Patent Publication Number: US-2023157629-A1

Title: Sleep onset determination method, sleep onset determination system, air conditioning device, and recording medium

Description:
CROSS-REFERENCE OF RELATED APPLICATIONS 
     This application is the U.S. National Phase under 35 U.S.C. § 371 of International Patent Application No. PCT/JP2021/022260, filed on Jun. 11, 2021, which in turn claims the benefit of Japanese Application No. 2021-025687, filed on Feb. 19, 2021, the entire disclosures of which Applications are incorporated by reference herein. 
    
    
     TECHNICAL FIELD 
     The present invention relates to a sleep onset determination method, a sleep onset determination system, an air conditioning device, and a recording medium that determine sleep onset of a user. 
     BACKGROUND ART 
     For example, Patent Literature (PTL) 1 discloses a sleep onset determination method. In this method, an illuminance level in a room is detected and sleep onset of a user is detected based on the illuminance level. 
     CITATION LIST 
     Patent Literature 
     [PTL 1] Japanese Unexamined Patent Application Publication No. 2012-202659 
     SUMMARY OF INVENTION 
     Technical Problem 
     In the above sleep onset determination method, when the illuminance level becomes less than or equal to a predetermined value, it is determined as sleep onset. However, in practice, the sleep onset cannot be detected accurately because of actions of a user going in or going out of the room in combination with operation of a light fixture. 
     The present invention aims to provide a sleep onset determination method, a sleep onset determination system, an air conditioning device, and a recording medium that are capable of determining sleep onset more highly accurately. 
     Solution to Problem 
     A sleep onset determination method according to one aspect of the present invention includes: detecting a light-out time that is a time at which illuminance in a room becomes less than or equal to a predetermined value; storing the light-out time detected in the detecting of the light-out time and a time period including the light-out time detected; and determining whether a user fell asleep at a currently detected light-out time, based on the light-out time and the time period stored in the storing of the light-out time. 
     Furthermore, a sleep onset determination system according to one aspect of the present invention includes: an illuminance detection unit configured to detect a light-out time that is a time at which illuminance in a room becomes less than or equals to a predetermined value; a light-out time storage configured to store the light-out time detected by the illuminance detection unit and a time period including the light-out time detected; and a determination unit configured to determine whether a user fell asleep at a currently detected light-out time, based on the light-out time and the time period stored by the light-out time storage. 
     Furthermore, an air conditioning device according to one aspect of the present invention includes: a communication unit configured to obtain a determination result of the determination unit in the sleep onset determination system described above; an air conditioning unit configured to adjust a temperature in the room; and a control unit configured to control the air conditioning unit. The control unit is configured to control the air conditioning unit, based on the determination result of the determination unit obtained by the communication unit. 
     Furthermore, a recording medium according to one aspect of the present invention is a non-transitory computer-readable recording medium for use in a computer. The recording medium has a computer program recorded thereon for causing the computer to execute the above sleep onset determination method. 
     Advantageous Effects of Invention 
     A sleep onset determination method and so on according to one aspect of the present invention makes it possible to determine sleep onset more highly accurately. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a block diagram illustrating a functional configuration of a sleep onset determination system according to an embodiment. 
         FIG.  2    is a flowchart of a learning process according to the embodiment. 
         FIG.  3    is a flowchart of a determination process according to the embodiment. 
         FIG.  4    is a block diagram illustrating a functional configuration of a sleep onset determination system according to a variation. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following describes, in detail, embodiments of a sleep onset determination method and so on according to the present invention, with reference to the drawings. Note that each of the embodiments described below shows a preferred specific example of the present invention. Therefore, the numerical values, shapes, materials, structural elements, the arrangement and connection of the structural elements, steps and the order of the steps mentioned in the following embodiments are mere examples and not intended to limit the present invention. 
     Note that the accompanying drawings and the following description are provided to help a person skilled in the art to fully understand the present invention, and are not intended to limit the subject matter defined in the appended claims. 
     Furthermore, the figures are schematic diagrams and are not necessarily precise illustrations. Throughout the figures, structural elements that are essentially the same share like reference signs. Accordingly, duplicate description is omitted or simplified. 
     Embodiment 
     Configuration 
     First, a configuration of a sleep onset determination system according to an embodiment will be described. Note that in the present embodiment, an example in which the sleep onset determination system is applied to an air conditioner (air conditioning device) will be described. Note that the sleep onset determination system may be applied to an electronic device other than an air conditioner. The electronic device other than an air conditioner may be any electronic device. It is sufficient as long as the electronic device can reflect sleep onset of a user on its control. Examples of such an electronic device include an automatic control device for a television, an air cleaner, a humidifier, a smart lock, a loudspeaker, a lighting fixture, or a curtain. 
       FIG.  1    is a block diagram illustrating a functional configuration of sleep onset determination system  10  according to the embodiment. As illustrated in  FIG.  1   , sleep onset determination system  10  includes air conditioner  20  and server  30 . Air conditioner  20  and server  30  are connected to each other via network N. 
     Air Conditioner 
     Air conditioner  20  is provided in a room of a home where a user lives, for example. Air conditioner  20  includes communication unit  21 , illuminance measuring unit  22 , human detection sensor  23 , air conditioning unit  24 , and control unit  25 . 
     Communication unit  21  is a communication module connected to network N. Communication unit  21  is connected to illuminance measuring unit  22 , human detection sensor  23 , and control unit  25 . Communication unit  21  connects server  30 , illuminance measuring unit  22 , human detection sensor  23 , and control unit  25  such that these structural elements can freely communicate with each other via network N. 
     Illuminance measuring unit  22  is an illuminance sensor that detects illuminance in the room where air conditioner  20  is provided. Illuminance measuring unit  22  outputs an illuminance value measured at predetermined intervals (for example, at intervals of 5 minutes). 
     Human detection sensor  23  is a sensor that detects presence or absence of a user at a predetermined position in the room where air conditioner  20  is provided. Any device can be used as human detection sensor  23  as long as the device can detect a position of a user present in the room using any of the following: infrared rays, supersonic rays, and visible light. Human detection sensor  23  outputs user position information indicating presence or absence of the user detected at predetermined intervals (for example, at intervals of 5 minutes). Human detection sensor  23  may be a structural element different from air conditioner  20 . 
     Air conditioning unit  24  is a drive unit for adjusting the temperature in the room. Control unit  25  is a microcomputer that controls air conditioning unit  24  based on a determination result input from server  30  via communication unit  21 . 
     Server 
     Server  30  is a server device (computer) connected to air conditioner  20  such that server  30  can freely communicate with air conditioner  20  via network N. Server  30  includes: communication unit  31 , illuminance measurement value accumulation unit  32 , human detection sensor measurement value accumulation unit  41 , light-out determination learning unit  33 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination learning unit  36 , final light-out determination unit  37 , final light-out determination result accumulation unit  38 , sleep determination waiting learning unit  39 , and sleep determination unit  40 . 
     Note that each of the learning units (light-out determination learning unit  33 , final light-out determination learning unit  36 , and sleep determination waiting learning unit  39 ) outputs a learning model to a corresponding one of the determination units (light-out determination unit  34 , final light-out determination unit  37 , and sleep determination unit  40 ). Each determination unit gives an input value to its learning model and performs determination. The type of the learning model included in each learning unit may be any type of learning model without limitation. For example, machine learning may be used. A specific example of the learning model may be a neural network. Moreover, as a machine learning algorithm, for example, one of a decision tree, a support vector machine (SVM), k-nearest neighbors algorithm (kNN), a random forest, and gradient boosting may be used, or these may be used in any combination. Furthermore, it is preferable that each learning unit performs supervised learning, but each learning unit may perform unsupervised learning. 
     Communication unit  31  is a communication module connected to network N. Communication unit  31  is connected to illuminance measurement value accumulation unit  32 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination unit  37 , sleep determination unit  40 , and human detection sensor measurement value accumulation unit  41 . Communication unit  31  connects, via network N, to air conditioner  20 , illuminance measurement value accumulation unit  32 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination unit  37 , sleep determination unit  40 , and human detection sensor measurement value accumulation unit  41  such that illuminance measurement value accumulation unit  32 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination unit  37 , sleep determination unit  40 , and human detection sensor measurement value accumulation unit  41  can freely communicate with air conditioner  20 . 
     Illuminance measurement value accumulation unit  32  is a storage that accumulates, as a log, illuminance values of illuminance measuring unit  22 . The illuminance values are each input from illuminance measuring unit  22  in air conditioner  20  via network N and communication unit  31 . 
     Human detection sensor measurement value accumulation unit  41  is a storage that accumulates, as a log, detection results of human detection sensor  23 . The detection results are each input from human detection sensor  23  in air conditioner  20  via network N and communication unit  31 . 
     Light-out determination learning unit  33  learns whether the room where air conditioner  20  is provided is dark or bright by performing statistical processing based on the log of the illuminance values accumulated in illuminance measurement value accumulation unit  32 . Specifically, light-out determination learning unit  33  determines an illuminance value when the room is dark (a first illuminance value). The illuminance value when the room is dark is an illuminance value measured over the longest time in a time frame that is generally considered to be a light-out time during the night (for example, AM 1:00-AM 5:00). Moreover, light-out determination learning unit  33  determines an illuminance value when the room is bright (a second illuminance value). The illuminance value when the room is bright is an illuminance value in a time frame that is generally considered to be a time before sleep onset during the night (for example, PM 8:00-AM 4:00), and is measured immediately after the first illuminance value. The illuminance value when the room is bright is also a value that is other than values in the lowest 10% of the accumulated illuminance values greater than the first illuminance value. The values in the lowest 10% of the accumulated values may be obtained in cases where the illuminance values do not result from turning on a light (for example, light enters the room from outside the room due to opening and closing the door). Therefore, excluding illuminance values in the lowest 10% of the accumulated illuminance values makes it possible to extract illuminance values resulting from turning on the light. Note that values in the lowest 10% of the accumulated illuminance values are used here, but any other values may be used. 
     Light-out determination unit  34  obtains an illuminance value currently measured by illuminance measuring unit  22  from communication unit  31 , and determines a light-out time in the room based on the illuminance value and the learning model that has been learned by light-out determination learning unit  33 . Specifically, light-out determination unit  34  determines, as a light-out time, a timing (time) at which the second illuminance value changes to the first illuminance value after the second illuminance value continues for at least a predetermined time (for example, 5 minutes to 10 minutes). In other words, in the present embodiment, light-out determination learning unit  33  and light-out determination unit  34  are an example of the illuminance detection unit that determines a light-out time at which the illuminance value in the room becomes less than or equal to a predetermined value (first illuminance value). Note that since the first illuminance value and the second illuminance value are determined using statistical processing in the present embodiment, the first illuminance value and the second illuminance value are determined according to an environment in which illuminance values are to be detected. However, the first illuminance value and the second illuminance value may be fixed values that are set in advance. In other words, in this case, the system configuration may be simplified because the light-out time can be detected without using statistical processing. 
     Light-out determination result accumulation unit  35  is a storage that associates, with one another, the light-out time determined by light-out determination unit  34 , the time period including the light-out time detected, and user position information output from human detection sensor  23  at a time at which the light-out time is determined, and accumulates them as a log. Here, the time period includes a day of week, month, and season. Here, light-out determination result accumulation unit  35  is an example of a light-out time storage that stores a light-out time detected by illuminance detection unit and a time period including the light-out time detected. 
     Final light-out determination learning unit  36  learns whether light-out at a light-out time is the final light-out of a day through processing with its learning model, based on logs of the light-out times, time periods, and user position information that are accumulated in light-out determination result accumulation unit  35 . For example, final light-out determination learning unit  36  obtains a determination result of each machine learning algorithm and its probability by using machine learning algorithms individually. 
     Note that it is difficult to determine in real time whether light-out at a light-out time is the final light-out of a day. However, checking one day data of a target day, which is a day for which the final light-out is to be determined, on the day following the target day makes it easier to determine whether the light-out at the light-out time is the final light-out of the target day. Final light-out determination learning unit  36  performs learning in view of the result determined on the day following the target day. 
     Final light-out determination unit  37  inputs the determination result of light-out determination unit  34  to the learning model obtained by final light-out determination learning unit  36  to determine whether the current light-out is the final light-out. Specifically, final light-out determination unit  37  extracts a determination result having the highest probability from among determination results and their probabilities determined by final light-out determination learning unit  36 , and uses the extracted determination result as a result of final light-out determination. In other words, when final light-out determination unit  37  determines that the currently detected light-out time is the final light-out, final light-out determination unit  37  determines that the user in the room fell asleep. As described above, final light-out determination learning unit  36  and final light-out determination unit  37  are an example of a determination unit that determines whether the user fell asleep at the currently detected light-out time through processing a light-out time and its time period with a machine learning model. When final light-out determination unit  37  determines the final light-out, final light-out determination unit  37  outputs the determination result to air conditioner  20  from communication unit  31  via network N. 
     Final light-out determination result accumulation unit  38  is a storage that associates, the light-out time, the time period, and user position information that have been input to obtain the determination result adopted by final light-out determination unit  37  with the determination result and its probability, and stores them as a log. 
     After a predetermined time elapsed from when the determination has been performed by final light-out determination unit  37 , sleep determination waiting learning unit  39  processes, using the learning model obtained by machine learning, (i) the probability of the determination result of final light-out determination unit  37 ; (ii) light-out times, time periods, and user position information that have been associated with the probability; and (iii) time elapsed after the light-out has been detected. 
     Specifically, sleep determination waiting learning unit  39  uses, as explanatory variables, parameters to be used in the above-described machine learning, and determines, as response variables, flags indicating whether light-out occurred again after the light-out time. For example, a flag is determined for each of the logs of illuminance measuring unit  22  transmitted at 5-minute intervals. When there is light-out after the light-out time, the flag is 0. When there is no light-out, the flag is 1. Sleep determination waiting learning unit  39  learns whether the flag is 0 or 1, based on the explanatory variables. Sleep determination waiting learning unit  39  learns the data by machine learning and creates a model that calculates a probability of whether the user fell asleep according to the time frame when the light-out has been detected and the time elapsed from the detection of light-out. Here, sleep determination waiting learning unit  39  uses a logistic regression model, for example. 
     For example, sleep determination waiting learning unit  39  learns that the probability that the user fell asleep at a current time increases as the time frame of the light-out time becomes later. Moreover, sleep determination waiting learning unit  39  learns that the probability that the user fell asleep at a current time increases as the probability of the determination for the light-out time immediately before the light-out time that is currently being determined increases. Moreover, sleep determination waiting learning unit  39  learns that the probability that the user fell asleep at a current time increases as the time elapsed from the time at which the light-out has been detected becomes longer. 
     Sleep determination unit  40  inputs, to the learning model obtained by sleep determination waiting learning unit  39 , the determination result of light-out determination unit  34  and the probability of the determination result adopted by final light-out determination unit  37  to determine whether the user fell asleep at a current time. Specifically, sleep determination unit  40  calculates the probability of the determination using the learning result obtained by sleep determination waiting learning unit  39 . Sleep determination unit  40  determines that the user fell asleep at a current time when the probability of the determination is greater than or equal to a predetermined value. As described above, sleep determination waiting learning unit  39  and sleep determination unit  40  are an example of a redetermination unit that redetermines whether the user fell asleep at the currently detected light-out time by processing a light-out time and its time period with a learning model after a predetermined time elapsed from the time at which determination by final light-out determination unit  37  has been performed. Then, sleep determination unit  40  outputs its determination result to air conditioner  20  from communication unit  31  via network N. Note that sleep determination unit  40  does not perform the determination when final light-out determination unit  37  determines the final light-out. 
     Each of the processing units of server  30  (illuminance measurement value accumulation unit  32 , light-out determination learning unit  33 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination learning unit  36 , final light-out determination unit  37 , final light-out determination result accumulation unit  38 , sleep determination waiting learning unit  39 , and sleep determination unit  40 ) may be implemented by a control program for executing the above-described processes and a central processing unit (CPU), a random-access memory (RAM), and a read-only memory (ROM) that execute the control program. Each of these processing units may be implemented by one or more CPUs. 
     Sleep Onset Determination Method 
     Next, a sleep onset determination method to be executed by server  30  in sleep onset determination system  10  according to the present embodiment will be described. Note that the sleep onset determination method is stored as a program in server  30 . The sleep onset determination method includes a learning process and a determination process. 
       FIG.  2    is a flowchart of a learning process according to the embodiment. As illustrated in  FIG.  2   , in step S 1 , illuminance measurement value accumulation unit  32  accumulates, as a log, illuminance values of illuminance measuring unit  22 . The illuminance values are input from illuminance measuring unit  22  of air conditioner  20  via network N and communication unit  31 . 
     In step S 2 , light-out determination learning unit  33  learns whether the room where air conditioner  20  is provided is dark or bright by performing statistical processing based on the log of illuminance values accumulated in illuminance measurement value accumulation unit  32 . 
     In step S 3 , light-out determination unit  34  determines, as a light-out time in the room, based on the contents learned by light-out determination learning unit  33 , a timing (time) at which the second illuminance value changes to the first illuminance value after the second illuminance value continues for at least a predetermined time (for example, 5 minutes to 10 minutes). Accordingly, step S 2  and step S 3  are an example of detecting a light-out time that is a time at which illuminance in a room becomes less than or equals to a predetermined value. 
     In step S 4 , light-out determination result accumulation unit  35  associates, with one another, the light-out time determined by light-out determination unit  34 , the time period including the light-out time detected, and user position information output from human detection sensor  23  at a time at which the light-out time is determined, and accumulates them as a log. Accordingly, step S 4  is an example of storing the light-out time detected in the detecting of the light-out time and a time period including the light-out time detected. Furthermore, step S 4  is also an example of detecting presence or absence of the user at a predetermined position in the room, as well as an example of storing user position information indicating the presence or absence of the user detected in the detecting of presence or absence of the user. The present embodiment describes an example in which the storing of the light-out time, the detecting of presence or absence of the user, and the storing of user position information are executed in step S 4 . However, these may be executed independently of each other. 
     In step S 5 , final light-out determination learning unit  36  learns, through processing with its learning model, whether the light-out at a current light-out time is the final light-out of the day, based on the logs including the light-out times, their time periods, and user position information accumulated in light-out determination result accumulation unit  35 . 
     In step S 6 , final light-out determination unit  37  extracts a determination result having the highest probability from among the determination results and their probabilities determined by final light-out determination learning unit  36 , and adopts the extracted determination result as a result of final light-out determination. Accordingly, step S 5  and step S 6  are an example of determining whether the user fell asleep at the currently detected light-out time through processing, using a learning model, (i) the light-out time and the time period stored in the storing of the light-out time, and (ii) the user position information stored in the storing of the user position information. 
     In step S 7 , final light-out determination result accumulation unit  38  associates, the light-out time, the time period, and user position information that have been input to obtain the determination result, with the determination result adopted by final light-out determination unit  37  and the probability of the determination result, and stores accumulates as a log. Accordingly, step S 7  is an example of storing a determination result of the determining. 
     In step S 8 , sleep determination waiting learning unit  39  learns, through processing, using its learning model, (i) the probability of the determination result of final light-out determination unit  37 ; (ii) the light-out time, time period, and user position information that have been associated with the probability; and (iii) time elapsed after the light-out has been detected. This processing is performed after a predetermined time elapsed from when the determination by final light-out determination unit  37  has been performed. Then, sleep determination waiting learning unit  39  ends the learning process. 
     Next, a determination process will be described.  FIG.  3    is a flowchart of a determination process according to the embodiment. As illustrated in  FIG.  3   , in step S 21 , light-out determination unit  34  obtains an illuminance value of illuminance measuring unit  22 . The illuminance value is input from illuminance measuring unit  22  of air conditioner  20  via network N and communication unit  31 . 
     In step S 22 , light-out determination unit  34  obtains the illuminance value currently measured by illuminance measuring unit  22  from communication unit  31 , and determines a light-out time in the room based on the illuminance value and the learning model obtained by light-out determination learning unit  33 . Here, light-out determination unit  34  proceeds to step S 21  when the light-out time is not yet determined and proceeds to step S 23  when the light-out time is determined. 
     In step S 23 , final light-out determination unit  37  inputs the determination result of light-out determination unit  34  to the learning model obtained by final light-out determination learning unit  36  to determine whether the current light-out is the final light-out. Step S 23  is an example of determining whether a user fell asleep at a currently detected light-out time. Here, final light-out determination unit  37  proceeds to step S 24  when the final light-out time is not yet determined and proceeds to step S 28  when the final light-out time is determined. 
     In step S 24 , light-out determination unit  34  obtains an illuminance value of illuminance measuring unit  22 . The illuminance value is input from illuminance measuring unit  22  in air conditioner  20  via network N and communication unit  31 . 
     In step S 25 , light-out determination unit  34  obtains the illuminance value currently measured by illuminance measuring unit  22  from communication unit  31 , and determines a light-out time in the room based on the illuminance value and the learning model obtained by light-out determination learning unit  33 . Here, light-out determination unit  34  proceeds to step S 26  when the light-out time is not yet determined and proceeds to step S 23  when the light-out time is determined. 
     In step S 26 , light-out determination unit  34  determines whether the light in the room is off, based on the illuminance value obtained in step S 25 . Here, light-out determination unit  34  proceeds to step S 24  when it is not yet determined that the light in the room is off and proceeds to step S 27  when it is determined that the light in the room is off. 
     In step S 27 , sleep determination unit  40  inputs, to the learning model obtained by sleep determination waiting learning unit  39 , the determination result of light-out determination unit  34  and the probability of the determination result adopted by final light-out determination unit  37  to determine whether the user fell asleep at a current time. As described above, step S 8  and step S 27  are an example of redetermining whether the user fell asleep after a predetermined time elapsed from the currently detected light-out time. This redetermining is performed by processing, using the learning model, the light-out time and the time period stored in the storing after a predetermined time elapsed from when the determining has been performed. Here, sleep determination unit  40  proceeds to step S 24  when it is not yet determined that the user fell asleep, and proceeds to step S 28  when it is determined that the user fell asleep. 
     In step S 28 , final light-out determination unit  37  or sleep determination unit  40  transmits its determination result to air conditioner  20  from communication unit  31  via network N. In other words, step S 28  is an example of transmitting a control signal to a device (air conditioner  20 ) in the room, when it is determined that the user fell asleep in the determining. In air conditioner  20 , when communication unit  21  receives the determination result of final light-out determination unit  37  or sleep determination unit  40 , control unit  25  controls air conditioning unit  24  based on the determination result. In other words, after the light-out time is determined to be a time at which the user fell asleep, the temperature in the room will be controlled in a mode suitable for sleeping. 
     Effects Etc. 
     As described above, the sleep onset determination method according to the embodiment includes: detecting a light-out time that is a time at which illuminance in a room becomes less than or equal to a predetermined value; storing the light-out time detected in the detecting of the light-out time and a time period including the light-out time detected; and determining whether a user fell asleep at a currently detected light-out time, based on the light-out time and the time period stored in the storing of the light-out time. 
     Moreover, the program according to the embodiment is a program for causing a computer to execute the sleep onset determination method described above. 
     Moreover, sleep onset determination system  10  according to the embodiment includes: an illuminance detection unit (light-out determination learning unit  33  and light-out determination unit  34 ) configured to detect a light-out time that is a time at which illuminance in a room becomes less than or equals to a predetermined value; a light-out time storage (light-out determination result accumulation unit  35 ) configured to store the light-out time detected by the illuminance detection unit and a time period including the light-out time detected; and a determination unit (final light-out determination learning unit  36  and final light-out determination unit  37 ) configured to determine whether a user fell asleep at a currently detected light-out time, based on the light-out time and the time period stored by the light-out time storage. 
     Here, the inventors have studied a case where the light-out time detected in the step of detecting a light-out time is determined to be a timing that a user fell asleep (sleep onset timing). In this case, an error occurred at a rate of approximately 25% that it is determined that the user fell asleep at a timing earlier than the actual final light-out time of the day (the timing at which the user actually fell asleep) in the determination. 
     In the present embodiment, it is determined that the user fell asleep at the currently detected light-out time by detecting light-out times when the illuminance in the room becomes less than or equal to a predetermined value and then processing the light-out times and their time periods with a learning model. With this, considering the past tendency makes it possible to determine whether the currently detected light-out time is the final light-out of a day, i.e., a timing at which the user fell asleep. In this case, the above-described rate of error in the determination was reduced to approximately 10%, according to the studies by the inventors. Accordingly, sleep onset can be determined more highly accurately. 
     In particular, a sleep onset timing of a user is determined by considering the time period including the light-out time detected. Examples of the time period include a day of week, month, and season, as described above. The daily habit of a user tends to differ depending on such time periods. For example, a user who works differently depending on days of a week, there may be a certain tendency in a timing of falling asleep for each day of a week. Moreover, there may be a user who falls asleep later in summer and earlier in winter. When a sleep onset timing is determined by considering such time periods, the sleep onset timing can be determined more accurately for a user who changes his/her daily habit depending on the time periods. Note that statistical processing may be performed using data having a high similarity in characteristics of time periods and the processed data may be reflected on sleep onset timing determination. 
     Moreover, the air conditioner (air conditioning device) according to the embodiment includes: communication unit  21  that obtains a determination result of the determination unit in sleep onset determination system  10  described above; air conditioning unit  24  configured to adjust a temperature in the room; and control unit  25  configured to control air conditioning unit  24 . Control unit  25  is configured to control air conditioning unit  24 , based on the determination result of the determination unit obtained by communication unit  21 . 
     Since this makes it possible to reflect more highly accurate sleep onset determination on a control by air conditioning unit  24 , a mode suitable for sleep can be performed more reliably after the user fell asleep. 
     Moreover, the determining includes determining whether the user fell asleep at the currently detected light-out time through processing, using a learning model, the light-out time and the time period stored in the storing of the light-out time. 
     With this, the light-out time and the time period stored in the storing of the light-out time are processed using a learning model to determine that the user fell asleep at the currently detected light-out time. Therefore, sleep onset of a user can be determined more accurately. 
     Moreover, the sleep onset determination method includes: storing a determination result of the determining; and redetermining whether the user fell asleep after a predetermined time elapsed from the currently detected light-out time, through processing the light-out time and the time period stored in the storing of the light-out time, the processing being performed using a learning model obtained by machine learning after the predetermined time elapsed from when the determining has been performed. 
     For example, when the determination result obtained in the determining is simply adopted, i.e., when the redetermining is not performed, an error may occur that it is continuously determined that a currently detected light-out time is not the final light-out time in a day, and a final determination result is not determined. According to studies by the inventors, such an error occurs at a rate of approximately 8% when the redetermining is not performed. 
     In the present embodiment, it is redetermined whether the user fell asleep at the currently detected light-out time by processing, using a learning model obtained by machine learning, a log including light-out times and time periods after a predetermined time elapsed from when the determining has been performed. It is possible to determine whether the currently detected light-out time is the final light-out of a day, i.e., a timing at which the user fell asleep, by considering the past tendency after the predetermined time elapsed from when the determining has been performed. In this case, the rate of error described above is reduced to approximately 4% according to the studies by the inventors. Accordingly, sleep onset can be determined more highly accurately. 
     Furthermore, the sleep onset determination method includes detecting presence or absence of the user at a predetermined position in the room; and storing user position information indicating the presence or absence of the user that has been detected in the detecting of the presence or absence of the user. The determining includes determining whether the user fell asleep at the currently detected light-out time, through processing, using a learning model obtained by machine learning, the user position information stored in the storing of the user position information, in addition to the light-out time and the time period stored in the storing of the light-out time. 
     With this, in the determining, determination is performed including the user position information. In other words, sleep onset determination can be determined more highly accurately, because it is determined that a user fell asleep at a currently detected light-out time by considering the position of the user in a room. For example, it is possible to reduce cases where the light-out time at which a user turned the light off when leaning the room is erroneously determined to the sleep onset timing. 
     Moreover, the sleep onset determination method further includes transmitting a control signal to a device (air conditioner  20 ) in the room when it is determined that the user fell asleep in the determining. 
     With this, the determination result that the user fell asleep can be reflected on a control of a device in the room. Therefore, it is possible to cause a device to perform a mode suitable for sleep after the user fell asleep. 
     Other Embodiments 
     Hereinbefore, one or more aspects of the sleep onset determination system and so on according to the present invention have been described based on the embodiment, but the present invention should not be limited to the embodiment. 
     For example, in the embodiment, an example has been described in which the sleep onset determination method includes detecting presence or absence of the user and storing user position information. However, the sleep onset determination method does not need to include the detecting of presence or absence of the user and the storing of user position information. 
     Moreover, in the embodiment, an example has been described in which a sleep onset determination method includes storing a determination result and redetermining. However, the sleep onset determination method does not need to include the storing of a determination result and the redetermining. 
     Moreover, in the embodiment, an example has been described in which server  30  includes: illuminance measurement value accumulation unit  32 , light-out determination learning unit  33 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination learning unit  36 , final light-out determination unit  37 , final light-out determination result accumulation unit  38 , sleep determination waiting learning unit  39 , sleep determination unit  40 , human detection sensor measurement value accumulation unit  41 . However, these structural elements may be provided only in the air conditioner, or provided in the server and the air conditioner separately. For example,  FIG.  4    illustrates an example in which these structural elements are provided in the server and the air conditioner separately. 
       FIG.  4    is a block diagram illustrating a functional configuration of sleep onset determination system  10 A according to a variation. Note that in the following description, structural elements substantially the same as the structural elements in the embodiment are assigned the same reference signs and description thereof may be omitted. 
     As illustrated in  FIG.  4   , when server  30   a  is compared with the server in the embodiment, the sleep determination unit is removed from server  30   a.  Final light-out determination learning unit  36  and sleep determination waiting learning unit  39  can freely communicate with communication unit  21  in air conditioner  20   a  via communication unit  31  and network N. Air conditioner  20   a  includes light-out determination unit  26 , final light-out determination unit  27 , and sleep determination unit  28 . Each determination is performed by receiving a learning model from light-out determination unit  33 , final light-out determination learning unit  36 , and sleep determination waiting learning unit  39 , via communication unit  21 , network N, and communication unit  31 . Moreover, human detection sensor  23  is connected to final light-out determination unit  27  such that human detection sensor  23  can freely communicate with final light-out determination unit  27 . The sleep onset determination system according to the present invention can be implemented also when the learning units and the determination units are provided in separate devices as in this variation. Specifically, in the present variation, since light-out determination unit  26  obtains a learning model from light-out determination learning unit  33 , light-out determination unit  26  may obtain a current illuminance value from illuminance measuring unit  22 . In other words, light-out determination unit  36  does not need to obtain a log from illuminance measurement value accumulation unit  32 , it is possible to reduce a communication load between server  30   a  and determination unit  36 . 
     Moreover, in the above embodiment, it has been described that each of the processing units of server  30  (illuminance measurement value accumulation unit  32 , light-out determination learning unit  33 , light-out determination unit  34 , light-out determination result accumulation unit  35 , final light-out determination learning unit  36 , final light-out determination unit  37 , final light-out determination result accumulation unit  38 , sleep determination waiting learning unit  39 , sleep determination unit  40 ) are implemented by a CPU and a control program. For example, the structural elements of each of the processing units may be configured by one or more electronic circuits. The one or more electronic circuits may be each a general-purpose circuit or a dedicated circuit. The one or more electronic circuits may include, for example, a semiconductor device, an integrated circuit (IC), or a large scale integration (LSI). The IC or LSI may be integrated into a single chip or multiple chips. Due to a difference in the degree of integration, the electronic circuit referred here to as an IC or LSI may be referred to as a system LSI, very large scale integration (VLSI), or ultra large scale integration (ULSI). Furthermore, a field programmable gate array (FPGA) which programmable after manufacturing of the LSI can be used for the same purposes. 
     Furthermore, the general and specific aspects of the present invention may be implemented using a system, a device, a method, an integrated circuit, or a computer program. Alternatively, these aspects may be implemented using a non-transitory computer-readable recording medium such as an optical disk, hard disk drive (HDD), or semiconductor memory storing the computer program. Furthermore, these aspects may be implemented using any combination of systems, devices, methods, integrated circuits, computer programs, or recording media. 
     Other than the above, the present invention also includes embodiments as a result of adding various modifications that may be conceived by those skilled in the art to the embodiment, and embodiments obtained by combining structural elements and functions in the embodiment in any manner as long as the combination does not depart from the scope of the present invention. 
     INDUSTRIAL APPLICABILITY 
     The present invention is applicable to an electronic device that is capable of reflecting sleep onset of a user on its control. 
     REFERENCE SIGNS LIST 
       10 ,  10 A sleep onset determination system 
       20 ,  20   a  air conditioner 
       21 ,  31  communication unit 
       22  illuminance measuring unit 
       23  human detection sensor 
       24  air conditioning unit 
       25  control unit 
       26 ,  34  light-out determination unit (illuminance detection unit) 
       27 ,  37  final light-out determination unit (determination unit) 
       28 ,  40  sleep determination unit 
       30 ,  30   a  server 
       32  illuminance measurement value accumulation unit 
       33  light-out determination learning unit (illuminance detection unit) 
       35  light-out determination result accumulation unit (light-out time storage) 
       36  final light-out determination learning unit (determination unit) 
       38  final light-out determination result accumulation unit 
       39  sleep determination waiting learning unit 
       41  human detection sensor measurement value accumulation unit 
     N network