Drowsiness-estimating device and drowsiness-estimating method

• US 9,105,172 B2
• Filed: 05/25/2012
• Issued: 08/11/2015
• Est. Priority Date: 05/26/2011
• Status: Active Grant

• Current Assignee: Panasonic Automotive Systems Company of America (Panasonic Holdings Corporation)
• Original Assignee: Panasonic Corporation (Panasonic Holdings Corporation)
• Inventors: Kubotani, Hiroyuki, Kitajima, Hiroki
• Primary Examiner(s): BLOUNT, ERIC
• Application Number:
  US14/118,361
• Publication Number:
  US 20140062704A1
• Time in Patent Office: 1,173 Days
• Field of Search: 340/575, 340/576, 340/573.1
• US Class Current: 1/1
• CPC Class Codes: A61B 5/1079 using optical or photographic means
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G08B 21/06 indicating a condition of sleep, e.g. anti-dozing alarms

Abstract

A drowsiness-estimating device capable of improving the precision of drowsiness estimation by eliminating the effect of individual differences. In the device, a recurrence required time-calculating part (103) calculates the recurrence required time, which is the time needed, after a detection time when an action is detected, for a drowsiness estimation parameter value acquired after the detection time to return to the value of the drowsiness estimation parameter acquired before the detection time. A drowsiness-estimating part (104) estimates the level of drowsiness of the drowsiness-estimation subject on the basis of the calculated recurrence required time. To be specific, the drowsiness-estimating part (104) maintains a drowsiness level-estimating table in which each of multiple time ranges is correlated with a possible drowsiness level, and specifies a possible drowsiness level that corresponds to the time range, among the multiple time ranges, with which the recurrence required time is associated.

Claims

1. A drowsiness estimating apparatus for estimating a drowsiness level of a subject of drowsiness estimation from among a plurality of drowsiness level candidates, the drowsiness estimating apparatus comprising:

an acquisition section that acquires a drowsiness estimation parameter at each of a plurality of acquisition timings;

a detecting section that detects an action of the subject of drowsiness estimation;

a time-calculating section that calculates a recurrence required time which is the time it takes, from a detection timing at which the action is detected, for a value of the drowsiness estimation parameter acquired after the detection timing to return to a value of the drowsiness estimation parameter acquired before the detection timing; and

an estimating section that estimates a drowsiness level of the subject of drowsiness estimation based on the calculated recurrence required time.

2. The drowsiness estimating apparatus according to claim 1, wherein:

the acquisition section comprises a level calculation section that calculates a second drowsiness level as the drowsiness estimation parameter based on face expression feature information of the subject of drowsiness estimation; and

the time-calculating section calculates the recurrence required time only when the second drowsiness level is associated with a drowsiness level candidate other than the highest and lowest drowsiness levels among the plurality of the drowsiness level candidates.

3. The drowsiness-estimating apparatus according to claim 1, wherein the estimation section holds a drowsiness level estimation table in which a plurality of time ranges are associated with the drowsiness level candidates, respectively, and the estimation section identifies a drowsiness level candidate corresponding to a time range to which the calculated recurrence required time belongs from among the plurality of time ranges.

4. The drowsiness-estimating apparatus according to claim 3, further comprising:

an action-classifying section that classifies an action detected by the detection section into one of a plurality of classes based on an action classification parameter; and

a table-adjusting section that adjusts a time defining each of the time ranges of the drowsiness level estimation table in accordance with the class of classification.

5. The drowsiness-estimating apparatus according to claim 4, wherein the action classification parameter is a size of the detected action.

6. The drowsiness-estimating apparatus according to claim 4, wherein the action classification parameter is a frequency of occurrence of an event that evokes the detected action.

7. A drowsiness-estimating method that estimates a drowsiness level of a subject of drowsiness estimation from among a plurality of drowsiness level candidates, the drowsiness-estimating method comprising:

acquiring a drowsiness estimation parameter at each of a plurality of acquisition timings;

detecting an action of the subject of drowsiness estimation;

calculating a recurrence required time which is the time it takes, from a detection timing at which the action is detected, for a value of the drowsiness estimation parameter acquired after the detection timing to return to a value of the drowsiness estimation parameter acquired before the direction timing; and

estimating a drowsiness level of the subject of drowsiness estimation based on the calculated recurrence required time.