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US20200367802A1 - Systems and Methods for Impairment Detection Using Historical Data - Google Patents

Systems and Methods for Impairment Detection Using Historical Data
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Publication number
US20200367802A1
US20200367802A1US16/821,002US202016821002AUS2020367802A1US 20200367802 A1US20200367802 A1US 20200367802A1US 202016821002 AUS202016821002 AUS 202016821002AUS 2020367802 A1US2020367802 A1US 2020367802A1
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United States
Prior art keywords
monitored individual
impairment
individual
monitor device
threshold
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
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US16/821,002
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Duke Hanson
Dustin PETTIT
Joseph P. Newell
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BI Inc
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BI Inc
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Priority to US16/821,002priorityCriticalpatent/US20200367802A1/en
Assigned to BI INCORPORATEDreassignmentBI INCORPORATEDASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS).Assignors: HANSON, DUKE, NEWELL, JOSEPH P., PETTIT, Dustin
Publication of US20200367802A1publicationCriticalpatent/US20200367802A1/en
Assigned to ALTER DOMUS PRODUCTS CORP., AS ADMINISTRATIVE AGENTreassignmentALTER DOMUS PRODUCTS CORP., AS ADMINISTRATIVE AGENTSECURITY INTEREST (SEE DOCUMENT FOR DETAILS).Assignors: B.I. INCORPORATED, BI MOBILE BREATH, INC.
Assigned to ANKURA TRUST COMPANYreassignmentANKURA TRUST COMPANYPATENT SECURITY AGREEMENTAssignors: B.I. INCORPORATED, BI MOBILE BREATH, INC.
Assigned to BI MOBILE BREATH, INC., B.I. INCORPORATEDreassignmentBI MOBILE BREATH, INC.RELEASE BY SECURED PARTY (SEE DOCUMENT FOR DETAILS).Assignors: ALTER DOMUS PRODUCTS CORP.
Assigned to BI MOBILE BREATH, INC., B.I. INCORPORATEDreassignmentBI MOBILE BREATH, INC.RELEASE BY SECURED PARTY (SEE DOCUMENT FOR DETAILS).Assignors: ANKURA TRUST COMPANY, LLC
Abandonedlegal-statusCriticalCurrent

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Abstract

Various embodiments provide systems and methods for identifying impairment using measurement devices.

Description

Claims (21)

What is claimed is:
1. A method for identifying individual impairment, the method comprising:
generating a characteristic of a monitored individual based at least in part on data received from a sensor on a user detached monitor device associated with the monitored individual;
generating an impairment value by the user detached monitor device based at least in part on the characteristic of the monitored individual;
receiving historical data about the monitored individual; and
increasing the impairment value based at least in part on the historical data about the monitored individual.
2. The method ofclaim 1, wherein the historical data is an indication that the monitored individual has a substance addiction.
3. The method ofclaim 1, wherein the historical data is a location where the monitored individual has previously been.
4. The method ofclaim 1, wherein the historical data is a person with whom the monitored individual was in proximity during a previous time period.
5. The method ofclaim 1, wherein the characteristic of the monitored individual is an eye movement characteristic, wherein the sensor is a camera, and wherein generating the characteristic of the monitored individual includes:
displaying a video on a display of the user detached monitor device;
receiving images of eyes of the monitored individual captured by the camera while the monitored individual watches the video; and
using the received images to calculate the eye movement characteristic.
6. The method ofclaim 5, wherein generating the impairment value includes:
comparing the eye movement characteristic to a baseline eye movement threshold; and
generating the impairment value based upon the comparison of the eye movement characteristic and the baseline eye movement threshold.
7. The method ofclaim 1, wherein the characteristic of the monitored individual is a balance characteristic, wherein the sensor is an accelerometer, and wherein generating the characteristic of the monitored individual includes:
receiving acceleration data from the accelerometer; and
calculating the balance characteristic based upon the acceleration data.
8. The method ofclaim 7, wherein generating the impairment value includes:
comparing the balance characteristic to a baseline balance threshold; and
generating the impairment value based upon the comparison of the balance characteristic and the baseline balance threshold.
9. The method ofclaim 7, wherein the characteristic of the monitored individual is a balance characteristic, wherein the sensor is an accelerometer, and wherein generating the characteristic of the monitored individual includes:
providing an instruction for the monitored individual to engage in a particular movement via the user detached monitor device; and
wherein the acceleration data is received as the monitored individual engages in the particular movement.
10. The method ofclaim 9, wherein the particular movement is selected from a group consisting of: walking in a straight line, and standing on one foot.
11. The method ofclaim 1, wherein the characteristic of the monitored individual is a duration of time for an action taken by the monitored individual, wherein the sensor is a timer, and wherein generating the characteristic of the monitored individual includes:
displaying a game via a display on the user detached monitor device; and
receiving timer data from the timer, wherein the timer data indicates the duration of time for an action taken by the monitored individual while playing the game.
12. The method ofclaim 11, wherein generating the impairment value includes:
comparing the duration of time for the action taken by the monitored individual to a baseline reaction threshold; and
generating the impairment value based upon the comparison of the duration of time for the action taken by the monitored individual and the baseline reaction threshold.
13. A system for determining a likelihood of impairment, the system comprising:
a user detached monitor device, wherein the user detached monitor device includes:
a sensor;
a display;
a processor; and
a computer readable medium including non-transitory instructions executable by the processor to:
generate a characteristic of a monitored individual based at least in part on data received from the sensor;
generate an impairment value based at least in part on the characteristic of the monitored individual;
receive historical data about the monitored individual; and
increase the impairment value based at least in part on the historical data about the monitored individual.
14. The system ofclaim 13, wherein the system further comprises:
a user attached monitor device physically attached to the monitored individual; and
wherein the computer readable medium further includes non-transitory instructions executable by the processor to:
receive a test setup request from a user attached monitor device physically attached to the monitored individual, wherein the test setup request indicates a particular impairment test; and
start the particular impairment test by enabling the sensor and requesting that the monitored individual perform a particular activity.
15. The system ofclaim 13, wherein the historical data is selected from a group consisting of: an indication that the monitored individual has a substance addiction, a location where the monitored individual has previously been, and a person with whom the monitored individual was in proximity during a previous time period.
16. The system ofclaim 13, wherein the characteristic of the monitored individual is an eye movement characteristic, wherein the sensor is a camera, and wherein the non-transitory instructions executable by the processor to generate the characteristic of a monitored individual include non-transitory instructions executable by the processor to:
display a video on a display of the user detached monitor device;
receive images of eyes of the monitored individual captured by the camera while the monitored individual watches the video; and
use the received images to calculate the eye movement characteristic.
17. The system ofclaim 16, wherein the non-transitory instructions executable by the processor to generate the impairment value include non-transitory instructions executable by the processor to:
compare the eye movement characteristic to a baseline eye movement threshold; and
generate the impairment value based upon the comparison of the eye movement characteristic and the baseline eye movement threshold.
18. The method ofclaim 13, wherein the characteristic of the monitored individual is a balance characteristic, wherein the sensor is an accelerometer, and wherein the non-transitory instructions executable by the processor to generate the characteristic of a monitored individual include non-transitory instructions executable by the processor to:
receive acceleration data from the accelerometer; and
calculate the balance characteristic based upon the acceleration data.
19. The system ofclaim 18, wherein the non-transitory instructions executable by the processor to generate the impairment value include non-transitory instructions executable by the processor to:
compare the balance characteristic to a baseline balance threshold; and
generate the impairment value based upon the comparison of the balance characteristic and the baseline balance threshold.
20. The system ofclaim 13, wherein the characteristic of the monitored individual is a duration of time for an action taken by the monitored individual, wherein the non-transitory instructions executable by the processor to generate the characteristic of a monitored individual include non-transitory instructions executable by the processor to:
display a game via a display on the user detached monitor device; and
receive timer data from the timer, wherein the timer data indicates the duration of time for an action taken by the monitored individual while playing the game.
21. The system ofclaim 20, wherein the non-transitory instructions executable by the processor to generate the impairment value include non-transitory instructions executable by the processor to:
compare the duration of time for the action taken by the monitored individual to a baseline reaction threshold; and
generate the impairment value based upon the comparison of the duration of time for the action taken by the monitored individual and the baseline reaction threshold.
US16/821,0022019-05-222020-03-17Systems and Methods for Impairment Detection Using Historical DataAbandonedUS20200367802A1 (en)

Priority Applications (1)

Application NumberPriority DateFiling DateTitle
US16/821,002US20200367802A1 (en)2019-05-222020-03-17Systems and Methods for Impairment Detection Using Historical Data

Applications Claiming Priority (5)

Application NumberPriority DateFiling DateTitle
US201962851127P2019-05-222019-05-22
US201962936024P2019-11-152019-11-15
US201962939588P2019-11-232019-11-23
US202062966709P2020-01-282020-01-28
US16/821,002US20200367802A1 (en)2019-05-222020-03-17Systems and Methods for Impairment Detection Using Historical Data

Publications (1)

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US20200367802A1true US20200367802A1 (en)2020-11-26

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Application NumberTitlePriority DateFiling Date
US16/821,002AbandonedUS20200367802A1 (en)2019-05-222020-03-17Systems and Methods for Impairment Detection Using Historical Data
US16/820,969ActiveUS11147489B2 (en)2019-05-222020-03-17Systems and methods for stand alone impairment detection
US16/820,988AbandonedUS20200367801A1 (en)2019-05-222020-03-17Systems and Methods for Balance Deviation Detection in a Monitoring System
US16/821,012Active2041-01-06US12133729B2 (en)2019-05-222020-03-17Systems and methods for multi-tiered impairment testing
US16/820,942Active2041-09-12US11672453B2 (en)2019-05-222020-03-17Systems and methods for impairment testing in a monitoring system
US16/821,032Active2041-04-09US11529082B2 (en)2019-05-222020-03-17Systems and methods for impairment baseline learning
US17/006,748Active2041-09-21US11931150B2 (en)2019-05-222020-08-28Wrist-worn impairment detection and methods for using such
US17/969,520ActiveUS11832945B2 (en)2019-05-222022-10-19Systems and methods for impairment baseline learning
US18/433,122PendingUS20240172978A1 (en)2019-05-222024-02-05Wrist-worn impairment detection and methods for using such

Family Applications After (8)

Application NumberTitlePriority DateFiling Date
US16/820,969ActiveUS11147489B2 (en)2019-05-222020-03-17Systems and methods for stand alone impairment detection
US16/820,988AbandonedUS20200367801A1 (en)2019-05-222020-03-17Systems and Methods for Balance Deviation Detection in a Monitoring System
US16/821,012Active2041-01-06US12133729B2 (en)2019-05-222020-03-17Systems and methods for multi-tiered impairment testing
US16/820,942Active2041-09-12US11672453B2 (en)2019-05-222020-03-17Systems and methods for impairment testing in a monitoring system
US16/821,032Active2041-04-09US11529082B2 (en)2019-05-222020-03-17Systems and methods for impairment baseline learning
US17/006,748Active2041-09-21US11931150B2 (en)2019-05-222020-08-28Wrist-worn impairment detection and methods for using such
US17/969,520ActiveUS11832945B2 (en)2019-05-222022-10-19Systems and methods for impairment baseline learning
US18/433,122PendingUS20240172978A1 (en)2019-05-222024-02-05Wrist-worn impairment detection and methods for using such

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US20230048282A1 (en)2023-02-16
US20200367801A1 (en)2020-11-26
US20200372271A1 (en)2020-11-26
US12133729B2 (en)2024-11-05
US20200390388A1 (en)2020-12-17
US20200372824A1 (en)2020-11-26
US20240172978A1 (en)2024-05-30
US20200367814A1 (en)2020-11-26
US20200367799A1 (en)2020-11-26
US11832945B2 (en)2023-12-05
US11931150B2 (en)2024-03-19
US11672453B2 (en)2023-06-13
US11147489B2 (en)2021-10-19
US11529082B2 (en)2022-12-20

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