The present disclosure provides wearable apparatus and method for calculating drift-free plantar pressure parameters for gait monitoring of an individual. Most conventional techniques use different kind of sensors placed in in-sole based wearable apparatus but are costly and not effective in calculating accurate plantar pressure parameters. The disclosed wearable apparatus uses off-the shelf piezoelectric sensors that are widely available in market with less cost. The drift-free plantar pressure parameters are calculated using drift-free static pressure data obtained by numerically integrating acquired dynamic sensor data from the piezoelectric sensors, using a LiTCEM correction mechanism. A 6-DOF Inertial Measurement Unit (IMU sensor) helps in isolating zero-pressure duration indicating when a foot of the individual is in air during a stride, while obtaining the drift-free static pressure data. The disclosed wearable apparatus calculate the drift-free plantar pressure parameters for long duration and facilitates monitoring walking patterns of the individual.

A method and computer program are provided for operating a motorized two-wheeled vehicle, in particular a motorcycle that includes a sensor system for accident recognition that generates measuring signals. The sensor system is used for recognizing a rotation of a front wheel of the two-wheeled vehicle that deviates from a normal steering movement and allows an inference concerning a collision of the two-wheeled vehicle with another object. Moreover, the invention relates to a computer program for carrying out the method.

A movement distance calculation device includes: a first movement distance calculation unit which calculates a first movement distance of a movable body based on plural rotation speeds of plural wheels of the movable body and a steering angle of the movable body; a vector detection unit which detects a movement vector of an object included in the acquired images as defined herein; a second movement distance calculation unit which calculates a second movement distance of the movable body based on the movement vector; a first reliability determining unit which determines reliability of the calculated first movement distance as defined herein; a second reliability determining unit which determines reliability of the calculated second movement distance as defined herein; and a movement distance determining unit which determines a movement distance using at least one of the calculated first movement distance and the calculated second movement distance as defined herein.

A movement distance calculation device includes: a first movement distance calculation unit which calculates a first movement distance of a movable body based on plural rotation speeds of plural wheels of the movable body and a steering angle of the movable body; a vector detection unit which detects a movement vector of an object included in the acquired images as defined herein; a second movement distance calculation unit which calculates a second movement distance of the movable body based on the movement vector; a first reliability determining unit which determines reliability of the calculated first movement distance as defined herein; a second reliability determining unit which determines reliability of the calculated second movement distance as defined herein; and a movement distance determining unit which determines a movement distance using at least one of the calculated first movement distance and the calculated second movement distance as defined herein.

A device for multi-parameter integrated monitoring of a deep submarine turbidity current primarily includes cement pile pore-pressure monitoring, optical turbidity monitoring, floating ball flow velocity monitoring, and turbidity current sediment sampling, can observe the turbidity, excess pore pressure, flow velocity, and other parameters of the turbidity current, can fulfill simultaneous and real-time transmission for in-situ monitoring, and can complete multiple tasks at the same observing position, so that the situation where the sampling position and the observing position are different due to the movement of an apparatus along with a ship during ordinary work is avoided.

A device for multi-parameter integrated monitoring of a deep submarine turbidity current primarily includes cement pile pore-pressure monitoring, optical turbidity monitoring, floating ball flow velocity monitoring, and turbidity current sediment sampling, can observe the turbidity, excess pore pressure, flow velocity, and other parameters of the turbidity current, can fulfill simultaneous and real-time transmission for in-situ monitoring, and can complete multiple tasks at the same observing position, so that the situation where the sampling position and the observing position are different due to the movement of an apparatus along with a ship during ordinary work is avoided.

A data processing device includes a peak detector that detects a peak from a frequency spectrum and a map generator that generates an abnormality map for the frequency spectrum. The abnormality map includes as abnormal components, a frequency of a detected peak of interest and a frequency of a peak that appears together with the peak of interest when the peak of interest is assumed as the peak originating from abnormality. The data processing device includes an abnormal peak extractor that extracts as an abnormal peak, a peak at a frequency that matches with any of the abnormal components included in the abnormality map and a first criterion value calculator that calculates a first criterion value representing occurrence of abnormality corresponding to the abnormality map based on a spectral density of the abnormal peak.

A data processing device includes a peak detector that detects a peak from a frequency spectrum and a map generator that generates an abnormality map for the frequency spectrum. The abnormality map includes as abnormal components, a frequency of a detected peak of interest and a frequency of a peak that appears together with the peak of interest when the peak of interest is assumed as the peak originating from abnormality. The data processing device includes an abnormal peak extractor that extracts as an abnormal peak, a peak at a frequency that matches with any of the abnormal components included in the abnormality map and a first criterion value calculator that calculates a first criterion value representing occurrence of abnormality corresponding to the abnormality map based on a spectral density of the abnormal peak.

A wheel position detection apparatus applied to a vehicle including a vehicle body attached with multiple travelling wheels. The apparatus includes: multiple transmitters respectively disposed at travelling wheels; and a receiver disposed at the vehicle body. The transmitter includes: an acceleration sensor outputting a detection signal according to an acceleration including a gravitational acceleration component being varied by a rotation of the corresponding travelling wheel attached with the transmitter; and a first controller creating a frame including unique identification information, and transmit the frame in response to the receiver outputting a transmission command. The receiver includes a second controller executing a wheel position detection through: identifying, from each set of the unique identification information included in the frame, which of the travelling wheels is attached with the transmitter having transmitted the frame; and registering the travelling wheels in association with the unique identification information of the transmitter.

Systems and methods are provided for estimating a walking speed of a subject. A method comprises mounting an inertial measurement unit (IMU) on a wrist of the subject, the IMU configured to generate acceleration and rate of turn signals; processing the acceleration and rate of turn signals from the IMU to generate a pitch angle and a roll angle; processing the pitch angle and the roll angle to generate a rotation matrix from a sensor frame of the IMU to a navigation frame of the subject; applying the rotation matrix to the acceleration signals and removing gravitational acceleration to generate an external acceleration signal; processing the external acceleration signal to determine a principal horizontal axis and to generate a principal component acceleration signal representing external acceleration along the principal horizontal axis; and processing the principal component acceleration signal using a regression-based method to determine an estimated walking speed of the subject.