Abstract: Provided is a gait measurement device that includes an acquisition unit that acquires sensor data related to a motion of a foot, an interpolation unit that interpolates interpolation data into a period in which the sensor data is lost, a calculation unit that calculates a gait parameter by using the sensor data obtained by interpolating the interpolation data by the interpolation unit, and a transmission unit that transmits the gait parameter calculated by the calculation unit.

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 an evaluation device that includes an acquisition unit that acquires position data pertaining to a plurality of measurement target sites established in an evaluation target segment of a body, a calculation unit that fits a circle to the plurality of measurement target sites and calculates a value of a diameter of the fitted circle, an evaluation unit that evaluates flexibility of the evaluation target segment according to the calculated value of the diameter of the circle, and an output unit that outputs an evaluation result relating to the flexibility of the evaluation target segment.

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: A mobile terminal acquires a schedule of a user using a module device that includes a sensor, and instructs the module device to initiate a process for determining when to start or when to stop transmitting sensing information detected by the sensor to a communication destination, on the basis of the schedule. The module device acquires the sensing information, and initiates the process for determining when to start or when to stop transmitting the sensing information to the communication destination, on the basis of the instruction to initiate the determination process.

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 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 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: 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 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: Provided is a gait measurement device that includes an acquisition unit that acquires sensor data measured by a sensor mounted on footwear, a mounting-direction-determination unit that determines a mounting direction of the sensor using the acquired sensor data, a coordinate conversion unit that converts a coordinate system of the sensor data in accordance with the determined mounting direction of the sensor, a detection unit that detects a walking event from time-series data of the sensor data in which the coordinate system has been converted, a calculation unit that calculates a gait parameter in accordance with the detected walking event, and a transmission unit that transmits the calculated gait parameter.

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: A gait information generation device includes a communication unit that acquires sensor data measured in response to a motion of a foot, an increment calculating unit that calculates an increment of a lateral distance by using lateral acceleration included in the sensor data, a gait information generating unit that calculates, as a step width, a sum of a heel center distance substantially equivalent to a distance between center lines of heels of both feet in an initial state and a sum total of the increments of the lateral distance, and generates gait information related to the calculated step width, and an output unit that outputs the generated gait information.

Abstract: Provided is a gait index calculation device that includes a communication unit that receives sensor data based on spatial acceleration and spatial angular velocity that are measured at a frequency lower than a normal measurement frequency, an interpolation unit that interpolates the sensor data using a predetermined interpolation method, a gait index calculation unit that calculates a gait index using the interpolated sensor data, and an output unit that outputs the calculated gait index.

Abstract: A condition setting device includes an acquisition unit that acquires measurement success/failure information indicating success/failure of measurement of a physical quantity related to a motion of a foot, the measurement being executed by a measurement device installed at a foot portion of a user at a measurement opportunity included in a target measurement time zone, and a measurement condition setting unit that sets, based on the measurement success/failure information in the target measurement time zone, a measurement condition including a standby time from when a gait is sensed at a measurement opportunity included in a related measurement time zone related to the target measurement time zone to when measurement is started.

Abstract: A training device includes an acquisition unit that acquires a data set of raw data and correct answer data, a feature amount calculation unit that calculates a feature amount using the raw data, a model construction unit that constructs an encoding model that outputs a code related to the feature amount in response to an input of the raw data and an estimation model that outputs an estimation result related to the raw data in response to an input of the code, and a training processing unit that trains the encoding model and the estimation model in such a way that the estimation result matches the correct answer data based on a relationship between the code and the feature amount.

Abstract: To estimate the body condition according to the attributes on the basis of sensor data measured while a user is walking, this estimation device comprises: a feature amount extraction unit that extracts, from a walking waveform extracted from time-series data of sensor data based on the movement of user's legs, a feature amount according to user's attributes in a section in which features of the body condition of the user according to the user's attributes are exhibited; and an estimation unit that estimates the user's body condition using the feature amount extracted according to the user's attributes.

Abstract: A gait measurement device includes a first sensor that outputs an activation signal in response to detection of a magnetic field, a clock that generates a clock count value, a second sensor that measures a physical quantity related to movement of a foot, a control unit that is activated in response to the activation signal output from the first sensor to activate the clock and the second sensor, a data generation unit that generates sensor data using the physical quantity measured by the second sensor and generates measurement data associated with the clock count value and the sensor data at a timing when the physical quantity is measured, and an output unit that outputs the measurement data.