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.

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.

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.

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.

公开(公告)号：US20190216391A1

公开(公告)日：2019-07-18

申请号：US16364003

申请日：2019-03-25

Applicant: CURAEGIS TECHNOLOGIES, INC.

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

IPC: A61B5/00 ,  G16H20/00 ,  A61B5/0205 ,  A61B5/16 ,  A61B5/11 ,  A61B5/18 ,  A61B5/01

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 ,  G16H20/00 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50

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.