A method is implemented by a computer, and includes: (a) acquiring at least one set of waveform data having a time duration from physiological information waveform data; (b) classifying a waveform included in the waveform data into a predetermined type of waveform; (c) determining validity of a classification result of the waveform; and (d) correcting the classification result in accordance with the validity of the classification result.

Systems and methods which provide a motion sensor data-driven framework for sports action recognition and/or assessment using a wearable sensor are described. A motion sensor data-driven system may provide real-time kinematical analysis to athletes engaged in active competition or training sessions under typical competition or training conditions. Analysis of motion sensor data provided according to embodiments may operate to recognize instances of one or more particular sports actions performed by an athlete and/or assess the skill of the athlete from analysis of one or more sports actions. A motion sensor data processing platform of embodiments of a motion sensor data-driven system may comprise a processor-based system configured to receive and analyze data regarding the movement of an athlete's limb reported by a wearable sensor device comprising a micro inertial measurement unit configuration for capturing and reporting data regarding the movement of an athlete's limb.

A method and system for remotely monitoring intoxication of a user, comprising: prompting the user to provide a breath sample at a time point; at a breath sample acquisition device, generating a breath sample signal upon reception of the breath sample from the user, and broadcasting a unique signature proximal in time to the time point; using a sensor of a mobile computing device, generating an authentication signal derived from detection of the unique signature; at a processing system in communication with the mobile computing device and the sample acquisition device, receiving the breath sample signal and the authentication signal; generating a verification assessment that validates provision of the breath sample by the user; determining a value of an intoxication metric for the user based upon the breath sample signal; and transforming the verification assessment and the value of the intoxication metric into an analysis of intoxication of the user.

An apparatus for correcting an input signal is configured for receiving the input signal, the received input signal comprising a series of input values. The apparatus is configured for matching a series of template values to the series of input values by warping the series of template values and the series of input values relatively to each other so as to assign one or more template values to one or more input values, wherein the series of template values represents an approximation of a noise signal that is expected to be comprised in the input signal. The apparatus is configured for obtaining a series of corrected input values based on a mismatch between the input values and their respective assigned template values. The apparatus is configured for providing a corrected signal based on the series of corrected input values.

Technologies and techniques for vehicle perception. A first contour of a current image a dna second contour of a next image are determined relative to an optical center. The current image is scaled to the next image relative to the optical center, wherein the scaling includes applying a scale vector to the first contour. A frame offset vector is determined, and the second contour is translated, based on the frame offset vector and the scale vector, to align the translated second contour to a focus of expansion. An image velocity is determined, based on the first contour and the translated second contour, wherein the image velocity is used to determine object movement from the image data.

This disclosure relates to systems and methods for shapelet decomposition based recognition using radar. State-of-the-art solutions involve use of standard machine learning classification techniques for gesture recognition which suffer with problem of dependency on collected data. The present disclosure overcome the limitations faced by the state-of-the-art solutions by obtaining a plurality of time domain signal using a radar system comprising three vertically arranged radars and one or more sensors, identifying one or more gesture windows to obtain one or more shapelets corresponding to one or gestures which are further decomposed into a plurality of sub shapelets. Further, at least one of (i) a positive or (i) a negative time delay is applied to each of the plurality of sub shapelets to obtain a plurality of composite shapelets which are further mapped with a plurality of trained shapelets to recognize gestures comprised in one or more activities performed by a subject.

The present invention relates to use an Inertial Measurement Unit (IMU) to record the acceleration trajectory of a person's gait or pen-less handwriting motion or any predesignated gestures, and to convert the data to a unique biometric pattern. The pattern is unique for each case and can be used as biometric security authentication.

An object tracking method includes configuring a color of a first illuminating object to vary in a first pattern, capturing the first illuminating object according to a first color during a first time period, and capturing the first illuminating object according to a second color during a second time period after the first time period, wherein the second color is different from the first color.

A computer implemented method and system for confirming a device documented arrhythmia in cardiac activity are provided. The method is under control of one or more processors configured with executable instructions. The method obtains a cardiac activity (CA) data set that includes CA signals for a series of cardiac events and includes device documented (DD) markers within the series of cardiac events. The device documented markers are indicative of atrial fibrillation (AF) detected by the ICM utilizing an on-board R-R interval irregularity (ORI) process to analyze the CA signals. The method applies a feature enhancement function to the CA signals to form modified CA signals with enhanced sinus features and analyzes the enhanced sinus features in the modified CA signals. The method utilized a confirmatory feature detection process to identify false AF detection by the ORI process. The method records a result of the analysis identifying false AF detection by the ORI process.

A method of processing sensor logs is described. The method includes accessing a first sensor log and a corresponding first reference log. Each of the first sensor log and the first reference log includes a series of measured values of a parameter according to a first time series. The method also includes accessing a second sensor log and a corresponding second reference log. Each of the second sensor log and the second reference log includes a series of measured values of a parameter according to a second time series. The method also includes dynamically time warping the first reference log and/or and second reference log by a first transformation between the first time series and a common time-frame and/or a second transformation between the second time series and the common time-frame. The method also includes generating first and second warped sensor logs by applying the or each transformation to the corresponding ones of the first and second sensor logs.