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    Alertness prediction system and method
    1.
    发明授权
    Alertness prediction system and method 有权

    公开(公告)号：US10905372B2

    公开(公告)日：2021-02-02

    申请号：US16785033

    申请日：2020-02-07

    Applicant: CURAEGIS TECHNOLOGIES, INC.

    Inventor： Matt Kenyon ,  Colin Payne-Rogers ,  Josh Jones

    IPC: A61B5/11 ,  A61B5/00 ,  A61B5/01 ,  A61B5/0205 ,  A61B5/16 ,  G16H50/50 ,  G16H50/30 ,  G16H50/20 ,  A61B5/18 ,  G16H20/00 ,  A61B5/024

    Abstract: An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

    ROTATABLE PISTON ASSEMBLY
    2.
    发明申请
    ROTATABLE PISTON ASSEMBLY 审中-公开

    公开(公告)号：US20190112926A1

    公开(公告)日：2019-04-18

    申请号：US16160319

    申请日：2018-10-15

    Applicant: CurAegis Technologies, Inc.

    Inventor： Douglas A. Hemink

    IPC: F01B3/00 ,  F01B3/02 ,  F01B3/10

    Abstract: A rotatable piston assembly for a reciprocating piston type hydraulic machine includes a rotatable piston configured for a controlled rotation and configured to reciprocate within a cylinder bore of the reciprocating piston type hydraulic machine.

    ALERTNESS PREDICTION SYSTEM AND METHOD
    3.
    发明申请
    ALERTNESS PREDICTION SYSTEM AND METHOD 审中-公开

    公开(公告)号：US20200170573A1

    公开(公告)日：2020-06-04

    申请号：US16785033

    申请日：2020-02-07

    Applicant: CURAEGIS TECHNOLOGIES, INC.

    Inventor： Matt Kenyon ,  Colin Payne-Rogers ,  Josh Jones

    IPC: A61B5/00 ,  A61B5/01 ,  A61B5/0205 ,  A61B5/11 ,  A61B5/16 ,  G16H50/50 ,  G16H50/30 ,  G16H50/20 ,  A61B5/18 ,  G16H20/00

    Abstract: An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

    Alertness prediction system and method
    4.
    发明授权
    Alertness prediction system and method 有权

    公开(公告)号：US10238335B2

    公开(公告)日：2019-03-26

    申请号：US15436039

    申请日：2017-02-17

    Applicant: CurAegis Technologies, Inc.

    Inventor： Matt Kenyon ,  Colin Payne-Rogers ,  Josh Jones

    IPC: A61B5/11 ,  A61B5/00 ,  A61B5/01 ,  A61B5/0205 ,  A61B5/16 ,  G06F19/00 ,  A61B5/18 ,  A61B5/024

    Abstract: An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

    Alertness prediction system and method
    5.
    发明授权
    Alertness prediction system and method 有权

    公开(公告)号：US10588567B2

    公开(公告)日：2020-03-17

    申请号：US16364003

    申请日：2019-03-25

    Applicant: CURAEGIS TECHNOLOGIES, INC.

    Inventor： Matt Kenyon ,  Colin Payne-Rogers ,  Josh Jones

    IPC: A61B5/01 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/11 ,  A61B5/16 ,  G16H50/50 ,  G16H50/30 ,  G16H50/20 ,  A61B5/18 ,  G16H20/00 ,  A61B5/024

    Abstract: An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two-process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to improve the accuracy of the alertness estimation even further.

    ALERTNESS PREDICTION SYSTEM AND METHOD
    10.
    发明申请
    ALERTNESS PREDICTION SYSTEM AND METHOD 审中-公开
    警觉预测系统和方法

    公开(公告)号：WO2017143179A1

    公开(公告)日：2017-08-24

    申请号：PCT/US2017/018355

    申请日：2017-02-17

    Applicant: CURAEGIS TECHNOLOGIES, INC.

    Inventor： KENYON, Matt ,  PAYNE-ROGERS, Colin ,  JONES, Josh

    IPC: A61B5/16 ,  A61B5/0205 ,  A61B5/11 ,  A61B5/00 ,  A61B5/01 ,  G06F19/00

    CPC classification number: A61B5/4857 ,  A61B5/0022 ,  A61B5/01 ,  A61B5/0205 ,  A61B5/02055 ,  A61B5/02438 ,  A61B5/11 ,  A61B5/1118 ,  A61B5/165 ,  A61B5/18 ,  A61B5/4809 ,  A61B5/7267 ,  A61B5/7275 ,  A61B5/7278 ,  A61B5/7282 ,  A61B2560/0242 ,  G06F19/00 ,  G06F19/34

    Abstract: An alertness prediction bio-mathematical model for use in devices such as a wearable device that improves upon previous models of predicting fatigue and alertness by gathering data from the individual being monitored to create a more accurate estimation of alertness levels. The bio-mathematical model may be a two- process algorithm which incorporates a sleep-wake homeostasis aspect and a circadian rhythm aspect. The sleep-wake homeostasis aspect of the model is improved by using actigraphy measures in conjunction with distal skin, ambient light and heart rate measures to improve the accuracy of the sleep and wake estimations. The circadian rhythm model aspect improves fatigue prediction and estimation by using distal skin, heart rate and actigraphy data. The sleep-wake homeostasis and circadian rhythm aspects may also be combined with additional objective and subjective measures as well as information from a user to Improve the accuracy of the alertness estimation even further.

    Abstract translation: 用于诸如可穿戴设备之类的设备中的警觉性预测生物数学模型，其通过从正被监测的个体收集数据来改进预测疲劳和警觉性的先前模型，以创建更准确的警觉性估计 水平。 生物数学模型可以是结合了睡眠 - 觉醒内稳态方面和昼夜节律方面的两过程算法。 该模型的睡眠 - 觉醒稳态方面通过使用与远端皮肤，周围光线和心率测量结合的活动记录测量来改善睡眠和觉醒估计的准确性。 昼夜节律模型方面通过使用远端皮肤，心率和活动记录数据来改善疲劳预测和估计。 睡眠觉醒内稳态和昼夜节律方面也可以与额外的客观和主观测量以及来自用户的信息结合，以进一步提高警觉性估计的准确性。

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