Method of and apparatus for evaluation and mitigation of microsleep events
First Claim
1. A method of detecting and indicating at least one occurrence of a microsleep event experienced by a subject, comprising the steps of:
- training a neural network to detect said occurrence, said neural network having an input for receiving data, correlated to the occurrence and non-occurrence of microsleep events, from at least one descriptive data source, and an output representing an alertness level result indicating whether a microsleep event has occurred; and
,applying a plurality of input feature vectors derived from data received from said descriptive data source to said neural network input, each of said input feature vectors includes data elements corresponding to at least one set of physiological, behavioral or performance data relating to at least one aspect of said subject'"'"'s alertness state, so as to produce an alertness level result at said output.
1 Assignment
0 Petitions
Accused Products
Abstract
A method and apparatus for determining, monitoring and predicting levels of alertness by detecting microsleep episodes includes a plurality of channel processing units and a channel combining unit. Each of the channel processing units receives an information channel which conveys information associated with the mental and behavorial state of the subject, such as for example an EEG channel, and classifies the information into a distinct category. Such categories may include microsleep, non-microsleep, one or more of a plurality of stages of sleep, one or more of a plurality of stages of wakefulness, or a transition state characterized by a transition from one of the aforementioned states to another. Each of the channel processing units includes a neural network which has been trained with a set of example input/result vector pairs. The example input/result vector pairs are generated by correlating actual information channel outputs with observed fatigue related events such as nodding off, head snapping, multiple blinks, blank stares, wide eyes, yawning, partial and complete prolonged eyelid closures, and slow rolling eye movements.
242 Citations
42 Claims
-
1. A method of detecting and indicating at least one occurrence of a microsleep event experienced by a subject, comprising the steps of:
-
training a neural network to detect said occurrence, said neural network having an input for receiving data, correlated to the occurrence and non-occurrence of microsleep events, from at least one descriptive data source, and an output representing an alertness level result indicating whether a microsleep event has occurred; and
,applying a plurality of input feature vectors derived from data received from said descriptive data source to said neural network input, each of said input feature vectors includes data elements corresponding to at least one set of physiological, behavioral or performance data relating to at least one aspect of said subject'"'"'s alertness state, so as to produce an alertness level result at said output. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
-
-
13. An apparatus for detecting and indicating at least one occurrence of a microsleep event experienced by a subject, comprising:
-
a neural network trained to detect said occurrence, said neural network having an input for receiving data correlated to the occurrence and non-occurrence of microsleep events, from at least one descriptive data source, and an output representing an alertness level result indicating whether a microsleep event has occurred; and
,means for receiving a plurality of input feature vectors derived from data received from said descriptive data source to said neural network input, wherein each of said input feature vectors includes data elements corresponding to at least one set of physiological, behavioral or performance criteria relating to at least one aspect of said subject'"'"'s alertness state, so as to produce an alertness level result at said output. - View Dependent Claims (14, 15, 16, 17, 18, 19)
-
-
20. An apparatus for automatically detecting microsleep events, comprising:
-
A. a data recording system for receiving at least one source of descriptive data related to a subject and for storing said data; B. a feature extraction system for receiving said descriptive data stored by said recording system and generating processed data by producing a low dimensional representation of at least one fatigue-related data characteristic of said descriptive data; C. a detection system including a plurality of neural networks trained by said data base of fatigue events and said database of non-fatigue events, each of said plurality of neural networks receiving one of said sources of said descriptive data and classifying said descriptive data into one of a plurality of fatigue events and non-fatigue events so as to produce a classified result; and D. a contextual system for receiving said first classified result from each of said neural networks, and for applying a context interpretation algorithm to said classified results so as to produce an alertness level result related to said subject. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42)
-
Specification