by the wearable device <NUM> and the sleep and wakefulness of the user U was confirmed. This is the basic principle of the sleep-wakefulness determination system <NUM> of the present embodiment.

As mentioned above, the present sleep-wakefulness determination system <NUM> extracts the feature value f(N) from the <NUM>-D acceleration vector v (x, y, z) etc. from the L2 norm, and uses the feature value f(N) to determine sleep and wakefulness. Specifically, the feature value f(N) is a histogram generated by dividing the scalar value a or the logarithm thereof into classes with multiple thresholds, or a power spectrum based on the product of the scalar value a multiplied by a window function. For example, <FIG> shows the logarithmic power spectrum of the time difference vector Δv(x, y, z). The hypnogram correlating to the logarithmic power spectrum is also shown in <FIG>.

As described above, the sleep-wakefulness determination unit <NUM> determines the sleep and wakefulness of the user U based on the feature value f(N) of the desired epoch and the feature value f(N±δ) of the peripheral epochs included in the plurality of epochs before and after the desired epoch in a time series, in the epochs. The number of epochs (the sum of one desired epoch and the peripheral epochs) should be selected appropriately. For reference, a comparison between a case where the number of epochs is <NUM> and a case where the number of epochs is <NUM> is shown in <FIG>. In both the histogram and the power spectrum, it is confirmed that the correct answer percentage and the F-value (ratio of standard deviation) in the case of <NUM> are increased compared to the case of <NUM>.

In Section <NUM>, a method of determining the sleep-wakefulness using the sleep-wakefulness determination system <NUM> is described according to a flowchart shown in <FIG>.

Using the wearable device <NUM> worn by the user U, information including the time-series 3D acceleration vector v(x, y, z) at a part of the body of the user U is acquired. The information acquired thereby is read into the sleep-wakefulness determination apparatus <NUM> via the storage media M.

Following the step S1, the scalar value calculation unit <NUM> in the sleep-wakefulness determination apparatus <NUM> calculates the scalar value a based on each component (x, y, z) of the <NUM>-D acceleration vector v(x, y, z) at a part of the body of the user U.

Following the step S2, the feature value calculation unit <NUM> in the sleep-wakefulness determination apparatus <NUM> calculates the feature value f(N) for each epoch defined by a predetermined time based on the scalar value a calculated by the scalar value calculation unit <NUM>.

Following the step S3, the sleep-wakefulness determination unit <NUM> in the sleep-wakefulness determination device <NUM> determines the sleep and wakefulness of the user U by employing the feature value f(N) of the desired epoch and the feature value f(N±δ) of the peripheral epochs included in a plurality of epochs before and after the desired epoch in a time series, in the epochs, as inputs to the machine learning model stored in the storage unit <NUM>.

Due to the present method of determining sleep and wakefulness, the sleep and wakefulness with a sufficiently high degree of accuracy using only a few devices can be determined.

In Section <NUM>, a method of converting the results of sleep-wakefulness determination using the sleep-wakefulness determination system <NUM> is explained according to flowcharts shown in <FIG>.

In the result determined by the sleep-wakefulness determination unit <NUM>, as shown in <FIG>, the result is set to <NUM> or <NUM> (sleep or wakefulness) every <NUM> seconds. In this step, as shown in <FIG>, the determination results are averaged every <NUM> minutes, and the data is converted smoothly into data having a value between <NUM> and <NUM> with <NUM> increments. The specified time (<NUM> minutes) for averaging the determination results and the increment width (<NUM>) of the averaged results are both examples and can be changed as necessary.

Following the step S11, the period of the sleep-wakefulness is calculated using the Chi-square periodogram method.

Following the step S11, the coefficient of variation (standard deviation divided by the mean) is calculated to determine the amplitude of sleep-wakefulness.

According to the method of converting the determination results, the sleep and wakefulness of the user U can be examined from various perspectives.

In Section <NUM>, variations of the sleep-wakefulness determination system <NUM> according to the present embodiment will be described. That is, the sleep-wakefulness determination system <NUM> according to the present embodiment may be further devised in the following manner.

First, instead of the storage media M, the communication unit <NUM> in the wearable device <NUM> may transmit information including a time-series <NUM>-D acceleration vector v(x, y, z) in a part of the body of the user U to the communication unit <NUM> in the sleep-wakefulness determination apparatus <NUM>, via wireless communication. In other words, the communication unit <NUM> is configured to communicate with the wearable device <NUM> (including the acceleration sensor <NUM>) worn by the user U on a part of the body thereof so as to receive the <NUM>-D acceleration vector v(x, y, z) measured by the acceleration sensor <NUM>.

Secondly, the wearable device <NUM> and the sleep-wakefulness determination apparatus <NUM> may be configured as a single unit. In other words, the sleep-wakefulness determination apparatus <NUM> may be a wearable device <NUM> to be worn by the user U on a part of the body, and may further comprise an acceleration sensor <NUM>. The acceleration sensor <NUM> may be configured to measure a <NUM>-D acceleration vector v(x, y, z).

As described above, the present embodiment makes it possible to implement a sleep-wakefulness determination apparatus <NUM> configured to determine sleep and wakefulness with a sufficiently high degree of accuracy using a small number of wearing devices.

There is provided a sleep-wakefulness determination apparatus configured to determine sleep and wakefulness of a user, comprising a scalar calculation unit configured to calculate a scalar value based on each component of an acceleration vector in a part of a body of the user; a feature value calculation unit configured to calculate a feature value for each epoch defined by a predetermined time based on the scalar value; and a sleep-wakefulness determination unit configured to determine the sleep and wakefulness of the user based on the feature value of a desired epoch and the feature value of peripheral epochs included in a plurality of epochs before and after the desired epoch in a time series, in the epoch.

Software for implementing the sleep-wakefulness determination apparatus <NUM> as hardware so as to determine the sleep and wakefulness with sufficiently high accuracy using a small number of wearing devices can also be implemented as a program. Such a program may be provided as a non-transitory computer readable medium that can be read by a computer, or may be provided for download from an external server, or may be provided so that the program can be activated on an external computer to realize functions thereof on a client terminal (so-called cloud computing).

There is provided a program which allows a computer to function as a sleep-wakefulness determination apparatus configured to determine sleep and wakefulness of a user, the apparatus comprising: a scalar calculation unit configured to calculate a scalar value based on each component of an acceleration vector in a part of a body of the user; a feature value calculation unit configured to calculate a feature value for each epoch defined by a predetermined time based on the scalar value; and a sleep-wakefulness determination unit configured to determine the sleep and wakefulness of the user based on the feature value of a desired epoch and the feature value of peripheral epochs included in a plurality of epochs before and after the desired epoch in a time series.

Claim 1:
A sleep-wakefulness determination apparatus (<NUM>) configured to determine sleep and wakefulness of a user (U), comprising:
a scalar value calculation unit (<NUM>) configured to calculate a scalar value (a) based on each component of an acceleration vector in a part of a body of the user (U);
a feature value calculation unit (<NUM>) configured to calculate a feature value for each epoch defined by a predetermined time based on the scalar value (a); and
a sleep-wakefulness determination unit (<NUM>) configured to determine the sleep and wakefulness of the user (U) based on the feature value of a desired epoch and the feature value of peripheral epochs included in a plurality of epochs before and after the desired epoch in a time series, in the epoch,
characterized in that:
the scalar value (a) is an L2 norm based on each component of a time difference vector that is a difference vector of two of the acceleration vectors in a time series,
the feature value is a histogram generated by dividing the L2 norm or logarithm thereof into classes with a plurality of threshold values, and
the predetermined time is set to be equal or less than <NUM> minutes.