Abstract: A risk estimation apparatus includes a data acquisition unit that acquires measured data of foot pressures of the left foot and the right foot obtained by sensors that are provided in shoes and measure the foot pressures, a stance phase identification unit that identifies a starting timing and an ending timing of a stance phase of each of the left foot and the right foot from the measured data of the foot pressures, and a risk estimation unit that estimates a risk of abnormality of a lower limb on the basis of asymmetry between the foot pressures of the left foot and the right foot during the stance phase.

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: An estimation apparatus acquires stance phases of both feet and estimates a risk of abnormality of a lower limb on the basis of the difference between the stance phases of the both feet.

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: An estimation apparatus acquires stance phases of both feet and estimates a risk of abnormality of a lower limb on the basis of the difference between the stance phases of the both feet.

Abstract: An estimation apparatus acquires stance phases of both feet and estimates a risk of abnormality of a lower limb on the basis of the difference between the stance phases of the both feet.

Abstract: A risk estimation apparatus includes a data acquisition unit that acquires measured data of foot pressures of the left foot and the right foot obtained by sensors that are provided in shoes and measure the foot pressures, a stance phase identification unit that identifies a starting timing and an ending timing of a stance phase of each of the left foot and the right foot from the measured data of the foot pressures, and a risk estimation unit that estimates a risk of abnormality of a lower limb on the basis of asymmetry between the foot pressures of the left foot and the right foot during the stance phase.

Abstract: A risk estimation apparatus includes a data acquisition unit that acquires measured data of foot pressures of the left foot and the right foot obtained by sensors that are provided in shoes and measure the foot pressures, a stance phase identification unit that identifies a starting timing and an ending timing of a stance phase of each of the left foot and the right foot from the measured data of the foot pressures, and a risk estimation unit that estimates a risk of abnormality of a lower limb on the basis of asymmetry between the foot pressures of the left foot and the right foot during the stance phase.

Abstract: An estimation device that includes a detection unit that detects a terminal stance period from time-series data of sensor data based on a physical quantity related to movement of a foot measured by a sensor provided at a foot portion, a feature amount extraction unit that extracts a feature amount from an angular waveform in a coronal plane during the terminal stance period, and a presumption unit that estimates a degree of pronation/supination of the foot by using the feature amount extracted from the angular waveform in the coronal plane.

Abstract: An estimation device that includes a detection unit that detects a terminal stance period from time-series data of sensor data based on a physical quantity related to movement of a foot measured by a sensor provided at a foot portion, a feature amount extraction unit that extracts a feature amount from an angular waveform in a coronal plane during the terminal stance period, and a presumption unit that estimates a degree of pronation/supination of the foot by using the feature amount extracted from the angular waveform in the coronal plane.

Abstract: An estimation device that includes a detection unit that detects a terminal stance period from time-series data of sensor data based on a physical quantity related to movement of a foot measured by a sensor provided at a foot portion, a feature amount extraction unit that extracts a feature amount from an angular waveform in a coronal plane during the terminal stance period, and a presumption unit that estimates a degree of pronation/supination of the foot by using the feature amount extracted from the angular waveform in the coronal plane.

Abstract: A feature-amount generation device that includes an extraction unit that generates a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot, extracts a feature amount from the generated gait waveform, and extracts a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, and a generation unit that generates a feature amount of the gait phase cluster using a preset feature-amount constitutive expression, and generates feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other.

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 measurement device that includes a detection unit that detects a gait event from time-series data of sensor data related to motion of a foot, and a measurement unit that performs a measurement of lower limbs by using the sensor data for a prescribed period with a timing of the gait event as a start point, based on a geometric model on which a constraint condition related to motion of the lower limbs is imposed.

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.

Abstract: A feature-amount generation device that includes an extraction unit that generates a gait waveform for one gait cycle from time-series data of sensor data related to a motion of a foot, extracts a feature amount from the generated gait waveform, and extracts a gait phase cluster by integrating a plurality of temporally continuous gait phases from each of which a feature amount is extracted, and a generation unit that generates a feature amount of the gait phase cluster using a preset feature-amount constitutive expression, and generates feature-amount data in which the plurality of gait phases constituting the gait phase cluster and the feature amount of the gait phase cluster are associated with each other.

Abstract: A walking index calculation device that includes a waveform generation unit configured to generate a walking waveform by using sensor data regarding motion of a foot acquired by a sensor installed in footwear; a detection unit configured to detect a timing at which a clearance of a toe is minimized from the walking waveform; and a calculation unit configured to calculate a minimum value of the clearance of the toe by using a walking parameter at the timing at which the clearance of the toe is minimized.

Abstract: A measurement device that includes a detection unit that detects a gait event from time-series data of sensor data related to motion of a foot, and a measurement unit that performs a measurement of lower limbs by using the sensor data for a prescribed period with a timing of the gait event as a start point, based on a geometric model on which a constraint condition related to motion of the lower limbs is imposed.

Abstract: A data processing device that includes a classification unit that classifies at least one piece of living-body information data into at least one group based on an attribute of at least one user, the at least one piece of living-body information data including a sensor value relating to a living body of the user, and a measurement time and a measurement position of the sensor value, and a learning unit that generates, for each of the group, a model for estimating interpolation data for interpolating a deficit of the living-body information data using a correlation among the sensor value included in the living-body information data, the measurement time, and the measurement position.