Patent US5884626A successfully refreshed from Google Patents.
US5884626A
Expired / Lapsed
US
⏱️ 0.0 years remaining
Apparatus and method for analyzing information relating to physical and mental condition
Assignee: National Institute of Advanced Industrial Science and Technology
•
Filing: 1995-08-31
•
Granted: 1999-03-23
•
Expires: 2015-08-31
Abstract
Abstract An apparatus and method are provided for analyzing information relating to the physiological and psychological conditions of a driver. Psychological conditions such as comfortableness or degree of alertness are estimated on the basis of physical data such as fluctuation in brain waves. This apparatus comprises a first neural network having a pre-processed 1/f fluctuation signal for brain waves as an input and for estimating a degree of alertness of the driver, and a second neural network receiving the estimated degree of alertness and the pre-processed 1/f fluctuation signal, for estimating and outputting driving comfortableness. By employing a neural network, which has a mapping ability as well as flexible adaptability even for non-linear data, based on the learning function, more accurate estimation of mental conditions can be achieved in comparison with conventional statistical analysis.
Inventors
- 👤 Yukio Kuroda
- 👤 Tomoyuki Yoshida
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-23 12:03
CPC Classifications
A; A61; A61B; A61B5/00; A61B5/0002; A61B5/0004; A61B5/0006; A61B5/24; A61B5/316; A61B5/369; A61B5/372; A61B5/374; A61B5/72; A61B5/7235; A61B5/7264; A61B5/7267; A61B5/16; A61B5/18; A61B5/7232; G; G16; G16H; G16H50/00; G16H50/70
Claims Analysis
Total Claims: 20
Claim 1
Independent Claim
98 words
An apparatus for analyzing a physiological fluctuation signal of a subject to determine at least one psychological condition of the subject, the apparatus comprising a neural network receiving the physiological fluctuation signal as an input for estimating physiological conditions using frequency variations, wherein said neural network is an hourglass type neural network, training of the hourglass type neural network carried out by supplying the physiological fluctuation signal to both an input and an output of the hourglass type neural network, output values of an intermediate layer of the hourglass type neural network representing at least one psychological condition.
Claim 2
Independent Claim
98 words
An apparatus for analyzing a physiological fluctuation signal of a subject to determine at least one psychological condition of the subject, the apparatus comprising a neural network receiving the physiological fluctuation signal as an input for estimating physiological conditions using frequency variations, wherein said neural network is an hourglass type neural network, training of the hourglass type neural network carried out by supplying the physiological fluctuation signal to both an input and an output of the hourglass type neural network, output values of an intermediate layer of the hourglass type neural network representing at least one psychological condition.
Claim 3
Dependent Claim
21 words
The apparatus according to claim 1, wherein said neural network receives a pre-processed EEG fluctuation signal as a physiological fluctuation signal.
Claim 4
Dependent Claim
30 words
The apparatus of claim 2, wherein said hourglass-type neural network outputs a signal indicative of an alertness of the subject as the at least one psychological condition of the subject.
Claim 5
Dependent Claim
31 words
The apparatus of claim 2, wherein said hourglass type neural network outputs a signal indicative of a comfortableness of the subject as the at least one psychological condition of the subject.
Claim 6
Dependent Claim
29 words
The apparatus of claim 1, further comprising signal sensing means, attachable to the subject, for sensing a fluctuating physiological condition of the subject and outputting the physiological fluctuation signal.
Claim 7
Dependent Claim
15 words
The apparatus of claim 1, wherein the physiological fluctuation signal is an EEG fluctuation signal.
Claim 8
Independent Claim
94 words
An apparatus for analyzing information relating to physiological and psychological conditions comprising a neural network receiving a preprocessed EEG fluctuation signal of a subject as an input for estimating physiological conditions of the subject, wherein said neural network comprises: a first neural network receiving the pre-processed EEG fluctuation signal as an input for estimating a degree of alertness; and a second neural network receiving the degree of alertness estimated by the first neural network and the pre-processed EEG fluctuation signal as inputs for estimating comfortableness, to estimate both alertness and comfortableness as psychological conditions.
Claim 9
Independent Claim
94 words
An apparatus for analyzing information relating to physiological and psychological conditions comprising a neural network receiving a preprocessed EEG fluctuation signal of a subject as an input for estimating physiological conditions of the subject, wherein said neural network comprises: a first neural network receiving the pre-processed EEG fluctuation signal as an input for estimating a degree of alertness; and a second neural network receiving the degree of alertness estimated by the first neural network and the pre-processed EEG fluctuation signal as inputs for estimating comfortableness, to estimate both alertness and comfortableness as psychological conditions.
Claim 10
Dependent Claim
54 words
The apparatus according to claim 7, wherein said first and second neural networks are hourglass type neural networks, the EEG fluctuation signals are supplied to both input and output of each of the neural networks for training the neural networks, and output of an intermediate layers of the neural network represents the psychological conditions.
Claim 11
Dependent Claim
35 words
The apparatus of claim 5, further comprising: signal sensing means, attachable to the subject for sensing an EEG fluctuation signal; and processing means for generating the preprocessed EEG fluctuation signal from the EEG fluctuation signal.
Claim 12
Independent Claim
81 words
A method for analyzing information relating physiological and psychological conditions, comprising: applying a physiological fluctuation signal to a neural network; and estimating psychological conditions using frequency variations; wherein said neural network is an hourglass type neural network, training of the hourglass type neural network carried out by supplying the physiological fluctuation signal to both an input and an output of the hourglass type neural network, output values of an intermediate layer of the hourglass type neural network representing the psychological conditions.
Claim 13
Independent Claim
81 words
A method for analyzing information relating physiological and psychological conditions, comprising: applying a physiological fluctuation signal to a neural network; and estimating psychological conditions using frequency variations; wherein said neural network is an hourglass type neural network, training of the hourglass type neural network carried out by supplying the physiological fluctuation signal to both an input and an output of the hourglass type neural network, output values of an intermediate layer of the hourglass type neural network representing the psychological conditions.
Claim 14
Dependent Claim
17 words
The method according to claim 10 wherein said physiological fluctuation signal includes a pre-processed EEG fluctuation signal.
Claim 15
Dependent Claim
12 words
The method according to claim 11, wherein said psychological conditions include alertness.
Claim 16
Dependent Claim
12 words
The method according to claim 11, wherein said psychological conditions include comfortableness.
Claim 17
Dependent Claim
15 words
The method of claim 6, wherein the physiological fluctuation signal is an EEG fluctuation signal.
Claim 18
Independent Claim
84 words
A method for analyzing information relating physiological and psychological conditions, comprising: applying a physiological fluctuation signal of a subject to a neural network, the physiological fluctuation signal including a pre-processed EEG fluctuation signal of the subject; applying the pre-processed EEG fluctuation signal to an alertness estimation portion of the neural network to estimate alertness; and applying the estimated alertness and the pre-processed EEG fluctuation signal to a comfortableness estimation portion of the neural network to estimate both alertness and comfortableness as the psychological conditions.
Claim 19
Independent Claim
84 words
A method for analyzing information relating physiological and psychological conditions, comprising: applying a physiological fluctuation signal of a subject to a neural network, the physiological fluctuation signal including a pre-processed EEG fluctuation signal of the subject; applying the pre-processed EEG fluctuation signal to an alertness estimation portion of the neural network to estimate alertness; and applying the estimated alertness and the pre-processed EEG fluctuation signal to a comfortableness estimation portion of the neural network to estimate both alertness and comfortableness as the psychological conditions.
Claim 20
Dependent Claim
54 words
The method according to claim 15, wherein the alertness estimation neural network and the comfortableness estimation neural network are hourglass type neural networks, both of which have been trained with the EEG fluctuation signals at both input and output, and the output of an intermediate layer of each neural network represents the psychological conditions.
References Cited (Backward Citations)
US4862359A
Cited
EP0551524A1
Cited
US5447166A
Cited
EP0555591A1
Cited
US5522863A
Cited
US5325862A
Cited
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2002-03-22 | 2002-09-21 | 2003-03-23 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2006-03-22 | 2006-09-21 | 2007-03-23 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2010-03-22 | 2010-09-21 | 2011-03-23 | $7700.00 | Window Closed / Lapsed |