Abstract: A position sense correction device according to an aspect of the present invention includes a movement information obtainment unit, a storage unit, a difference calculation unit, a repetition number calculation unit, and a goal value calculation unit. The movement information obtainment unit obtains a measurement value of a physical position of a body part which a subject is able to autonomously move. The storage unit stores therein a final goal value that is set in advance. The difference calculation unit calculates a difference between the final goal value and the measurement value. The repetition number calculation unit calculates, on a basis of the difference, a number of times the subject needs to make a voluntary movement until the measurement value reaches the final goal value. The goal calculation unit calculates a goal value with respect to each voluntary movement on a basis of the number of times.

Abstract: A psychological oppression feeling that is given to a person by a robot is accurately calculated. There is provided a psychological oppression feeling calculating device including: a first acquisition unit (S02) that acquires a relative positional relationship between a person and a plurality of robots including the number of the plurality of robots with respect to the person, a moving speed of the robot, and a distance between the person and the robot; a second acquisition unit (S01) that acquires a density of the robots with respect to the person; and a calculation unit (S03 and S04) that calculates a psychological oppression feeling given to the person by the robots from results acquired by the first acquisition unit and the second acquisition unit.

Abstract: An embodiment of the present invention is provided with a projective transform model including a plurality of nodes and a projection table, the plurality of nodes each holding a reference vector having a dimension corresponding to the dimension of multi-dimensional data. The projection table indicates the correspondence relation between the number of each node and a coordinate in a two-dimensional space as a projection target of the reference vector held by the node. First in a learning phase, multi-dimensional input data of a positive example and a negative example is acquired, the amplitude characteristic amounts thereof are calculated, and this amplitude characteristic amount data is learned as the reference vectors of the nodes for each sample.

Abstract: A reliable technology for determining the masticatory side of the user is provided. First and second electromyographic waveforms respectively originating from left and right muscles related to masticatory actions of a user are acquired; a coefficient of correlation between pieces of information respectively extracted from the first and the second electromyographic waveforms is calculated as a first feature value; a second feature value is calculated from a power spectrum obtained by performing frequency analysis on the first electromyographic waveform; a third feature value is calculated from a power spectrum obtained by performing frequency analysis on the second electromyographic waveform; a learning model is generated by associating the first, second, and third feature values with a plurality of labels; and the masticatory side of the user is determined based on first, second, and third feature values calculated from a newly acquired electromyographic waveform and the learning model.

Abstract: A technology for simulating physiological activity more appropriately is provided. An optimization apparatus according to an embodiment includes an estimation unit, a first calculation unit, a second calculation unit, and an updating unit. The estimation unit estimates a physiological parameter indicating a physiological state using an estimation model. The first calculation unit calculates a first difference value based on a first estimated value of the physiological parameter in a first time interval and an actually measured value of the physiological parameter in the first time interval. The second calculation unit calculates a second difference value based on the first estimated value and a second estimated value of the physiological parameter in a second time interval previous to the first time interval. The updating unit adjusts the estimation model based on the first difference value and the second difference value.

Abstract: A living body guidance device includes: a measurement unit 1 that measures biometric information of a user; a stimulus unit 2 that is installed in contact with the user's body and presents a tactile stimulus to the user; a database unit 3 that has a relationship between the tactile stimulus presented by the stimulus unit and the biometric information according to the tactile stimulus recorded; an input unit 4 that sets target biometric information which is target biometric information that is desired of the user to realize; and a control unit 7 that, when there is a difference between current biometric information which is current biometric information of the user estimated based on the measurement result by the measurement unit and the target biometric information, controls the tactile stimulus presented by the stimulus unit so that the difference is reduced, so as to guide the user so that the current biometric information approaches the target biometric information, based on the relationship recorded in th

Abstract: An embodiment of the present invention is provided with a projective transform model including a plurality of nodes and a projection table, the plurality of nodes each holding a reference vector having a dimension corresponding to the dimension of multi-dimensional data. The projection table indicates the correspondence relation between the number of each node and a coordinate in a two-dimensional space as a projection target of the reference vector held by the node. First in a learning phase, multi-dimensional input data of a positive example and a negative example is acquired, the amplitude characteristic amounts thereof are calculated, and this amplitude characteristic amount data is learned as the reference vectors of the nodes for each sample.

Abstract: A theme phrase feature value calculation unit 103 calculates a feature value Fq of a given theme phrase Q based on the cosine similarity with a feature keyword of a feature keyword DB 107b. A positive/negative example determination unit 104 applies the feature value Fq to a positive/negative example determination model to determine whether a response phrase A is a positive example or a negative example. A response phrase score calculation unit 105 calculates the cosine similarity between the theme phrase Q and a plurality of theme phrases that form a pair with a response phrase A determined to be a positive example, and the highest value of the calculated cosine similarity is set as a score of the response phrase A. A response phrase selection unit 106 selects the response phrase indicating the maximum score as the response phrase for the theme phrase Q.

Abstract: A reliable technology for determining the masticatory side of the user based on the surface myoelectric potential of masticatory muscles is provided.