Sleepiness prediction apparatus and sleepiness prediction method

• US 20060224047A1
• Filed: 09/23/2005
• Published: 10/05/2006
• Est. Priority Date: 03/30/2005
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Suzuki, Takuji, Kameyama, Kenichi
• Application Number:
  US11/232,988
• Publication Number:
  US 20060224047A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1455 using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/4035 Evaluating the autonomic nervous system
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4857 Indicating the phase of biorhythm clocks or watches with indicators for biological cycles G04B19/264

Abstract

Since the sleepiness degree is changed by a daytime activity, a time and a sleep state, a sleepiness prediction apparatus in view of these is provided. The sleepiness prediction apparatus 10 includes a sensor head 14 mounted on a fingertip, and a main body 12 of a wrist watch type, measures a sleep state relevant value relevant to a sleep state of a test subject, measures a daytime activity relevant value relevant to a daytime activity of the test subject, calculates, based on the sleep state relevant value and the daytime activity relevant value, an accumulated sleepiness degree predicted to be accumulated by a sleep history of the test subject and a daytime activity, calculates a biological rhythm sleepiness degree based on a biological rhythm changing according to a time, and calculates a comprehensive sleepiness degree corresponding to a time based on the accumulated sleepiness degree and the biological rhythm sleepiness degree.

Claims

1. A sleepiness prediction apparatus comprising:

a sleep state measurement processing part of measuring a sleep state relevant value relevant to a sleep state of a test subject;

a daytime activity acquisition part of inputting or measuring a daytime activity relevant value relevant to a daytime activity of the test subject; and

an accumulated sleepiness degree calculation processing part of calculating, based on the sleep state relevant value and the daytime activity relevant value, an accumulated sleepiness degree predicted to be accumulated by a sleep history of the test subject and the daytime activity.

2. A sleepiness prediction apparatus according to claim 1, further comprising:

a biological rhythm sleepiness degree calculation processing part of calculating a biological rhythm sleepiness degree based on a biological rhythm changing according to a time; and

a comprehensive sleepiness degree calculation processing part of calculating a comprehensive sleepiness degree corresponding to a time based on the accumulated sleepiness degree and the biological rhythm sleepiness degree.

3. A sleepiness prediction apparatus according to claim 1, wherein the sleep state measurement processing part includes:

an autonomic nervous index value measurement processing part of measuring an autonomic nervous index value of the test subject;

a body movement detection processing part of detecting a body movement state of the test subject;

an awakening/sleep judgment processing part of judging whether the test subject is wakeful or asleep based on the body movement state; and

a sleep state judgment processing part of obtaining a sleep state relevant value based on the autonomic nervous index value at a time when it is judged that the test subject is asleep.

4. A sleepiness prediction apparatus according to claim 1, wherein the sleep state relevant value is one of a deep sleep time of the test subject, a REM sleep time, the number of times of REM sleep, and a total sleep time.

5. A sleepiness prediction apparatus according to claim 1, wherein the daytime activity relevant value is one of a daytime activity amount of the test subject, a daytime metabolic amount of the test subject, an amount of exposure of the test subject to daytime light, and a stress value of the test subject.

6. A sleepiness prediction apparatus according to claim 5, wherein the daytime activity acquisition processing part includes:

an activity amount measurement processing part of measuring the daytime activity amount of the test subject;

a metabolic amount measurement processing part of measuring the daytime metabolic amount of the test subject;

a light irradiation amount measurement processing part of measuring the amount of the exposure of the test subject to the daytime light; and

a stress state measurement processing part of measuring the stress value relevant to stress of the test subject.

7. A sleepiness prediction apparatus according to claim 1, wherein the accumulated sleepiness degree calculation processing part multiplies a value of each of a deep sleep time of the test subject, a REM sleep time, the number of times of REM sleep, and a total sleep time, each of which is the sleep state relevant value, a daytime activity amount of the test subject, a daytime metabolic amount of the test subject, an amount of exposure of the test subject to daytime light, and a stress value of the test subject, each of which is the daytime action relevant value, by a weight and adds up them.

8. A sleepiness prediction apparatus according to claim 7, wherein the accumulated sleepiness degree calculation processing part includes:

a sleepiness degree input processing part by which the test subject inputs a sleepiness degree plural times; and

a weight learning processing part of calculating back to and learning the weight of each of the values by a least square method from the plural inputted sleepiness degrees, the respective values of the inputted or measured sleep state relevant values, and the respective values of the daytime activity relevant values.

9. A sleepiness prediction apparatus according to claim 2, wherein the biological rhythm sleepiness degree is expressed by a sinusoidal composite function of a 24-hour circadian rhythm component and a 12-hour circasemidian rhythm component.

10. A sleepiness prediction apparatus according to claim 2, further comprising a display processing part of displaying the accumulated sleepiness degree or the comprehensive sleepiness degree simultaneously with a schedule of the test subject.

11. A sleepiness prediction method, comprising the steps of:

measuring a sleep state relevant value relevant to a sleep state of a test subject;

inputting or measuring a daytime action relevant value relevant to a daytime activity of the test subject; and

calculating, based on the sleep state relevant value and the daytime activity relevant value, an accumulated sleepiness degree predicted to be accumulated by a sleep history of the test subject and a daytime activity.

12. A program of a sleepiness prediction method, causing a computer to realize:

a sleep state measurement function of measuring a sleep state relevant value relevant to a sleep state of a test subject;

a daytime activity acquisition function of inputting or measuring a daytime activity relevant value relevant to a daytime activity of the test subject; and

an accumulated sleepiness degree calculation function of calculating, based on the sleep state relevant value and the daytime activity relevant value, an accumulated sleepiness degree predicted to be accumulated by a sleep history of the test subject and a daytime activity.