Method and system for assessment of cognitive workload using breathing pattern of a person

    公开(公告)号:US11523761B2

    公开(公告)日:2022-12-13

    申请号:US16889871

    申请日:2020-06-02

    Abstract: This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques. Finally based on personalized training model, the extracted breathing pattern is classified as high cognitive workload or low cognitive workload.

    METHODS AND SYSTEMS FOR AUTOMATIC VEHICLE MAINTAINANCE SCHEDULING

    公开(公告)号:US20170161964A1

    公开(公告)日:2017-06-08

    申请号:US15371930

    申请日:2016-12-07

    CPC classification number: G07C5/006 G06Q10/20 G07C5/02

    Abstract: The disclosure generally relates to vehicle maintenance and more particularly to methods and systems for automatic vehicle maintenance scheduling. The method comprises obtaining acceleration data at a fixed sampling rate of a moving vehicle when the moving vehicle attains at least a pre-determined speed for a pre-determined time from start of the moving vehicle. The obtained acceleration data is processed to generate jerk energy values at predetermined intervals. Speed normalized jerk energy is computed based on an average of the generated jerk energy values and average speed of the moving vehicle. A relationship between journey number and the computed speed normalized jerk energy is computed. A vehicle maintenance indicator based on the computed relationship between the journey number and the computed speed normalized jerk energy is finally computed.

    Methods and systems for automatic vehicle maintenance scheduling

    公开(公告)号:US10475256B2

    公开(公告)日:2019-11-12

    申请号:US15371930

    申请日:2016-12-07

    Abstract: The disclosure generally relates to vehicle maintenance and more particularly to methods and systems for automatic vehicle maintenance scheduling. The method comprises obtaining acceleration data at a fixed sampling rate of a moving vehicle when the moving vehicle attains at least a pre-determined speed for a pre-determined time from start of the moving vehicle. The obtained acceleration data is processed to generate jerk energy values at predetermined intervals. Speed normalized jerk energy is computed based on an average of the generated jerk energy values and average speed of the moving vehicle. A relationship between journey number and the computed speed normalized jerk energy is computed. A vehicle maintenance indicator based on the computed relationship between the journey number and the computed speed normalized jerk energy is finally computed.

    System and method for monitoring driving behavior of a driver

    公开(公告)号:US09702703B2

    公开(公告)日:2017-07-11

    申请号:US15074641

    申请日:2016-03-18

    CPC classification number: G01C21/165 B60W40/09 G07C5/0816

    Abstract: This disclosure relates generally to data processing, and more particularly to a system and method for monitoring driving behavior of a driver. In one embodiment, a system (102) for monitoring driving behavior of a driver is disclosed. The system (102) may configure a processor (202) to execute computer-readable instructions (208) stored in a memory (206) in order to: capture a plurality of acceleration samples; compute Kurtosis values and Skewness values corresponding to a set of acceleration samples; filter the Kurtosis values; determine a probability distribution function of the filtered Kurtosis values; compute a mean and a standard deviation associated with the filtered Kurtosis values; determine a first threshold for each driver based upon the mean and the standard deviation; compute a first score for each driver based upon the first threshold and the number of trips; determine a second threshold; compute a second score for each driver based upon the second threshold and the number of trips; and evaluate driving behavior of a driver based upon the first score and the second score.

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