Abstract: A model generation apparatus includes: an acquisition unit for obtaining learning data including a first index value indicating a condition of a sample patient at a first time, and a second index value indicating a condition of the sample patient at a second time that is after the first time; and a learning unit for learning a prediction mode for predicting a condition of a target patient at the second time on the basis of a condition of the target patient at the first time, wherein the learning unit is configured to learn the prediction model by updating the prediction model on the basis of a third index value indicating a condition of the sample patient at the second time that is predicted by the prediction model on the basis of the first index value, and a change information indicating a change tendency of the condition over time.

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

Abstract: An information processing apparatus capable of effectively supporting the creation of a plan for activities aimed at improving abilities is provided. A storage unit (2) stores therein past patient data in which a plurality of past patients are associated with activity targets. A long-term target determination unit (12) determines, for each of the plurality of past patients, whether or not the activity target for that past patient is similar to a long-term target for an ability improving activity performed by a target patient. A short-term target extraction unit (14) extracts a short-term target set for a past patient corresponding to the activity target determined to be similar to the long-term target for the target patient. A short-term target presentation unit (16) performs a process for presenting information about the extracted short-term target to the user.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: In information processing device, the information acquisition means acquires patient information, medical professional information, and environment information. The treatment order determination means determines an order of treatment of the patients for each medical professional based on the patient information, the medical professional information, and the environment information.

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: In an insulation inspection method for detecting creeping discharge that occurs in a coil in an armature, a predetermined impulse voltage is applied to the coil, and an electrical current value of a current that flows to a current detector that is connected to the coil is detected. The detected electrical current value is analyzed to acquire a relationship between a frequency and an electrical current value spectrum in the detected electrical current value. Based on the acquired relationship, a total sum of electrical current value spectrum areas in a high-frequency band is calculated as a high-frequency spectrum area. The high-frequency band differs from a predetermined low-frequency band in which partial discharge that occurs in a portion of a thin coating of the wire configuring the coil can be detected. Based on a magnitude of the calculated high-frequency spectrum area, creeping discharge that occurs in the coil is detected.

Abstract: A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.

Abstract: An information processing apparatus 100 includes an input unit 121 and a generation unit 122. The input unit 121 receives input of a first assessment value representing assessment of a subject at a predetermined point of time and input of a second assessment value representing assessment of the subject after a predetermined time elapsed from the predetermined point of time. The first and second assessment values are values for each of an item of the Stroke Impairment Assessment Set (SIAS) and an item of a second index, different from the SIAS, for assessing the condition of a human body. The generation unit 122 generates a model for calculating the second assessment value with respect to the first assessment value for each item of the SIAS and the second index, on the basis of information representing a relationship between the item of the SIAS and the item of the second index.

Abstract: A static charge eliminator for neutralizing static electricity on an armature, such as a rotor or a stator of a rotating electrical machine. The static charge eliminator includes a first contact, a second contact, a third contact, and a drive unit. The drive unit works to move at least one of the first, second, and third contact and the armature to simultaneously achieve electrical contacts between the first contact and a core of the armature, between the second contact and a metallic section(s) of wire segments of a coil of the armature or power wires electrically connecting with the coil, and between the third contact and outer layers of portions of the wire segments contacting a coil end protruding outside an axial end of the core of the armature. A static charge elimination method is also provided.

Abstract: An insulation testing apparatus includes: a power unit having a first and second terminals; a switching unit switching between an electrical connected and disconnected states between the wire rods of respective phases and the core, and the first and second terminals; a discharge measurement unit measuring an amount of discharge produced between a first and second members electrically connected to the first and the second terminal, respectively; an insulation determination unit determines, based on the amount of discharge, whether the coating of the wire rods satisfies a predetermined quality regulation; and a control unit controlling the switching unit such that all of the wire rods and the core are electrically connected to the first or second terminals, while controlling the switching unit such that none of the wire rods nor the core is insulated with both of the first and second terminals.

Abstract: An information processing apparatus inputs expiratory phase data being data of expiratory phase and inspiratory phase data being data of inspiratory phase, for each of abdominal waveform data indicating a breathing waveform of an abdomen of a subject performing breathing training and chest waveform data indicating a breathing waveform of a chest of the subject. The information processing apparatus calculates a phase difference between the abdominal waveform data and the chest waveform data in each of the expiratory phase and the inspiratory phase.

Abstract: A rehabilitation assistance apparatus 1 includes: an acquisition unit 211 configured to acquire at least one of an image of a subject who is doing exercise for rehabilitation, audio of the subject, and biological information of the subject; a calculation unit 212 configured to calculate a fatigue level of the subject based on at least one of the image, the audio, and the biological information; a generation unit 213 configured to generate feedback information related to the rehabilitation, based on the fatigue level; and an output unit 23 configured to output the feedback 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: 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: 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: A rehabilitation work support apparatus, a rehabilitation work support method, and a program capable of reducing a work burden on a therapist are provided. A rehabilitation work support apparatus (1) includes a difference calculation unit (2) and a difference output unit (3). The difference calculation unit (2) calculates, for each type of ability, a difference between a target value for a patient's ability value in rehabilitation and the patient's ability value at a time before a start of the rehabilitation or during the rehabilitation. The difference output unit (3) performs control so as to output information representing the calculated difference for each type of ability.