Abstract: Provided is a sensor network capable of acquiring, from a data center, learning data composed of a pair of sensor data (input) and a classification label (output) necessary for adding/changing a classification label for updating a classifier while reducing the volume of communication between a sensor and the center. One aspect of the present invention relates to a sensor network, including one or more sensor nodes and a data center. The sensor node includes: an encoding unit for encoding sensor data by an encoder part of an autoencoder; and a transmission unit for transmitting the encoded data. The data center includes: a reception unit for receiving data encoded from sensor data by the encoder part of the autoencoder; a decoding unit for decoding the encoded data by a decoder part of the autoencoder; and a storage unit for storing the decoded data therein.

Abstract: A sensor node connected to a server device through a communication network includes an acquisition unit configured to acquire sensor data from a sensor included in the sensor node, an identification unit configured to classify the sensor data into a plurality of classes to identify occurrence of a predetermined event based on the sensor data and an identification model created by the server device in advance, a certainty degree calculation unit configured to calculate a degree of certainty of the sensor data as an indicator representing uncertainty of a result of the identification based on the result of the identification, and a transmission unit configured to select sensor data having a minimum degree of certainty from a transmission buffer in which the sensor data and the degree of certainty of the sensor data are stored in association with each other, and transmit the selected sensor data to the server device.

Abstract: Provided is a sensor network capable of acquiring, from a data center, learning data composed of a pair of sensor data (input) and a classification label (output) necessary for adding/changing a classification label for updating a classifier while reducing the volume of communication between a sensor and the center. One aspect of the present invention relates to a sensor network, including one or more sensor nodes and a data center. The sensor node includes: an encoding unit for encoding sensor data by an encoder part of an autoencoder; and a transmission unit for transmitting the encoded data. The data center includes: a reception unit for receiving data encoded from sensor data by the encoder part of the autoencoder; a decoding unit for decoding the encoded data by a decoder part of the autoencoder; and a storage unit for storing the decoded data therein.

Abstract: A feature transformation apparatus recognizes activities of subjects in adaptation to each individual and improves recognition accuracy. The feature transformation apparatus includes a section that acquires respective prescribed activity data of one or more reference individuals and a subject when the one or more reference individuals and the subject perform a prescribed activity. A section extracts a reference individual feature of the one or more reference individuals and a subject feature of the subject. A calculation section calculates at least one standard feature based on the one or more reference individual features. A transform matrix between the standard feature and the subject feature is calculated. Recognition object activity data is acquired when the subject performs a recognition object activity. A recognition object feature of the subject is extracted from the recognition object activity data. The recognition object feature is transformed using the transform matrix.

Abstract: An operations analysis apparatus for motion includes a prescribed-motion data acquisition unit configured to acquire prescribed-motion data which is measured when an exemplar and an examinee perform a prescribed motion, respectively, and a feature extraction unit configured to extract exemplar features and examinee features, and a transformation matrix calculation unit configured to calculate a transformation matrix between the exemplar features and the examinee features, and a specified-motion data acquisition unit configured to acquire specified-motion data which is measured when the exemplar performs a series of specified motions, and a creation unit configured to, from the acquired set of specified-motion data regarding the exemplar, create a piece of exemplary specified-motion information for the examinee by using the calculated transformation matrix, and a indication unit configured to indicate the piece of exemplary specified-motion information for the examinee.

Abstract: A feature transformation apparatus recognizes activities of subjects in adaptation to each individual and improves recognition accuracy. The feature transformation apparatus includes a section that acquires respective prescribed activity data of one or more reference individuals and a subject when the one or more reference individuals and the subject perform a prescribed activity. A section extracts a reference individual feature of the one or more reference individuals and a subject feature of the subject. A calculation section calculates at least one standard feature based on the one or more reference individual features. A transform matrix between the standard feature and the subject feature is calculated. Recognition object activity data is acquired when the subject performs a recognition object activity. A recognition object feature of the subject is extracted from the recognition object activity data. The recognition object feature is transformed using the transform matrix.

Abstract: An operations analysis apparatus for motion includes a prescribed-motion data acquisition unit configured to acquire prescribed-motion data which is measured when an exemplar and an examinee perform a prescribed motion, respectively, and a feature extraction unit configured to extract exemplar features and examinee features, and a transformation matrix calculation unit configured to calculate a transformation matrix between the exemplar features and the examinee features, and a specified-motion data acquisition unit configured to acquire specified-motion data which is measured when the exemplar performs a series of specified motions, and a creation unit configured to, from the acquired set of specified-motion data regarding the exemplar, create a piece of exemplary specified-motion information for the examinee by using the calculated transformation matrix, and a indication unit configured to indicate the piece of exemplary specified-motion information for the examinee.