Abstract: An interpolation device includes a gait information processing unit that generates a gait waveform for each gait cycle by using time-series data of sensor data regarding a movement of a foot and specifies a missing section of data in the time-series data, a missing information processing unit that calculates a gait phase of the specified missing section, and an interpolation unit that generates interpolation data for interpolating the missing section by using data of the gait phase of the missing section in a gait waveform with a gait cycle different from a gait cycle including the missing section, and interpolates the generated interpolation data to the missing section.

Abstract: The present disclosure provides a gantry for an X-ray system. The gantry may include a base section, a lifting section, and a swing section. The base section may be configured to move. A first end of the lifting section may be connected to the base section. A first end of the swing section may be rotatably connected to a second end of the lifting section. A radiation assembly may be disposed on a second end of the swing section.

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: 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 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: The present disclosure provides a gantry for an X-ray system. The gantry may include a base section, a lifting section, and a swing section. The base section may be configured to move. A first end of the lifting section may be connected to the base section. A first end of the swing section may be rotatably connected to a second end of the lifting section. A radiation assembly may be disposed on a second end of the swing section.

Abstract: A storage device syndrome-weight-based error correction system includes a syndrome-weight-based error correction subsystem that is coupled to a storage subsystem in a chassis. The syndrome-weight-based error correction subsystem performs a first error correction hard decoding operation on the storage subsystem that utilizes first read voltage thresholds and that generates a first final codeword candidate having a first syndrome weight. The syndrome-weight-based error correction subsystem then performs error correction hard decoding read voltage threshold real-time search operations to determine second read voltage thresholds, and performs a second error correction hard decoding operation on the storage subsystem that utilizes the second read voltage thresholds and that generates a second final codeword candidate having a second syndrome weight.

Abstract: A foot angle calculation device including: a posture angle calculation unit that calculates a posture angle by using an angular velocity vector of a foot portion; and a foot angle calculation unit that calculates a foot angle that is an angle formed by a velocity vector of the foot portion and a center line of a foot by using the velocity vector and the posture angle.

Abstract: A storage device syndrome-weight-based error correction system includes a syndrome-weight-based error correction subsystem coupled to a storage subsystem in a chassis. The syndrome-weight-based error correction subsystem performs a plurality of respective first error correction hard decoding operations on the storage subsystem that each utilize respective read voltage thresholds and that each generate a respective final codeword candidate having a respective syndrome weight.

Abstract: A storage device syndrome-weight-based error correction system includes a syndrome-weight-based error correction subsystem that is coupled to a storage subsystem in a chassis. The syndrome-weight-based error correction subsystem performs a first error correction hard decoding operation on the storage subsystem that utilizes first read voltage thresholds and that generates a first final codeword candidate having a first syndrome weight. The syndrome-weight-based error correction subsystem then performs error correction hard decoding read voltage threshold real-time search operations to determine second read voltage thresholds, and performs a second error correction hard decoding operation on the storage subsystem that utilizes the second read voltage thresholds and that generates a second final codeword candidate having a second syndrome weight.

Abstract: A storage device syndrome-weight-based error correction system includes a syndrome-weight-based error correction subsystem coupled to a storage subsystem in a chassis. The syndrome-weight-based error correction subsystem performs a plurality of respective first error correction hard decoding operations on the storage subsystem that each utilize respective read voltage thresholds and that each generate a respective final codeword candidate having a respective syndrome weight.

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.