A method of estimating a speed of an object in real-time includes acquiring from an acceleration sensor mounted on the object a first original acceleration signal indicative of an acceleration of the object in a direction parallel to a travelling direction of the object, filtering the first original acceleration signal to remove noise to produce a first filtered acceleration signal, down-sampling the first filtered acceleration signal to obtain a first optimized acceleration signal, and calculating an estimated speed value of the object along the direction parallel to the travelling direction of the object based on the first optimized acceleration signal.

A method of estimating a speed of an object in real-time includes acquiring from an acceleration sensor mounted on the object a first original acceleration signal indicative of an acceleration of the object in a direction parallel to a travelling direction of the object, filtering the first original acceleration signal to remove noise to produce a first filtered acceleration signal, down-sampling the first filtered acceleration signal to obtain a first optimized acceleration signal, and calculating an estimated speed value of the object along the direction parallel to the travelling direction of the object based on the first optimized acceleration signal.

A method for evaluating sensor data. In the method, firstly, raw sensor data and/or processed sensor data from at least one sensor are input and measurement data determined from the raw sensor data and/or the processed sensor data. The measurement data are then corrected on the basis of a mathematical model, wherein, on correction, drift of the raw sensor data and/or of the processed sensor data is determined and removed from the measurement data. The corrected measurement data are furthermore output.

A method for evaluating sensor data. In the method, firstly, raw sensor data and/or processed sensor data from at least one sensor are input and measurement data determined from the raw sensor data and/or the processed sensor data. The measurement data are then corrected on the basis of a mathematical model, wherein, on correction, drift of the raw sensor data and/or of the processed sensor data is determined and removed from the measurement data. The corrected measurement data are furthermore output.

An equipment fitting system that measures swings by a user of different pieces of equipment with inertial sensors, and analyzes sensor data to recommend which piece of equipment is optimal for the user from among those tested. Illustrative applications include fitting of baseball bats and golf clubs. Swing metrics calculated from sensor data may include an acceleration metric, a speed metric, and a momentum metric; these metrics may be combined into a metrics score for each piece of equipment. Other factors may be included in an overall score, such as the user's subjective score for each piece of equipment, and ratings from experts or other consumers. Users may assign the relative importance for the different factors to calculate an overall equipment score.

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.

Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the preintegration term, and the wearable head device presents the virtual content based on the position of the device.

Examples of the disclosure describe systems and methods for presenting virtual content on a wearable head device. In some embodiments, a state of a wearable head device is determined by minimizing a total error based on a reduced weight associated with a reprojection error. A view reflecting the determined state of the wearable head device is presented via a display of the wearable head device. In some embodiments, a wearable head device calculates a preintegration term based on the image data received via a sensor of the wearable head device and the inertial data received via a first IMU and a second IMU of the wearable head device. The wearable head device estimates a position of the device based on the preintegration term, and the wearable head device presents the virtual content based on the position of the device.

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.