Abstract: In order to feed back the risk of degradation of a quality of sleep to a subject, in a risk prediction device, a processor acquires a life log from the subject terminal, the life log including quality-of-sleep information and lifestyle information. The processor creates a life map representing a tendency of a lifestyle in a coordinate space having lifestyle-related parameters, and estimates a latest state by calculating the lifestyle-related parameters and mapping the latest state in the life map. The processor predicts a risk of degradation of quality, based on a positional relationship between a position of the latest state and a region in the life map, and provides feedback if the risk is equal to or more than a threshold. the subject terminal displays an alert and a recommended action to reduce the risk. Accordingly, it is possible to supports users in making decisions concerning their sleep.

Abstract: A long-term state transition is estimated in consideration of variations among individuals. A state transition estimation device acquires a first feature vector of a first individual in a first age range, a second feature vector of a second individual in a second age range, and individual data of the first individual, generates a latent vector representing variation between the first individuals from the individual data, performs mapping to an estimated feature vector in a case where the first individual falls within a second age range by using the latent vector, updates a mapping parameter so that the mapping approaches optimal transport from a probability distribution of the first feature vector to a probability distribution of the second feature vector, and estimates a probability distribution of the estimated feature vector in a case where the individual in the first age range falls within the second age range.

Abstract: A path estimation device includes an acquisition unit, a generation unit, a path estimation unit, and an output unit. The acquisition unit acquires health-related data of a plurality of persons. The generation unit generates an occurrence probability distribution of the health-related data, based on the health-related data at a plurality of time points of each of the plurality of persons. The path estimation unit estimates a path between pieces of health-related data of a target person at different time points, on the generated occurrence probability distribution. The output unit outputs information regarding the estimated path. By including such a configuration, the path estimation device is capable of assisting decision making based on an estimation result of transition of the health-related data.

Abstract: An aspect of the present disclosure includes: an acquisition unit acquiring health data being data regarding health of a plurality of persons in a first period and a second period a predetermined duration before the first period; a calculation unit calculating distribution feature information indicating a feature related to a distribution of health data of a first age group in the first period; a generation unit generating a prediction-target distribution by integrating a distribution of health data of a prediction-target age group corresponding to the age of the target person when the predetermined period of time or more has elapsed and the distribution feature information; and a prediction unit predicting the health data in the prediction-target distribution in the distribution of the health data of the first age group in the first period. The present disclosure supports decision making regarding the health of the target person.

Abstract: An estimation device includes a reception unit that receives an input of a data set for each stage, an estimation unit that estimates a state transition probability based on a transition from a first distribution that is a data distribution in a first stage to a second distribution that is a data distribution in a second stage based on a cost determined for each piece of data in the first distribution and required for a transition to a transition destination, and an output unit that outputs an estimated state transition probability. The estimation device can support decision making regarding future states by predicting future states through the estimation of state transitions.

Abstract: A data estimation device includes an acquisition unit, a stratification unit, an estimation unit, and an output unit. The acquisition unit acquires data sets including pieces of data indicating mutually different probability distributions and an attribute used for stratification of the data sets. The stratification unit stratifies the data sets based on the attribute. The estimation unit estimates a state transition probability between the data sets after the stratification based on a difference in distribution between a state transition probability between the data sets before the stratification and a state transition probability between the data sets stratified for each of the attributes. The output unit outputs the state transition probability between the data sets after the stratification. The use of the state transition probability estimated in this manner enables the data estimation device to support decision making based on an estimation result of a transition destination of data.

Abstract: An estimation device includes a reception unit that receives an input of a data set for each stage, a first estimation unit that estimates a first simultaneous distribution based on a transition from a data distribution in a first stage to a data distribution in a second stage that is a stage after the first stage, a second estimation unit that estimates a second simultaneous distribution based on a transition from the estimated first simultaneous distribution to a data distribution in a third stage that is a stage after the second stage, and a calculation unit that calculates a state transition probability related to data transition from the second stage to the third stage based on the second simultaneous distribution. The estimation device can support decision making regarding future states by predicting future states through the estimation of state transitions.

Abstract: The data estimation device includes an acquisition unit, an estimation unit, and an output unit. The acquisition unit acquires health-related data of each person of different age groups. The estimation unit estimates the transition destination of the health-related data of the target person in a case where the age of the target person in the first age group becomes the second age group. The output unit outputs health-related data in a case where the age of the target person has increased based on the estimation result of the transition destination. With such a configuration, the data estimation device can support decision making related to a future health condition.

Abstract: The training device 1X mainly includes a type determination means 16X and a training means 17X. The type determination means 16X determines a type of each of subjects related to an action based on a recommended action, a type of the action, or the combination thereof, and an actual action, a type of the action, or the combination thereof. The training means 17X trains a state prediction model, for each type related to the action, based on a lifelog of each of the subjects classified by the type, wherein the state prediction model is obtained by performing machine learning of a relationship between a lifelog and a predicted index value of a state to be predicted of a person for which the lifelog has been measured.

Abstract: A health management system includes an image sensor installed in a house of a user, a wearable device used by the user, and a health management device capable of communicating with the image sensor and the wearable device, the health management device including at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: acquire an image of an inside of the house, analyze the image to identify a room layout of the house, acquire an exercise history of the user, estimate a health condition of the user after a change in a living environment, determine an action according to the health condition after the change in the living environment, and control display of the wearable device in such a way as to display the action. It is possible to support the decision making regarding the action of the user.

Abstract: Action recommendation device includes at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: acquire information about a life of a disaster victim living an evacuation life in a vehicle, the information being obtained based on a detection result by a sensor installed in the vehicle, determine an action for maintaining health of the disaster victim, the action being recommended to the disaster victim, based on information about the life of the disaster victim, and output information about the action for maintaining health. The action recommendation device makes it possible to support decision making on the action for maintaining health by the disaster victim.

Abstract: A pressure sensitive element (110) is located on an upper surface side of a substrate (100), and includes a pressure sensitive layer (114). When the pressure sensitive layer (114) is deformed, an electrical characteristic of the pressure sensitive layer (114) changes. For example, resistance of the pressure sensitive layer (114) changes by deformation. A deformation layer (120) faces the substrate (100) with a plurality of the pressure sensitive layers (114) in between, is deformable in a thickness direction, and includes a plurality of protrusions and recesses. An average value Pi of center-to-center distances of a plurality of protrusions of the deformation layer (120) is equal to or more than one time of a center-to-center distance Po of the pressure sensitive layers (114).