TECHNOLOGY ADAPTED TO ENABLE IMPROVED COLLECTION OF INVOLUNTARY EYELlD MOVEMENT PARAMETERS, INCLUDING COLLECTION OF EYELlD MOVEMENT PARAMETERS TO SUPPORT ANALYSIS OF NEUROLOGICAL FACTORS

    公开(公告)号:US20210236023A1

    公开(公告)日:2021-08-05

    申请号:US16972535

    申请日:2019-06-05

    Abstract: Technology is adapted to enable improved collection of involuntary eyelid movement parameters, including collection of eyelid movement parameters to support analysis of neurological factors. For example, this may include methods and systems configured to enable improved analysis of involuntary eyelid movement parameters, including diagnosis of subject neurological conditions and/or other subject attributes from analysis of involuntary eyelid movement parameters. Some embodiments relate to testing, which provide a standardized environment for collection of involuntary eyelid movement data thereby to reduce influence of variable factors, which affect involuntary eyelid movement. For example, the standardized environment influences the subject to adopt a controlled cognitive and/or physiological state, thereby to improve comparability of test results. In some cases, the controlled test parameters include a test parameter, which influences the subject to voluntarily maintain a substantially consistent gaze detection, thereby to minimize eye movement.

    MONITORING DROWSINESS
    9.
    发明申请

    公开(公告)号:US20170119248A1

    公开(公告)日:2017-05-04

    申请号:US15318417

    申请日:2015-06-19

    Abstract: Lower sampling rates have been found to provide sufficient data for use in the method of U.S. Pat. Nos. 7,071,831, 7,616,125 and 7,791,491. The method of determining drowsiness includes the steps of receiving eye movement data collected at sampling rates as low as 20 Hz. The data is preferentially interpolated to provide a data set at a higher sampling rate in the order of 500 Hz. For each data point, values of amplitude and velocity of eye movement and whether the measures relate to eyelid opening or closing are derived. An algorithm is then used to obtain values of the amplitude to velocity ratios of eyelid opening and closing and using these values in an algorithm for providing a measure of drowsiness. With this method eyelid and eye movement may be monitored using any suitable technology or sensor including video or digital camera technology to identify and measure the appropriate ocular movements.

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