Abstract: In an inspection device, a classification means classifies temporal captured images which capture a target object, into a plurality of groups. A recognition means recognizes the captured images belonging to each of the groups, and outputs a determination result for each of the groups. An integration means integrates respective determination results of the groups, and outputs a final determination result.

Abstract: In a recovery level estimation device, an image acquisition means acquires images in which eyes of a patient are captured. An eye movement feature extraction means extracts an eye movement feature which is a feature of an eye movement based on the images. A recovery level estimation means estimates a recovery level of the patient based on the eye movement feature by using a recovery level estimation model which has been learned by machine learning in advance.

Abstract: In a recovery level estimation device, an image acquisition means acquires images in which eyes of a patient are captured. An eye movement feature extraction means extracts an eye movement feature which is a feature of an eye movement based on the images. A recovery level estimation means estimates a recovery level of the patient based on the eye movement feature by using a recovery level estimation model which has been learned by machine learning in advance.

Abstract: In a recovery level estimation device, an image acquisition means acquires images in which eyes of a patient are captured. An eye movement feature extraction means extracts an eye movement feature which is a feature of an eye movement based on the images. A recovery level estimation means estimates a recovery level of the patient based on the eye movement feature by using a recovery level estimation model which has been learned by machine learning in advance.

Abstract: A data acquisition means acquires source domain data and target domain data. An alignment means performs an alignment which converts the source domain data and the target domain data into images of a predetermined reference angle. A feature extraction means extracts local features of the source domain data and the target domain data. A classification means classifies a class based on the local features of the source domain data and the target domain data after the alignment. A learning means trains the feature extraction means based on the local features of the source domain data and the target domain data after the alignment and a classification result of the class.

Abstract: In order to ensure necessary inference accuracy while minimizing inference time, an information processing apparatus (1) includes: a first difficulty calculation unit (11) that calculates difficulty in inference carried out by inputting input data, constituting a time series, to a first-stage inference model among multiple-stage inference models which are configured such that use of a later-stage inference model achieves higher inference accuracy; and a first determination unit (12) that determines, on the basis of the difficulty, whether a second- or later-stage inference model will be used.

Abstract: In a recovery level estimation device, an image acquisition means acquires images in which eyes of a patient are captured. An eye movement feature extraction means extracts an eye movement feature which is a feature of an eye movement based on the images. A recovery level estimation means estimates a recovery level of the patient based on the eye movement feature by using a recovery level estimation model which has been learned by machine learning in advance.

Abstract: A determination apparatus 600 includes an agitation state determination unit 621 configured to determine an agitation state of a target based on acquired information, a condition determination unit 622 configured to determine a condition of the target based on the acquired information, and a specifying unit 623 configured to specify which to perform, the determination processing performed by the agitation state determination unit 621 or the determination processing performed by the condition determination unit 622, according to an acquisition condition of the information.

Abstract: An action recognition apparatus 1 including: a generation unit 2 that generates an addition image by adding a setting image to a target object image; an action recognition and estimation unit 3 that performs action recognition regarding the target object, outputs action recognition information indicating the result of action recognition, estimates the setting image, and outputs estimation information indicating the result of estimation; an action recognition loss calculation unit 4 that calculates an action recognition loss by using reference action recognition information generated in advance, and the action recognition information; an estimation loss calculation unit 5 that calculates an estimation loss by using reference estimation information generated in advance, and the estimation information; and a learning parameter updating unit 6 that updates learning parameters by using the action recognition loss and the estimation loss.

Abstract: A determination apparatus (500) includes an estimation unit (512) configured to estimate a cause for an error based on data used when an agitation state is determined, and a notification unit (522) configured to issue a notification according to a result of the estimation by the estimation unit (521).

Abstract: In a data augmentation device, a data acquisition means acquires two sets of source domain data of a predetermined class from a data group of a source domain, and acquires one set of target domain data of the predetermined class from a data group of a target domain data. An estimation means estimates a structure of a manifold representing a data distribution of the source domain by using two sets of source domain data. A data generation means generates new data of the target domain by using the one set of target domain data and the structure of the manifold.

Abstract: A patient condition prediction apparatus includes: an acquisition unit that obtains patient data, which are information about a patient; a selection unit that selects one predictive model from a plurality of predictive models for predicting a change in a patient condition that is a condition of the patient, on the basis of the patient data; and a prediction unit that predicts a change in the patient condition in the future by using the one predictive model. This makes it possible to predict a change in the patient condition by using an appropriate predictive model.

Abstract: An inspection system includes: a suspended matter detecting and tracking means that detects and tracks suspended matter present in the liquid in the container in chronological images obtained by consecutively imaging the liquid with a camera; a determining and storage controlling means that determines, based on a movement locus of the tracked suspended matter, whether the suspended matter is foreign matter or an air bubble, and stores inspection result information including a result of the determination and information of the movement locus of the suspended matter used as a basis for the determination into a storing means in association with identification information of the container; and a display controlling means that causes a displaying means to display the result of the determination and the information of the movement locus of the suspended matter used as the basis for the determination included by the inspection result information.

Abstract: A determination apparatus includes: a dividing unit configured to divide chronological image data acquired by imaging a liquid filled in a container while switching between a plurality of illumination conditions, into chronological image data corresponding to the illumination conditions; and a determining unit configured to determine foreign matter contained in the container based on each of the chronological image data obtained by division by the dividing unit.

Abstract: A grasping device includes: a grasping unit configured to grasp a container filled with liquid; a tilting unit configured to at least tilt the container in a state where the grasping unit grasps the container; and a maker installed at a part tilting in synchronism with the container when the container tilts and configured to move in conjunction with movement of the container.

Abstract: A rehabilitation planning apparatus includes: a similarity calculation unit that calculates a degree of similarity between each of a plurality of pieces of past information and target patient information, each of the plurality of pieces of past information being information about a respective one of a plurality of past patients who performed rehabilitation in a past, and the target patient information being information about a target patient; a ranking unit that extracts at least one piece of past information from the plurality of pieces of past information based on the degree of similarity, and ranks the at least one piece of past information according to a predetermined ranking rule; and a determination unit that determines, as a rehabilitation plan for the target patient, a rehabilitation history of the past patient associated with the past information according to a result of the ranking of the pieces of past information.

Abstract: A rehabilitation planning apparatus, a rehabilitation planning system, a rehabilitation planning method, and a program capable of efficiently creating a rehabilitation plan are provided. A rehabilitation planning apparatus (1) includes a rehabilitation pattern selection unit (2), an ability value prediction unit (3) that predicts a physical ability value after a target patient performs rehabilitation indicated in a selected rehabilitation pattern, a repetition control unit (4) that performs control so that the selection by the rehabilitation pattern selection unit (2) and the prediction by the ability value prediction unit (3) are repeated, and a determination unit (5) that determines a rehabilitation pattern for, among combinations of rehabilitation patterns and physical ability values obtained through the repetition of the selection and the prediction, a combination of which the physical ability value satisfies a predetermined condition as a rehabilitation plan for the target patient.

Abstract: An environment management system, an environment management method, and a program that are capable of reducing the load imposed on a responder are provided. An environment management system includes: a determination unit configured to determine, based on a feature value of biological information of a target patient to be input, identification information indicating a condition of the target patient; an estimation unit configured to estimate environment control information for the target patient based on the identification information and an environment control prediction parameter that has been learned in advance; and an environment control unit configured to automatically change a surrounding environment of the target patient based on the environment control information for the target patient estimated by the estimation unit.

Abstract: An information processing apparatus according to the present invention includes: a calculating unit configured to calculate, based on subject information including a first assessment value representing an assessment of a subject at a predetermined moment for each of a plurality of items set in FIM (Functional Independence Measure), a prediction value representing an assessment of the subject predicted after the predetermined moment; and a control unit configured to set the prediction value as a provisional assessment value representing a provisional assessment of the subject for a predetermined item of the FIM, and control to output so as to display in a correctable manner on an information processing device operated by an assessor.

Abstract: The condition predicting apparatus is provided with an acquisition unit configured to acquire target patient information indicating a condition of a target patient at a first time point of a hospitalization period, an extraction unit configured to extract, from a plural pieces of past patient information that respectively correspond to a plurality of other patients who are different from the target patient and that include first condition information indicating a condition at a time point corresponding to the first time point and second condition information indicating a condition at a time point corresponding to a second time point after the first time point of the hospitalization period of the target patient, one or more pieces of first past patient information that include the first condition information indicating a condition similar to the condition at the first time point indicated by the target patient information, an output unit configured to output at least a part of the one or more pieces of first p