Environmental Control System

Abstract

To accurately estimate a sleep state of a sleeper while considering that a deep body temperature influences on the sleep.SOLUTION: A learning device 30 learns a sleep quality of a sleeper. The learning device 30 includes an acquisition unit 31, a learning unit 32, and a generation unit 33. The acquisition unit 31 acquires a feature quantity related to a deep body temperature of the sleeper, or a feature quantity related to a skin temperature of the sleeper as a state variable. The feature quantity related to the deep body temperature is determined at least on the basis of a first deep body temperature when the sleeper falls asleep. The feature quantity related to the skin temperature is determined at least on the basis of a first skin body temperature when the sleeper falls asleep. The learning unit 32 learns the state variable and the sleep quality in association with each other. The generation unit 33 generates a learning model on the basis of a result of learning of the learning unit 32. The learning model outputs the sleep quality with the input of the feature quantity related to the deep body temperature or the feature quantity related to the skin temperature of the sleeper.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a learning device, an estimation device, and an environment adjustment system. [Background technology]

[0002] Non-Patent Document 1 (<Article> Effect of Body Temperature Fluctuations Before Sleep on Falling Asleep (General)) discloses that deep body temperature affects sleep. Summary of the Invention [Problem to be solved by the invention]

[0003] To accurately estimate a sleeper's sleep state, taking into account that deep body temperature affects sleep. [Means for solving the problem]

[0004] A learning device according to a first aspect learns the quality of sleep of a sleeping person. The learning device includes an acquisition unit, a learning unit, and a generation unit. The acquisition unit acquires state variables. The state variables are features related to the deep body temperature of the sleeping person or features related to the skin temperature of the sleeping person. The features related to the deep body temperature are determined based on at least a first deep body temperature when the sleeping person falls asleep. The features related to the skin temperature are determined based on at least a first skin temperature when the sleeping person falls asleep. The learning unit learns by associating the state variables with the quality of sleep. The generation unit generates a learning model based on the results of learning by the learning unit. The learning model receives as input at least the features related to the deep body temperature or the features related to the skin temperature of the sleeping person, and outputs the quality of sleep.

[0005] In this way, the learning device generates a learning model that can accurately estimate the quality of sleep as a sleeping state of a sleeper using deep body temperature.

[0006] The learning device of a second aspect is the learning device of the first aspect, wherein the feature amount related to the deep body temperature is further determined based on a second deep body temperature acquired before the first deep body temperature, and the feature amount related to the skin temperature is further determined based on a second skin temperature acquired before the first skin temperature.

[0007] This makes it possible to obtain more accurate learning results.

[0008] The learning device of the third aspect is the learning device of the first or second aspect, wherein the acquisition unit further acquires features related to the deep body temperature of the sleeper at normal times, or features related to the skin temperature of the sleeper at normal times.

[0009] This makes it possible to obtain more accurate learning results.

[0010] A learning device of a fourth aspect is a learning device of any one of the first aspect to the third aspect, wherein the acquisition unit acquires the ambient temperature when the sleeper falls asleep, the fingertip blood flow of the sleeper, or an RGB image of the sleeper as a state variable.

[0011] This makes it possible to obtain more accurate learning results.

[0012] A learning device of a fifth aspect is a learning device of any one of the first aspect to the fourth aspect, in which the quality of sleep is determined based on at least one of the time from when the sleeper gets into bed until he or she falls asleep, the deep sleep rate of the sleeper, the number of times the sleeper wakes up during the night, the time it takes for the sleeper to wake up during the night, a questionnaire on sleep quality administered to the sleeper, a questionnaire on daytime performance administered to the sleeper, the amount of hormone secretion by the sleeper, and the hormone concentration of the sleeper.

[0013] A learning device of a sixth aspect is a learning device of any one of the first aspect to the fifth aspect, wherein the feature related to deep body temperature is determined based on at least one of the amount of change in the deep body temperature of the sleeper over a specified period, the maximum value, the minimum value, the mode, the average value, the difference between the maximum and minimum values, the maximum or minimum slope, the average slope, and an index related to the change.

[0014] The learning device of the seventh aspect is a learning device of any one of the first aspect to the sixth aspect, wherein the feature related to skin temperature is determined based on at least one of the amount of change in the skin temperature of the sleeper over a specified period, the maximum value, the minimum value, the mode, the average value, the difference between the maximum and minimum values, the maximum or minimum slope, the average slope, and an index related to change.

[0015] The learning device of the eighth aspect is a learning device of any one of the first aspect to the seventh aspect, wherein the acquisition unit further acquires, as a state variable, any one of the humidity, illuminance, chromaticity, scent, and airflow surrounding the sleeper when the sleeper falls asleep.

[0016] This makes it possible to obtain more accurate learning results.

[0017] A learning device according to a ninth aspect is the learning device according to any one of the first to eighth aspects, wherein the learning unit learns using a plurality of teacher data, the teacher data including state variables and sleep quality.

[0018] An estimation device according to a tenth aspect estimates sleep quality using a learning model generated based on the results of learning by the learning device according to any one of the first to ninth aspects.

[0019] This enables the estimation device to estimate sleep quality with high accuracy.

[0020] The estimation device of an eleventh aspect is the estimation device of the tenth aspect, further comprising a deep body temperature feature amount estimation unit. The deep body temperature feature amount estimation unit estimates a feature amount relating to the deep body temperature of the sleeper from a feature amount relating to the skin temperature.

[0021] This enables the estimation device to reduce the burden on the sleeper caused by measuring deep body temperature.

[0022] The estimation device of a twelfth aspect is the estimation device of the tenth aspect, in which the deep body temperature feature estimation unit estimates the feature amount related to the deep body temperature of the sleeping person by inputting the feature amount related to the skin temperature of the sleeping person based on a learning result of a learning model that has learned the feature amount related to the deep body temperature of the sleeping person in association with the feature amount related to the skin temperature of the sleeping person. The feature amount related to the skin temperature of the sleeping person is determined based on at least a first skin temperature when the sleeping person falls asleep. The feature amount related to the deep body temperature of the sleeping person is determined based on at least a first deep body temperature when the sleeping person falls asleep.

[0023] An environment adjustment system according to a thirteenth aspect includes an estimation device according to any one of the tenth to twelfth aspects, and an environment adjustment device. The environment adjustment device includes an actuator and a control unit. The actuator adjusts the environment around the sleeper. The control unit controls the operation of the actuator based on the quality of sleep of the sleeper estimated by the estimation device. This allows the environmental adjustment system to adjust the environment around the sleeper, thereby improving the quality of sleep of the sleeper. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 illustrates an example of a learning system and an estimation system. [Diagram 2] FIG. 1 is a diagram illustrating a configuration of a learning system. [Diagram 3] FIG. 2 is a diagram showing the configuration of an estimation system and an environment adjustment system 3. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] First Embodiment (1) Overview FIG. 1 is a diagram showing an example of a learning system 1, an estimation system 2, and an environmental adjustment system 3 according to the first embodiment. The learning system 1 acquires various data, learns based on the acquired data, and generates a trained learning model based on the learning results. The estimation system 2 acquires various data, and estimates the sleep quality of a sleeper using the trained learning model generated in the learning system 1. The environmental adjustment system 3 adjusts the environment around the sleeper based on the sleep quality of the sleeper estimated by the estimation system 2. The learning system 1, the estimation system 2, and the environment adjustment system 3 will be described in detail below.

[0026] (2) Learning System 1 2 is a schematic diagram showing an example of a learning system 1 according to the first embodiment. The learning system 1 mainly includes a data acquisition device 10, a teacher data generation device 20, and a learning device 30. Each device can communicate with each other via a network such as the Internet.

[0027] (2-1) Data acquisition device 10 The data acquisition device 10 includes, for example, a first acquisition device 11 that acquires data on the temperature around the sleeper, a second acquisition device 12 that acquires data on the skin temperature of the sleeper, a third acquisition device 13 that acquires data on the deep body temperature of the sleeper, and a fourth acquisition device 14 that acquires data on the quality of sleep of the sleeper. In the first embodiment, the first acquisition device 11, the second acquisition device 12, and the third acquisition device 13 acquire data continuously from 30 minutes before the sleeper goes to bed until the sleeper wakes up, but the data may be acquired at a predetermined time interval or at any timing. In the first embodiment, the fourth acquisition device 14 acquires data after the sleeper wakes up. The data acquired by the first acquisition device 11, the second acquisition device 12, the third acquisition device 13, and the fourth acquisition device 14 is linked to information on the time when the data was acquired.

[0028] The data acquired by the first acquisition device 11, the second acquisition device 12, the third acquisition device 13, and the fourth acquisition device 14 are each transmitted via a network to the teacher data generation device 20. Note that the first acquisition device 11, the second acquisition device 12, the third acquisition device 13, and the fourth acquisition device 14 may transmit only a portion of the acquired data in response to a request from the teacher data generation device 20 to the teacher data generation device 20.

[0029] (2-1-1) First acquisition device 11 The first acquisition device 11 is a device such as a temperature sensor or a thermograph, or a device including them. For example, the first acquisition device 11 may be an air conditioning device including a temperature sensor. The first acquisition device 11 is not particularly limited, but is at least a device capable of acquiring data on the temperature around a sleeper.

[0030] (2-1-2)Second acquisition device 12 The second acquisition device 12 is a device such as a skin temperature sensor, or a device including a skin temperature sensor. For example, the second acquisition device 12 may be a wearable sensor including a skin temperature sensor. The second acquisition device 12 is not particularly limited, but is at least a device capable of acquiring data on the skin temperature of a sleeper.

[0031] (2-1-3) Third acquisition device 13 The third acquisition device 13 is a device such as a deep body temperature sensor or a device including a deep body temperature sensor. For example, the third acquisition device 13 may be a tympanic thermometer. The third acquisition device 13 is not particularly limited, but is at least a device capable of acquiring data of the deep body temperature of a sleeper.

[0032] (2-1-4) Fourth acquisition device 14 The fourth acquisition device 14 is a device for acquiring the quality of sleep of the sleeper. In the first embodiment, the quality of sleep is determined based on a questionnaire on the quality of sleep and daytime performance administered to the sleeper. The questionnaire on the quality of sleep and daytime performance is, for example, an OSA sleep inventory MA version. The fourth acquisition device 14 may be, for example, a mobile terminal or the like. The mobile terminal as the fourth acquisition device 14 acquires data on sleep, for example, by having the sleeper complete the OSA sleep inventory MA version via the mobile terminal after waking up. Note that the fourth acquisition device 14 is not limited to a mobile terminal, and may be any device capable of acquiring the results of the sleeper completing the OSA sleep inventory MA version.

[0033] (2-2) Teacher data generating device 20 The teacher data generation device 20 may be a computer having a known hardware configuration such as a CPU (Central Processing Unit), memory, storage, an input / output interface, a communication interface, an input device, a display device, and a data bus, a known operation system, etc. installed, and further having a server function. In addition, a GPU (Graphics Processing Unit) may be provided as necessary. Alternatively, it may be a virtual server provided by using one or more computers. In addition, the teacher data generation device 20 in the present disclosure preferably has a large capacity memory.

[0034] The teacher data generating device 20 includes a data acquiring unit 21 , a feature extracting unit 22 , a teacher data generating unit 23 , an acquired data storage unit 24 , and a teacher data storage unit 25 .

[0035] (2-2-1) Data Acquisition Unit 21 The data acquisition unit 21 performs a process of acquiring various data acquired by the data acquisition device 10 by receiving the data via a network. The various data acquired by the data acquisition unit 21 are stored in the acquired data storage unit 24. The various data stored in the acquired data storage unit 24 are associated with information on the time when each data was acquired by the data acquisition device 10.

[0036] In the first embodiment, the data acquisition unit 21 acquires data of the first ambient temperature and the second ambient temperature from the first acquisition device 11, data of the first skin temperature and the second skin temperature from the second acquisition device 12, data of the first deep body temperature and the second deep body temperature from the third acquisition device 13, and data of the quality of sleep from the fourth acquisition device 14. Each data will be described below. In the following description, the time when the sleeper falls asleep is preferably acquired at the timing when the sleeper falls asleep by measuring the sleeper's falling asleep using a sensor or the like, but is not limited to this. The time when the sleeper falls asleep is at least the timing after the sleeper goes to bed, and may be, for example, the timing after a predetermined time has passed since the sleeper goes to bed. In the first embodiment, the time when the sleeper falls asleep is 30 minutes after the sleeper goes to bed, but is not limited to this.

[0037] (2-2-1-1) First ambient temperature and second ambient temperature data The first ambient temperature is the temperature around the sleeper when the sleeper falls asleep. The second ambient temperature is the temperature around the sleeper acquired at a timing earlier than the first ambient temperature. The second ambient temperature data is acquired at least at a timing before the sleeper goes to bed. For example, the second ambient temperature data may be data acquired any time before the sleeper goes to bed. In the first embodiment, the second ambient temperature data is data of the temperature around the sleeper 30 minutes before the sleeper goes to bed, but is not limited to this.

[0038] (2-2-1-2) First skin temperature and second skin temperature data The first skin temperature is the skin temperature when the sleeper falls asleep.

[0039] The second skin temperature is the skin temperature of the sleeping person acquired at a timing earlier than the first skin temperature. The second skin temperature data is preferably acquired at a timing before the sleeping person goes to bed. For example, the second skin temperature data is preferably data acquired any time before the sleeping person goes to bed. In the first embodiment, the second skin temperature data is data of the skin temperature of the sleeping person 30 minutes before the sleeping person goes to bed, but is not limited to this.

[0040] (2-2-1-3) First and second deep body temperature data The first deep body temperature is the deep body temperature when the sleeper falls asleep. The second deep body temperature is the deep body temperature of the sleeper acquired at a timing earlier than the first deep body temperature. The second deep body temperature data is preferably acquired at a timing before the sleeper goes to bed. For example, the second deep body temperature data is preferably data acquired any time before the sleeper goes to bed. In the first embodiment, the second deep body temperature data is data of the sleeper's deep body temperature 30 minutes before the sleeper goes to bed, but is not limited to this.

[0041] (2-2-1-4) Sleep quality data Sleep quality is determined based on the OSA Sleep Questionnaire MA version as described above. Sleep quality data is obtained after the sleeper completes the OSA Sleep Questionnaire MA version after waking up.

[0042] (2-2-2) Feature Extraction Unit 22 The feature extraction unit 22 performs processing to extract skin temperature feature amounts and deep body temperature feature amounts from the first skin temperature and second skin temperature data and the first deep body temperature and second deep body temperature data stored in the acquired data storage unit 24. The skin temperature feature amounts and deep body temperature feature amounts extracted by the feature extraction unit 22 are stored in the acquired data storage unit 24. The skin temperature feature amounts and deep body temperature feature amounts will be described below.

[0043] (2-2-2-1) Skin temperature feature The skin temperature feature is a feature related to the skin temperature of the sleeper and is extracted from the data of the first skin temperature and the second skin temperature. Specifically, the skin temperature feature is a feature determined based on any of the change value, maximum value, minimum value, mode, average value, difference between the maximum value and the minimum value, maximum or minimum slope, average slope, and index related to the change of the first skin temperature and the second skin temperature. The index related to the change is, for example, an index showing a change tendency such as the slope of the deep body temperature correlated with the skin temperature. Note that the method of extracting the skin temperature feature from the data of the first skin temperature and the second skin temperature is not particularly limited, but it is preferable to select useful features by performing feature selection using a filter method, a wrapper method, an embedding method, or the like. In addition, an interaction feature may be used.

[0044] (2-2-2-2) Deep body temperature feature The deep body temperature feature is a feature related to the deep body temperature of the sleeper and is extracted from the data of the first deep body temperature and the second deep body temperature. Specifically, the deep body temperature feature is a feature determined based on any of the change value, maximum value, minimum value, mode, average value, difference between the maximum value and the minimum value, maximum or minimum slope, average slope, and index related to the change of the first deep body temperature and the second deep body temperature. The index related to the change is, for example, an index indicating a change tendency such as the slope of the skin temperature correlated with the deep body temperature. The method of extracting the deep body temperature feature from the data of the first deep body temperature and the second deep body temperature is not particularly limited, but it is preferable to select a useful feature by performing feature selection using a filter method, a wrapper method, an embedding method, or the like. In addition, an interaction feature may be used.

[0045] (2-2-3) Teacher data generation unit 23 The teacher data generating unit 23 extracts state variables and correct answer data from the acquired data storage unit 24 to generate teacher data (teacher data set). The teacher data is a data set for the learning device 30 to learn about sleep quality. In the first embodiment, data on the first ambient temperature, the second ambient temperature, the skin temperature feature, the deep body temperature feature, and the sleep quality are used as the teacher data.

[0046] Specifically, the teacher data generating unit 23 first extracts data on the first ambient temperature, the second ambient temperature, the skin temperature feature, the deep body temperature feature, and the quality of sleep from the acquired data storage unit 24. At this time, the teacher data generating unit 23 extracts the data based on time information linked to each data. Next, the teacher data generating unit 23 generates teacher data by using the first ambient temperature, the second ambient temperature, the skin temperature feature, and the deep body temperature feature as state variables and the data on the quality of sleep as correct answer data. The teacher data generated by the teacher data generating unit 23 is stored in the teacher data storage unit 35.

[0047] (2-3) Learning device 30 The learning device 30 is a high-performance computer that includes a known hardware configuration, such as a CPU (Central Processing Unit), memory, storage, an input / output interface, a communication interface, an input device, a display device, and a data bus (not shown), has a known operation system installed, and further has a server function. A GPU (Graphics Processing Unit) may also be provided as necessary. Alternatively, the learning device 30 may be a virtual server provided by using one or more computers.

[0048] The learning device 30 includes a teacher data acquisition unit 31, a learning unit 32, and a learning model generation unit 33.

[0049] (2-3-1) Teacher data acquisition unit 31 The teacher data acquiring unit 31 acquires a plurality of teacher data from the teacher data generating device 20 via a network. The teacher data acquired by the teacher data acquiring unit 31 is stored in a memory or the like.

[0050] (2-3-2) Learning Section 32 A learning model is provided in advance in the learning unit 32. The learning unit 32 causes the learning model to learn (for example, supervised learning) based on a plurality of teacher data acquired by the teacher data acquisition unit 31. Although there are no particular limitations on the learning method performed by the learning model, it is preferable to use multiple regression analysis, random forest, or the like.

[0051] The learning model in the first embodiment performs learning for the purpose of outputting an estimated value of sleep quality from input data inputted in an estimation system 2 (FIG. 3) described later.

[0052] (2-3-3) Learning model generation unit 33 The learning model generation unit 33 outputs the learning model as a trained learning model at a stage where the learning in the learning unit 32 progresses and the estimation accuracy of the learning model is improved, thereby generating a learning model to be used in the estimation device 50. The learning model output from the learning model generation unit 33 is capable of outputting an estimated value of sleep quality from input data input to the estimation system 2 with a predetermined accuracy or higher.

[0053] The learning model generated in the learning model generation unit 33 is distributed to the estimation device 50 (described later) via a network. Alternatively, the learning model generated in the learning model generation unit 33 is distributed to the estimation device 50 via a storage medium.

[0054] (3) Features of Learning System 1 (3-1) The learning device 30 included in the learning system 1 of the first embodiment is a learning device 30 that learns the quality of sleep of a sleeper. The learning device 30 includes a teacher data acquisition unit 31 as an acquisition unit, a learning unit 32, and a learning model generation unit 33 as a generation unit. The teacher data acquisition unit 31 acquires a first ambient temperature, a second ambient temperature, a feature amount related to deep body temperature, and a feature amount related to skin temperature as state variables. The first ambient temperature is the temperature around the sleeper when the sleeper falls asleep. The second ambient temperature is the temperature around the sleeper acquired before the first ambient temperature. The feature amount related to deep body temperature is determined based on at least the first deep body temperature when the sleeper falls asleep. The feature amount related to skin temperature is determined based on at least the first skin temperature when the sleeper falls asleep. The learning unit 32 learns by associating the state variables with the quality of sleep. The learning model generation unit 33 generates a learning model based on the result of learning by the learning unit 32. The learning model estimates sleep quality using feature quantities related to the first ambient temperature, the second ambient temperature, the deep body temperature, and the skin temperature as input.

[0055] Conventionally, research has been conducted on the effect of body temperature fluctuations before sleep on falling asleep, as in Non-Patent Document 1. For example, Non-Patent Document 1 reports that significant drowsiness occurs during the time period when deep body temperature drops, and in particular, the time when a rapid drop in deep body temperature occurs significantly affects the quality of sleep.

[0056] Specifically, when humans become sleepy, they release heat by transferring their core body temperature to the extremities and expanding the temperature-regulating blood vessels called AVAs (arteriovenous anastomoses) that exist in the palms of the hands, soles of the feet, and face. This lowers the core body temperature and increases the efficiency of rest for the brain and body during sleep. By raising body temperature during the day by exercising or bathing, the core body temperature tends to drop when going to bed, but if the core body temperature does not rise during the day or the bedroom is hot, the core body temperature does not drop and deep sleep cannot be achieved, resulting in poor quality sleep. Deep sleep is characterized by being most common in the first three hours after falling asleep and becoming less frequent thereafter. It has been found that the transition of the core body temperature affects the comfort of sleep, which can be rephrased as deep and high-quality sleep. To summarize the above, it has been found that the relationship between core body temperature, skin temperature, and sleep depth in ideal sleep is as shown in Figure 5. Other findings include that with age, the time it takes to fall asleep increases, the percentage of deep sleep decreases, and the total sleep time decreases.

[0057] The learning device 30 of the first embodiment acquires features related to the deep body temperature of a sleeper as a state variable, and by learning, is capable of generating a learning model that can more accurately estimate the sleep quality as a sleep state of the sleeper.

[0058] (3-2) The second feature amount acquired by the learning device 30 is determined based on at least the first skin temperature and the second skin temperature. The first skin temperature is the skin temperature of the sleeper when the sleeper falls asleep. The second skin temperature is the skin temperature of the sleeper acquired before the first skin temperature.

[0059] In this embodiment, the learning device 30 acquires data on the skin temperature of the sleeper 30 minutes after the sleeper goes to bed as the first skin temperature, and data on the skin temperature of the sleeper 30 minutes before the sleeper goes to bed as the second skin temperature. The learning device 30 of this embodiment also acquires the first deep body temperature at the same time as the first skin temperature, and the second deep body temperature at the same time as the second skin temperature.

[0060] This enables the learning device 30 to generate a learning model that outputs a more accurate deep body temperature feature.

[0061] (3-3) The learning device 30 learns using a plurality of teacher data. The teacher data includes data on sleep quality, data on a first ambient temperature, data on a second ambient temperature, a deep body temperature feature as a feature related to the deep body temperature, and a skin temperature feature as a feature related to the skin temperature.

[0062] In this embodiment, sleep quality is determined based on at least one of the time from when the sleeper goes to bed until they fall asleep, the deep sleep rate, the number of times they wake up during sleep, the duration of awakenings during sleep, and the OSA sleep inventory MA version.

[0063] In addition, the deep body temperature characteristic is determined based on at least one of the amount of change in the deep body temperature of the sleeper over a specified period, the maximum value, the minimum value, the mode, the average value, the difference between the maximum and minimum values, the maximum or minimum slope, and the average slope.

[0064] Furthermore, the skin temperature feature is determined based on at least one of the amount of change in the sleeper's skin temperature over a specified period, the maximum value, the minimum value, the mode, the average value, the difference between the maximum and minimum values, the maximum or minimum slope, and the average slope.

[0065] This enables the learning device 30 of the present embodiment to generate a learning model that outputs sleep quality with high accuracy.

[0066] (4) Variations (4-1) Variation 1 In the first embodiment, the sleep quality is determined based on the OSA sleep questionnaire MA version, but the sleep quality may be determined based on other methods. Also, the fourth acquisition device 14 may be a device other than a mobile terminal.

[0067] For example, the quality of sleep may be determined from the sleeper's heart rate, body movement, the ratio of deep sleep time, the time from when the sleeper gets into bed to when he or she falls asleep, the sleeper's hormone secretion amount, the sleeper's hormone concentration, or any questionnaire regarding the sleep quality and daytime performance administered to the sleeper, etc. The data on sleep quality is not particularly limited, and the fourth acquisition device 14 is not particularly limited as long as it is capable of acquiring at least data on sleep quality.

[0068] (4-2) Variation 2 The data acquisition device 10 may acquire data other than the data shown above. For example, the data acquisition device 10 may acquire environmental data, target data, or sleep data. Specifically, the environmental data is data such as humidity, illuminance, chromaticity, scent, or airflow around the sleeper. The target data is data such as the fingertip blood flow of the sleeper, an RGB image for estimating the fingertip blood flow of the sleeper, heart rate, heart rate variability, respiratory rate, galvanic skin reaction, amount of clothing, type of bedding, daytime activity status, and pillow temperature. The sleep data is data such as the body movement, sleep state, or time until falling asleep of the sleeper. Note that these data may be acquired by the first acquisition device 11, the second acquisition device 12, the third acquisition device 13, and the fourth acquisition device 14 included in the data acquisition device 10, or by devices other than these. The learning device 30 can use the data acquired by those devices as teacher data.

[0069] The learning device 30 can generate a learning model that estimates a core body temperature feature with higher accuracy by using the above-mentioned data as state variables.

[0070] (4-3) Variation 3 In the first embodiment, the first ambient temperature and the second ambient temperature are acquired by the same device (first acquisition device 11), but the first ambient temperature and the second ambient temperature may be acquired by different devices.

[0071] Furthermore, the first acquisition device 11 etc. may acquire data on the temperature around the sleeper at a predetermined timing as a third ambient temperature in addition to the first ambient temperature and the second ambient temperature.

[0072] (4-4) Variation 4 In the first embodiment, the teacher data generation device 20 extracts the skin temperature feature and the deep body temperature feature, but the skin temperature feature and the deep body temperature feature may be extracted in the learning device 30. Alternatively, the skin temperature feature may be extracted in the second acquisition device 12, and the deep body temperature feature may be extracted in the third acquisition device 13. The process of extracting the feature from each data may be performed in any device.

[0073] (4-5) Variation 5 In the first embodiment, the learning system 1 extracts the skin temperature feature and the deep body temperature feature to learn, but the learning system 1 may use deep learning to learn. In other words, the learning unit 32 may automatically extract appropriate feature values ​​from the skin temperature, the deep body temperature, the first ambient temperature, and the second ambient temperature to learn. In this case, the skin temperature and the deep body temperature may be acquired only when the sleeper falls asleep.

[0074] (4-6) Variation 6 In the first embodiment, the teacher data is a teacher data set related to a sleeping person. Here, the multiple teacher data used for learning by the learning unit 32 may be teacher data sets related to different targets (sleeping people).

[0075] On the other hand, the multiple teacher data used for learning by the learning unit 32 of the first embodiment may be a teacher data set related to a specific sleeper. In this case, the data acquisition device 10 may have a personal information acquisition device that acquires personal information of the specific sleeper. The personal information acquisition device is, for example, a mobile terminal, and personal information of the specific sleeper is input. The personal information is, for example, information such as the normal skin temperature, normal deep body temperature, age, sex, height, weight, and average normal skin temperature of the same age of the specific sleeper.

[0076] This enables the learning device 30 to contribute to generating a learning model that estimates deep body temperature features with higher accuracy for a specific sleeper.

[0077] (5) Estimation System 2 3 is a schematic diagram showing an example of the estimation system 2 and the environment adjustment system 3 according to the first embodiment. The estimation system 2 has devices such as an input data acquisition device 40 and an estimation device 50. Each device is capable of communicating via a predetermined network such as the Internet.

[0078] (5-1) Input data acquisition device 40 The input data acquisition device 40 includes a fifth acquisition device 41 that acquires data on the temperature around the sleeping person, a sixth acquisition device 42 that acquires data on the skin temperature of the sleeping person, and a seventh acquisition device 43 that acquires data on the deep body temperature of the sleeping person. The fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43 each acquire predetermined data.

[0079] In the first embodiment, the fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43 continuously acquire data from 30 minutes before the sleeper goes to bed, but the data may be acquired at a predetermined time interval or at any timing. The data acquired by the fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43 is associated with information on the time when the data was acquired.

[0080] The data acquired by the fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43 are transmitted to the estimation device 50 via the network. Note that the fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43 may transmit only a portion of the acquired data in response to a request from the estimation device 50 to the estimation device 50.

[0081] The fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43, and the data acquired by the fifth acquisition device 41, the sixth acquisition device 42, and the seventh acquisition device 43 will be described below. In the following description, when the sleeper falls asleep, it is preferable to measure the sleeper's sleep using a sensor or the like and acquire the data at the timing when the sleeper falls asleep, but this is not limited to this. The time when the sleeper falls asleep is at least the timing after the sleeper goes to bed, and may be, for example, the timing after any time has passed since the sleeper goes to bed. In the first embodiment, the data of the second ambient temperature is data of the temperature around the sleeper 30 minutes before the sleeper goes to bed, but this is not limited to this.

[0082] (5-1-1) Fifth acquisition device 41 The fifth acquisition device 41 is a device such as a temperature sensor or a thermograph, or a device including them. For example, the fifth acquisition device 41 may be an air conditioning device including a temperature sensor. The fifth acquisition device 41 is not particularly limited, but is at least a device capable of acquiring data on the temperature around the sleeper.

[0083] In the first embodiment, the fifth acquisition device 41 acquires at least a third ambient temperature and a fourth ambient temperature.

[0084] The third ambient temperature is data on the temperature around the sleeper when the sleeper falls asleep. The fourth ambient temperature is data on the temperature around the sleeper acquired before the third ambient temperature. The fourth ambient temperature is acquired at least before the sleeper goes to bed. For example, the fourth ambient temperature may be data acquired any time before the sleeper goes to bed. In the first embodiment, the fourth ambient temperature data is data on the temperature around the sleeper 30 minutes before the sleeper goes to bed, but is not limited to this.

[0085] (5-1-2) Sixth acquisition device 42 The sixth acquisition device 42 is a device such as a skin temperature sensor or a device including the same. For example, the sixth acquisition device 42 may be a wearable sensor including a skin temperature sensor. The sixth acquisition device 42 is not particularly limited, but is at least a device capable of acquiring data of the third skin temperature and the fourth skin temperature of the sleeper.

[0086] The third skin temperature is the skin temperature when the sleeper falls asleep. The fourth skin temperature is the skin temperature of the sleeper acquired at a time before the third skin temperature. The fourth skin temperature data is preferably acquired before the sleeper goes to bed. For example, the fourth skin temperature data is preferably acquired any time before the sleeper goes to bed. In the first embodiment, the fourth skin temperature data is the skin temperature data of the sleeper 30 minutes before the sleeper goes to bed, but is not limited to this.

[0087] (5-1-3) Seventh acquisition device 43 The seventh acquisition device 43 is a device such as a deep body temperature sensor or a device including the same. For example, the seventh acquisition device 43 may be a tympanic thermometer. The seventh acquisition device 43 is not particularly limited, but is at least a device capable of acquiring data of the third deep body temperature and the fourth deep body temperature of the sleeper.

[0088] The third deep body temperature is the deep body temperature when the sleeper falls asleep. The fourth deep body temperature is the deep body temperature of the sleeper acquired at a timing earlier than the third deep body temperature. The fourth deep body temperature data is preferably acquired at a timing before the sleeper goes to bed. For example, the fourth deep body temperature data is preferably data acquired any time before the sleeper goes to bed. In the first embodiment, the fourth deep body temperature data is data of the deep body temperature of the sleeper 30 minutes before the sleeper goes to bed, but is not limited to this.

[0089] (5-2) Estimation device 50 The estimation device 50 is a high-performance computer having a known hardware configuration such as a CPU (Central Processing Unit), memory, storage, an input / output interface, a communication interface, an input device, a display device, and a data bus, a known operation system, etc. installed therein, and further having a server function. In addition, a GPU (Graphics Processing Unit) may be provided as necessary. Alternatively, the estimation device 50 may be a virtual server provided by using one or more computers.

[0090] A storage unit such as a memory of the estimation device 50 stores a learned learning model that has been generated in advance by the learning system 1, and various programs for executing this learning model.

[0091] The estimation device 50 includes an input data acquisition unit 51, a feature extraction unit 52, an estimation unit 53, and an output unit 54.

[0092] (5-2-1) Input data acquisition unit 51 The input data acquisition unit 51 acquires the third ambient temperature and fourth ambient temperature data acquired by the fifth acquisition device 41, the third skin temperature and fourth skin temperature data acquired by the sixth acquisition device 42, and the third deep body temperature and fourth deep body temperature data acquired by the seventh acquisition device 43 by receiving them via a network. The data acquired by the input data acquisition unit 51 is stored in a memory or the like.

[0093] (5-2-2) Feature extraction unit 52 The feature extraction unit 52 extracts skin temperature features from the third skin temperature and fourth skin temperature data acquired by the input data acquisition unit 51. The feature extraction unit 52 also extracts deep body temperature features from the third deep body temperature and fourth deep body temperature data acquired by the input data acquisition unit 51. The skin temperature features and deep body temperature features are approximately the same features as the skin temperature features and deep body temperature features extracted by the feature extraction unit 22 of the learning system, and therefore a description thereof will be omitted.

[0094] (5-2-3) Estimation part 53 The estimation unit 53 estimates the quality of sleep using the third and fourth ambient temperature data acquired by the input data acquisition unit 51 and the skin temperature feature and deep body temperature feature extracted by the feature extraction unit 22 as input data. Estimation of sleep quality is, for example, classifying the quality of sleep of the sleeper into three stages (good, normal, bad, etc.). The estimation is, for example, supervised learning. The estimation method is not particularly limited, but it is preferable to use multiple regression analysis, random forest, etc. For example, as a result of an experiment using these methods, the accuracy rate according to the input data was 0.762, 0.930, or 0.803, and it was possible to estimate the quality of sleep with high accuracy.

[0095] (5-2-4) Output section 54 The output unit 54 outputs the estimated value of the sleep quality estimated by the estimation unit 53. Specifically, the output unit 54 transmits the estimated value of the sleep quality as an output to another device, or displays it on a display or the like.

[0096] (6) Features of Estimation System 2 The estimation device 50 included in the estimation system 2 in the first embodiment estimates the sleep quality of a sleeper by using a trained learning model generated based on the results of learning by the learning device 30.

[0097] This allows the estimation system 2 of the first embodiment to use the learning model generated in the learning system 1 to accurately estimate the quality of sleep of a sleeper.

[0098] (7) Variations (7-1) Variation 1 The input data acquisition unit 51 may acquire input data other than the data described above. For example, the input data acquisition unit 51 may acquire environmental data, target data, sleep data, etc. The input data acquisition unit 51 acquires input data according to the specifications of the learning model.

[0099] (7-2) Variation 2 In the first embodiment, the estimation device 50 extracts the skin temperature feature, but the skin temperature feature may be extracted by the sixth acquisition device 42. Alternatively, the skin temperature feature may be extracted by a device other than the sixth acquisition device 42.

[0100] (7-3) Variation 3 In the first embodiment, the estimation system 2 extracts the skin temperature feature amount and performs learning, but the estimation system 2 may perform learning using deep learning. In other words, the estimation unit 53 may automatically extract appropriate feature amounts from the third ambient temperature, the fourth ambient temperature, the third skin temperature, and the fourth skin temperature and perform learning.

[0101] (8) Environmental Control System 3 The environment adjustment system 3 is a system that adjusts the environment around the sleeper based on the sleep quality of the sleeper estimated by the estimation system 2. The environment adjustment system 3 includes the estimation device 50 of the first embodiment and an environment adjustment device 60. An example of the environment adjustment device 60 will be described below.

[0102] (8-1)Environmental adjustment device 60 The environmental adjustment device 60 is a device that adjusts the environment around a sleeper, and is, for example, an air conditioner. The environmental adjustment device 60 mainly includes an acquisition unit 61, a control unit 62, and an actuator 63.

[0103] (8-1-1) Acquisition part 61 The acquisition unit 61 acquires an estimated value of sleep quality from the estimation device 50 via a network such as the Internet. The acquisition unit 61 also acquires various data from the input data acquisition device 40 via the network. The acquisition unit 61 stores the information acquired from the estimation device 50 and the input data acquisition device 40 in a storage device of the control unit 62 described later.

[0104] (8-1-2) Control unit 62 The control unit 62 is realized by a computer, and includes a control and arithmetic device and a storage device, not shown. A processor such as a CPU or a GPU can be used as the control and arithmetic device. The control and arithmetic device reads a program stored in the storage device, and performs predetermined arithmetic processing and the like according to the program. Furthermore, the control and arithmetic device can write the results of calculations to the storage device and read information stored in the storage device according to the program.

[0105] Specifically, the storage device of the control unit 62 in the first embodiment stores information acquired by the acquisition unit 61. The storage device also stores control instructions for a predetermined period before the acquisition unit 61 acquires an estimated value of sleep quality. The control instructions are instruction signals for controlling the operation of an actuator 63, which will be described later.

[0106] The control arithmetic unit of the control unit 62 performs calculations based on the information stored in the storage device and the control instructions, and sends control instructions to the actuator 63 to improve the quality of sleep.

[0107] (8-1-3) Actuator 63 The actuator 63 operates in response to a control instruction received from the control and arithmetic device of the control unit 62. The actuator 63 is, for example, a compressor, an expansion valve, a fan, a flap, or the like of an air conditioner.

[0108] (9) Features The environment adjustment system 3 in the first embodiment includes an estimation device 50 and an environment adjustment device 60. The environment adjustment device 60 has an actuator 63 and a control unit 62. The actuator 63 adjusts the environment around the sleeper. The control unit 62 controls the operation of the actuator based on the quality of sleep of the sleeper estimated by the estimation device 50.

[0109] The environmental adjustment system 3 uses the human characteristic that a person feels very sleepy when their core body temperature drops as described in Non-Patent Document 1, and when it predicts that the quality of sleep will decrease, for example, it lowers the temperature around the sleeper (e.g., by 1 degree) so that the quality of sleep improves (the core body temperature decreases). Alternatively, the environmental adjustment system 3 performs environmental adjustment to strengthen the airflow around the sleeper. In this way, the environmental adjustment system 3 contributes to improving the quality of sleep of the sleeper.

[0110] (10) Variations In the first embodiment, the acquisition unit 61 acquires various data from the input data acquisition device 40, but the acquisition unit 61 may acquire various data from sensors appropriately attached to the environment adjustment system 3.

[0111] Furthermore, the environment adjustment system 3 and the estimation system 2 may be stored in the same housing, and therefore the acquisition unit 61 may acquire each piece of information without going through a network.

[0112] <Second embodiment> Hereinafter, the learning system 1 and the estimation system 2 according to the second embodiment of the present invention will be described in detail, focusing on the differences from the systems according to the first embodiment.

[0113] (11) Learning system 1 of the second embodiment (11-1) Data acquisition device 10 The data acquisition device 10 in the learning system 1 of the second embodiment is generally the same as the data acquisition device 10 of the first embodiment.

[0114] (11-2) Teacher data generating device 20 In the learning system 1 of the second embodiment, the teacher data generating device 20 generates teacher data for the learning device 30 to learn the deep body temperature feature of a sleeping person. For convenience, the teacher data generated to learn the sleep quality of the sleeping person described in the first embodiment is referred to as the first teacher data, and the teacher data to learn the deep body temperature feature is referred to as the second teacher data.

[0115] The teacher data generation unit 23 of the teacher data generation device 20 first extracts data on the first ambient temperature, the second ambient temperature, the skin temperature feature, and the deep body temperature feature from the acquired data storage unit 24. Next, the teacher data generation unit 23 generates second teacher data by using the first ambient temperature, the second ambient temperature, and the skin temperature feature as state variables and the data on the deep body temperature feature as correct answer data. The second teacher data generated by the teacher data generation unit 23 is stored in the teacher data storage unit 35.

[0116] The teacher data generating unit 23 in the second embodiment also generates first teacher data in the same manner as in the first embodiment.

[0117] (11-3) Learning device 30 The learning device 30 causes the learning model to learn based on a plurality of teacher data acquired by the teacher data acquisition unit 31. For convenience, the learning model that learns the sleep quality of a sleeper using the first teacher data is referred to as the first learning model, and the learning model that learns the deep body temperature feature amount using the second teacher data is referred to as the second learning model. The first learning model is the learning model described in the first embodiment.

[0118] The learning device 30 performs learning using a plurality of second teacher data acquired by the teacher data generating device 20, with the aim of outputting an estimated value of the deep body temperature feature from the input data inputted to the estimation system 2.

[0119] The learning model generation unit 33 outputs the second learning model as a trained second learning model at a stage where the learning in the learning unit 32 progresses and the estimation accuracy of the learning model is improved, thereby generating a second learning model to be used in the estimation device 50. The second learning model output from the learning model generation unit 33 is capable of outputting an estimated value of the deep body temperature feature from the input data input to the estimation system 2 with a predetermined accuracy or higher.

[0120] The learning device 30 of the second embodiment also generates a first learning model, which is a learning model for learning the quality of sleep of a sleeper, by using the first teacher data in the same manner as in the first embodiment.

[0121] (12) Features of the learning system 1 according to the second embodiment The learning device 30 included in the learning system 1 of the second embodiment is a learning device 30 that learns the quality of sleep of a sleeper. The learning device 30 includes a teacher data acquisition unit 31 as an acquisition unit, a learning unit 32, and a learning model generation unit 33 as a generation unit. The teacher data acquisition unit 31 acquires a first ambient temperature, a second ambient temperature, a feature amount related to a deep body temperature, and a feature amount related to a skin temperature as state variables. The first ambient temperature is the temperature around the sleeper when the sleeper falls asleep. The second ambient temperature is the temperature around the sleeper acquired before the first ambient temperature. The feature amount related to the deep body temperature is determined based on at least the first deep body temperature when the sleeper falls asleep. The feature amount related to the skin temperature is determined based on at least the first skin temperature when the sleeper falls asleep. The learning unit 32 learns by associating the state variables with the quality of sleep. The learning model generation unit 33 generates a learning model based on the result of learning by the learning unit 32. The learning model estimates the quality of sleep using the feature amounts related to the first ambient temperature, the second ambient temperature, and the skin temperature as input.

[0122] The deep body temperature of a sleeper has a significant impact on the quality of sleep. However, to measure deep body temperature, a device such as a rectal thermometer or tympanic thermometer must be attached, which places a greater burden on the sleeper than measuring skin temperature.

[0123] The learning device 30 shown in the second embodiment acquires information for calculating the feature amount related to the deep body temperature of the sleeping person in advance, and generates a learning model that outputs the first feature amount, which is information related to the deep body temperature, by learning. This makes it possible to reduce the burden on the sleeping person associated with acquiring information related to the deep body temperature.

[0124] (13) Variations In the second embodiment, the first teacher data and the second teacher data are generated by the same teacher data generation device 20, but this is not limited to this. The first teacher data and the second teacher data may be generated by different hardware.

[0125] Furthermore, the first learning model and the second learning model are trained on the same learning device 30, but this is not limited thereto. The first learning model and the second learning model may be trained on different hardware.

[0126] (14) Estimation System 2 (14-1) Input data acquisition device 40 The input data acquisition device 40 of the second embodiment includes the fifth acquisition device 41 and the sixth acquisition device 42 of the first embodiment. In other words, the input data acquisition device 40 of the second embodiment does not include the seventh acquisition device 43. The fifth acquisition device 41 and the sixth acquisition device 42 acquire substantially the same data as in the first embodiment.

[0127] (14-2) Estimation device 50 The estimation device 50 of the second embodiment stores a first learning model and a second learning model that have been trained in advance and generated by the learning system 1, as well as various programs for executing each learning model.

[0128] The estimation unit 53 estimates the deep body temperature feature using the second learning model with the data of the third and fourth ambient temperatures acquired by the input data acquisition unit 51 and the skin temperature feature extracted by the feature extraction unit 22 as input data. The estimation unit 53 can estimate whether the deep body temperature feature will be an increase of 0.2°C or more, a flat (between a decrease of 0.2°C and an increase of 0.2°C), or a decrease of 0.2°C or more when comparing the average eardrum temperature every minute from 30 minutes to 16 minutes before falling asleep with the average eardrum temperature every minute from 15 minutes before falling asleep. The estimation is, for example, supervised learning. Here, when there is variation in the number of samples, a method such as undersampling or oversampling can be performed. Table (1) shown below shows the accuracy rate in an experiment in which the deep body temperature feature was estimated using the estimation unit 53. In this way, a combination of experimental conditions for estimating the deep body temperature feature was compared.

[0129] [Table 1]

[0130] The estimation device 50 of the second embodiment estimates sleep quality using the deep body temperature feature estimated by the estimation unit 53. Specifically, the estimation unit 53 estimates sleep quality using a first learning model with the third and fourth ambient temperature data acquired by the input data acquisition unit 51, the skin temperature feature extracted by the feature extraction unit 22, and the deep body temperature feature estimated by the estimation unit 53 as input data. The output unit 54 outputs an estimate of the sleep quality estimated using the first learning model.

[0131] (15) Features The estimation device 50 included in the estimation system 2 in the second embodiment estimates the sleep quality of a sleeper by using a trained learning model generated based on the results of learning by the learning device 30.

[0132] The estimation system 2 further includes an estimation unit 53 as a deep body temperature feature amount estimation unit. The estimation unit 53 estimates the feature amount related to the deep body temperature of the sleeper from the first ambient temperature, the second ambient temperature, and the feature amount related to the skin temperature.

[0133] This enables the estimation device 50 to accurately estimate the quality of sleep of the sleeper, and also reduces the burden on the sleeper that is associated with wearing a device such as a rectal thermometer or tympanic thermometer to obtain information regarding deep body temperature.

[0134] (16) Variations In the second embodiment, the estimation of the deep body temperature feature amount and the estimation of the sleep quality are performed by the same estimation device 50, but this is not limited to this. The estimation of the deep body temperature feature amount and the estimation of the sleep quality may be performed by different hardware.

[0135] Although the embodiments of the present disclosure have been described above, it will be understood that various changes in form and details can be made without departing from the spirit and scope of the present disclosure described in the claims. [Explanation of symbols]

[0136] 3. Environmental Control System 30 Learning Device 31 Acquisition Department 32 Learning Department 50 Estimation device 53 Core body temperature feature estimation unit 62 Control section 63 Actuator [Prior art documents] [Non-patent literature]

[0137] [Non-Patent Document 1] <Paper> The effect of body temperature fluctuation before sleep on falling asleep (general) Shiro Oda http: / / id.nii.ac.jp / 1136 / 00002386 /

Claims

1. An estimation device (50) that estimates the sleep quality of a sleeper using a learning model; An environmental conditioning device (60); Equipped with The learning model is generated based on a result of learning by a learning device (30) that learns the sleep quality of the sleeper, The learning device includes: an acquisition unit (31) that acquires a feature amount related to the deep body temperature of the sleeping person or a feature amount related to the skin temperature of the sleeping person as a state variable; a learning unit (32) that learns the state variables and the quality of sleep in association with each other; A generation unit that generates the learning model based on the learning result of the learning unit (32) by inputting the feature amount related to the deep body temperature or the feature amount related to the skin temperature and estimating the quality of sleep of the sleeper; having The feature amount related to the deep body temperature is determined based on at least a first deep body temperature when the sleeper falls asleep, the feature amount related to the skin temperature is determined based on at least a first skin temperature when the sleeper falls asleep, The environmental adjustment device includes an actuator (63) that adjusts the environment around the sleeper, and a control unit (61) that controls the operation of the actuator (63), The control unit (61) controls the operation of the actuator (63) based on the quality of sleep of the sleeper estimated by the estimation device (50); When it is predicted that the quality of sleep will decrease, an environmental adjustment is made to improve the quality of sleep by lowering the temperature around the sleeper or by increasing the airflow around the sleeper. Environmental control system (3).

2. The feature amount related to the deep body temperature is further determined based on a second deep body temperature acquired before the first deep body temperature, The feature amount related to the skin temperature is further determined based on a second skin temperature acquired before the first skin temperature. An environmental conditioning system (3) according to claim 1.

3. The acquisition unit (31) further acquires a feature amount related to the deep body temperature of the sleeping person at normal time or a feature amount related to the skin temperature of the sleeping person at normal time. An environmental conditioning system (3) according to claim 1 or 2.

4. The acquisition unit (31) acquires an ambient temperature when the sleeper falls asleep, a fingertip blood flow of the sleeper, or an RGB image of the sleeper as a state variable. An environmental conditioning system (3) according to any one of claims 1 to 3.

5. The quality of sleep learned by the learning unit is determined based on at least one of the following: the time from when the sleeper gets into bed until he falls asleep, the deep sleep rate of the sleeper, the number of times the sleeper awakens during the night, the time it takes for the sleeper to awaken during the night, a questionnaire on sleep quality administered to the sleeper, a questionnaire on daytime performance administered to the sleeper, the amount of hormone secretion by the sleeper, and the hormone concentration of the sleeper. An environmental conditioning system (3) according to any one of claims 1 to 4.

6. The feature amount related to the deep body temperature is determined based on at least one of the amount of change in the deep body temperature of the sleeper during a predetermined period, the maximum value, the minimum value, the mode, the average value, the difference between the maximum value and the minimum value, the maximum or minimum slope, the average slope, and an index related to the change. An environmental conditioning system (3) according to any one of claims 1 to 5.

7. The feature amount related to the skin temperature is determined based on at least one of the amount of change in the skin temperature of the sleeper in a predetermined period, the maximum value, the minimum value, the mode, the average value, the difference between the maximum value and the minimum value, the maximum or minimum slope, the average slope, and an index related to the change. An environmental conditioning system (3) according to any one of the preceding claims.

8. The acquisition unit (31) further acquires, as the state variable, any one of humidity, illuminance, chromaticity, scent, and airflow around the sleeper when the sleeper falls asleep. An environmental conditioning system (3) according to any one of claims 1 to 7.

9. The learning unit (32) learns using a plurality of teacher data, The teacher data includes the state variables and the sleep quality. An environmental conditioning system (3) according to any one of the preceding claims.

10. Further comprising a deep body temperature feature estimation unit (53), The deep body temperature feature amount estimation unit estimates a feature amount related to the deep body temperature of the sleeping person from a feature amount related to the skin temperature of the sleeping person. An environmental conditioning system (3) according to claim 1.

11. The deep body temperature feature estimation unit (53) estimating the feature amount related to the deep body temperature of the sleeping person using the feature amount related to the skin temperature of the sleeping person as an input based on a learning result of a learning model that has learned the feature amount related to the deep body temperature of the sleeping person in association with the feature amount related to the skin temperature of the sleeping person; The feature amount related to the skin temperature of the sleeping person is determined based on at least a first skin temperature when the sleeping person falls asleep, The feature amount related to the deep body temperature of the sleeping person is determined based on at least a first deep body temperature when the sleeping person falls asleep. An environmental conditioning system (3) according to claim 10.

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