Abstract: A detection device that includes an acquisition unit that acquires data including a dorsiflexion peak time, a plantarflexion peak time, and travel direction acceleration acquired from sensor data regarding a movement of a foot, a candidate detection unit that detects, as a candidate time for heel strike, a time of a feature signal point extracted from time-series data of the travel direction acceleration in an investigation time period starting from an acceleration peak time detected from the travel direction acceleration based on the dorsiflexion peak time, and an output unit that outputs the detected candidate time as a heel strike time.

Abstract: A machine learning device that trains a first encoding model for encoding first sensor data into first code, a second encoding model for encoding second sensor data into second code, and an estimation model for making estimation using the first code and the second code such that an estimation result from the estimation model conforms to correct answer data, trains a first adversarial estimation model that outputs an estimated value of the second code in response to the input of the first code such that the estimated value of the second code estimated by the first adversarial estimation model conforms to the second code outputted from the second encoding model, and trains the first encoding model such that the estimated value of the second code estimated by the first adversarial estimation model does not conform to the second code outputted from the second encoding model.

Abstract: Provided is a mobility estimation device that includes a data acquisition unit that acquires feature amount data including a feature amount used for estimating a mobility of a user, the feature amount data being extracted from sensor data regarding a movement of a foot of the user, a storage unit that stores an estimation model that outputs a mobility index based on an input of the feature amount data, an estimation unit that inputs the acquired feature amount data to the estimation model and estimates the mobility of the user in accordance with the mobility index output from the estimation model, and an output unit that outputs information regarding the estimated mobility of the user.

Abstract: Provided is a static balance estimation device that includes a data acquisition unit that acquires feature amount data that include a feature amount to be used for estimating static balance of a user, the feature amount being extracted from a feature of a gait of the user, a storage unit that stores an estimation model that outputs a static balance index related to input of the feature amount data, an estimation unit that inputs the acquired feature amount data into the estimation model and estimates the static balance of the user according to the static balance index output by the estimation model, and an output unit that outputs information related to the estimated static balance of the user.

Abstract: Provided is a muscular strength index estimation device that includes a data acquisition unit that acquires feature amount data including a feature amount, the feature amount being extracted from a feature of gait of a user and being used for estimation of a muscular strength index of the user, a storage unit that stores an estimation model that outputs the muscular strength index in accordance with an input of the feature amount data, an estimation unit that inputs the acquired feature amount data to the estimation model to estimate the muscular strength index of the user, and an output unit that outputs information regarding the estimated muscular strength index.

Abstract: A lower limb muscle power estimation device that includes a data acquisition unit that acquires feature amount data including a feature amount extracted from sensor data related to foot motion of a user, the feature amount being used to estimate lower limb muscle power of the user, a storage unit that stores an estimation model for outputting a lower limb muscle power index corresponding to input of the feature amount data, an estimation unit that inputs the acquired feature amount data to the estimation model and estimates the lower limb muscle power of the user according to the lower limb muscle power index output from the estimation model, and an output unit that outputs information on the estimated lower limb muscle power of the user.

Abstract: Provided is a dynamic balance estimation device that includes a data acquisition unit that acquires feature amount data including a feature amount to be used for estimating dynamic balance of a user, the feature amount data being extracted from sensor data regarding motion of a foot of the user, a storage unit that stores an estimation model that outputs a dynamic balance index according to an input of the feature amount data, an estimation unit that inputs the acquired feature amount data to the estimation model to estimate the dynamic balance of the user in accordance with the dynamic balance index output from the estimation model, and an output unit that outputs information on the estimated dynamic balance of the user.

Abstract: Provided is a muscle strength evaluation device that includes a data acquisition unit that acquires feature quantity data including a feature quantity used for estimation of a muscle strength of an evaluation target muscle related to a fall risk, the feature quantity data being extracted from sensor data related to movement of a user's foot, a storage unit that stores an estimation model that outputs a muscle strength index of the evaluation target muscle according to an input of the feature quantity data, an evaluation unit that inputs the acquired feature quantity data to the estimation model and evaluate a muscle strength of the evaluation target muscle of the user according to the muscle strength index output from the estimation model, and an output unit that outputs information regarding an evaluation result related to the muscle strength of the evaluation target muscle of the user.

Abstract: Provided is a fall probability estimation device that includes a data acquisition unit that acquires feature amount data including a feature amount extracted from sensor data regarding a motion of a foot of a user and used for estimation of fall probability of the user, a storage unit that stores an estimation model that outputs a fall probability index according to an input of the feature amount data, an estimation unit that inputs the acquired feature amount data into the estimation model and estimates the fall probability of the user according to the fall probability index output from the estimation model, and an output unit that outputs information regarding the estimated fall probability of the user.

Abstract: Provided is a data generation device that acquires pair data constituted by a combination of measurement gait data relating to sensor data measured in accordance with the movement of a user's feet and a response variable corresponding to the measurement gait data, generates a measurement dataset vector by combining the response variable and a feature amount vector calculated using a feature amount extracted from measurement gait data, generates a covariance matrix relating to a plurality of pair data, generates pseudo gait data using the measurement gait data, generate a pseudo dataset vector by combining a pseudo feature amount vector calculated using a pseudo feature amount extracted from the pseudo gait data, and a pseudo response variable generated using a covariance matrix relating to the pseudo feature amount vector, and outputs the dataset.

Abstract: A knee pain risk estimation device includes a feature amount construction unit that constructs a feature amount related to a sign variable to be used for estimation of a knee pain risk indicating a risk of having a medical examination in a future due to knee pain, a sign variable estimation unit that inputs the feature amount to a sign variable estimation model to estimate at least one of the sign variables, and performs a principal component analysis on the estimated at least one of the sign variables to generate a principal component vector, a knee pain risk estimation unit that estimates a knee pain risk index, and generates knee pain risk information regarding the knee pain risk by using the estimated knee pain risk index, and an output unit that outputs the generated knee pain risk information.

Abstract: Provided is a learning device including a data acquisition unit that acquires a data set including a plurality of training data, a generation unit that includes a generation model that outputs pseudo data, a discrimination unit that includes a discrimination model that discriminates whether the input data is either the training data or the pseudo data according to an input of either the training data or the pseudo data, a management unit that sets a first hyperparameter to be used for updating the discrimination model based on a preset hyperparameter, and a second hyperparameter to be used for updating the generation model, and a learning processing unit that updates the discrimination model using the first hyperparameter and updates the generation model using the second hyperparameter.

Abstract: Provided is a motion data generation device including an acquisition unit that acquires a plurality of pieces of motion data to be data converted, a conversion data choosing unit that groups the plurality of pieces of motion data for each motion class that is a target motion for data augmentation, a data conversion unit that sets at least one piece of the motion data grouped for the each motion class for reference data, sets at least one piece of the motion data different from the reference data among the grouped motion data for data to be converted, and generates extension motion data in which the data to be converted is synchronized with reference to motion timing of the reference data, and an output unit that outputs the generated extension motion data.

Abstract: Provided is a waist swinging estimation device including a communication unit that acquires feature amount data including a feature amount extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data regarding a movement of a foot of a subject and used for estimation of waist swinging that is an index regarding a movement of a waist, a storage unit that stores an estimation model that outputs an estimated value regarding the waist swinging according to an input of a feature amount included in the feature amount data, an estimation unit that inputs a feature amount included in the acquired feature amount data to the estimation model, and estimate waist swinging of the subject according to an estimated value regarding the waist swinging output from the estimation model, and an output unit that outputs information according to waist swinging of the subject.

Abstract: An index value estimation device including a data acquisition unit that acquires feature amount data including a feature amount extracted from sensor data related to a motion of a foot of a user and used for estimating an index value indicating a knee state of the user, a storage unit that stores an estimation model that outputs an index value according to an input of the feature amount data, an estimation unit that estimates an output obtained by inputting the acquired feature amount data to the estimation model as an index value indicating the knee state of the user, and an output unit that outputs information about the estimated index value indicating the knee state of the user.

Abstract: Provided is a pelvic inclination estimation device including a communication unit that acquires feature amount data including a feature amount to be used for estimation of a pelvic inclination, the feature amount being extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data related to movement of a foot of a subject, a storage unit that stores an estimation model that outputs an estimation value related to the pelvic inclination according to an input of the feature amount included in the feature amount data, an estimation unit that inputs a feature amount included in the acquired feature amount data to the estimation model and estimate a pelvic inclination of the subject according to the estimation value output from the estimation model, and an output unit that outputs information associated to the pelvic inclination of the subject.

Abstract: A training device including a motion data acquisition unit that acquires first motion data related to a target motion, a first generation unit that generates pseudo first motion data by using a first generation model, a determination unit that calculates a determination loss indicating a degree of deviation between the first motion data and the pseudo first motion data using a determination model, a relevance calculation unit that reconfigures the target motion by a combination of basis motions and calculate a degree of relevance between the target motion and the basis motions, a regularization loss calculation unit that calculates a regularization loss indicating a degree of deviation between motion data related to the basis motions and the pseudo first motion data, and an adversarial training processing unit that adversarially trains the first generation model and the determination model using the determination loss and the regularization loss.

Abstract: Provided is a waist swinging estimation device including a communication unit that acquires feature amount data including a feature amount extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data regarding a movement of a foot of a subject and used for estimation of waist swinging that is an index regarding a movement of a waist, a storage unit that stores an estimation model that outputs an estimated value regarding the waist swinging according to an input of a feature amount included in the feature amount data, an estimation unit that inputs a feature amount included in the acquired feature amount data to the estimation model, and estimate waist swinging of the subject according to an estimated value regarding the waist swinging output from the estimation model, and an output unit that outputs information according to waist swinging of the subject.

Abstract: Provided is a pelvic inclination estimation device including a communication unit that acquires feature amount data including a feature amount to be used for estimation of a pelvic inclination, the feature amount being extracted from a gait waveform of a spatial acceleration and a spatial angular velocity included in sensor data related to movement of a foot of a subject, a storage unit that stores an estimation model that outputs an estimation value related to the pelvic inclination according to an input of the feature amount included in the feature amount data, an estimation unit that inputs a feature amount included in the acquired feature amount data to the estimation model and estimate a pelvic inclination of the subject according to the estimation value output from the estimation model, and an output unit that outputs information associated to the pelvic inclination of the subject.

Abstract: A data conversion device including a feature amount calculation unit that normalizes posture data estimated in each frame constituting moving image data including a synchronization target motion into an angular representation, and calculates a feature amount in an embedded space by inputting the posture data normalized into the angular representation to an encoder, a distance calculation unit that calculates a distance between a feature amount calculated in each frame constituting reference moving image data and a feature amount calculated in each frame constituting synchronization target moving image data, a synchronization processing unit that calculates an optimal path for each frame based on the calculated distance and synchronizes the synchronization target moving image data with the reference moving image data by aligning timings of frames connected by the optimal path, and an output unit that outputs the synchronization target moving image data synchronized with the reference moving image data.