Drowsiness onset detection
First Claim
1. A system for detecting the onset of drowsiness in an individual, comprising:
- one or more heart rate (HR) sensors which capture HR information of the individual over time, the HR information comprising heart rate variability (HRV) information;
one or more computing devices, said computing devices being in communication with each other whenever there is a plurality of computing devices, and a drowsiness onset detector computer program having a plurality of sub-programs executable by the one or more computing devices, the one or more computing devices being directed by the sub-programs of the drowsiness onset detector computer program to,receive the HR information from the heart rate sensor or sensors,extract a plurality of different HRV-related features from the HR information, said extracted different HRV-related features, which are among many features that can be extracted from HR information, having been determined to be specifically indicative of a transition from a wakeful state of an individual to a drowsy state of an individual,combine the extracted different HRV-related features to produce a drowsiness detection input,input the drowsiness detection input into an artificial neural network (ANN) classifier that has been trained to distinguish between said wakeful state of an individual and said drowsy state of an individual based on the extracted different HRV-related features,identify from an output of the ANN classifier if the drowsiness detection input indicates that the individual is exhibiting an onset of drowsiness, andwhenever the drowsiness detection input indicates that the individual is exhibiting an onset of drowsiness, initiate a drowsiness onset warning.
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Abstract
Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person'"'"'s heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual'"'"'s transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.
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Citations
20 Claims
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1. A system for detecting the onset of drowsiness in an individual, comprising:
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one or more heart rate (HR) sensors which capture HR information of the individual over time, the HR information comprising heart rate variability (HRV) information; one or more computing devices, said computing devices being in communication with each other whenever there is a plurality of computing devices, and a drowsiness onset detector computer program having a plurality of sub-programs executable by the one or more computing devices, the one or more computing devices being directed by the sub-programs of the drowsiness onset detector computer program to, receive the HR information from the heart rate sensor or sensors, extract a plurality of different HRV-related features from the HR information, said extracted different HRV-related features, which are among many features that can be extracted from HR information, having been determined to be specifically indicative of a transition from a wakeful state of an individual to a drowsy state of an individual, combine the extracted different HRV-related features to produce a drowsiness detection input, input the drowsiness detection input into an artificial neural network (ANN) classifier that has been trained to distinguish between said wakeful state of an individual and said drowsy state of an individual based on the extracted different HRV-related features, identify from an output of the ANN classifier if the drowsiness detection input indicates that the individual is exhibiting an onset of drowsiness, and whenever the drowsiness detection input indicates that the individual is exhibiting an onset of drowsiness, initiate a drowsiness onset warning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A system for detecting the onset of drowsiness in an individual, comprising:
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one or more computing devices, said computing devices being in communication with each other whenever there is a plurality of computing devices; and a drowsiness onset detector computer program having a plurality of sub-programs executable by the one or more computing devices, the one or more computing devices being directed by the sub-programs of the drowsiness onset detector computer program to, receive heart rate (HR) information of the individual over time, the HR information comprising heart rate variability (HRV) information, wherein the one or more computing devices are in communication over a data communication network with a remote computing device associated with one or more HR sensors which capture the HR information, and wherein the HR information is received from the remote computing device via the data communication network, extract a plurality of different HRV-related features from the HR information, said extracted different HRV-related features, which are among many features that can be extracted from HR information, having been determined to be specifically indicative of a transition from a wakeful state of an individual to a drowsy state of an individual, combine the extracted different HRV-related features to produce a drowsiness detection input, input the drowsiness detection input into an artificial neural network (ANN) classifier that has been trained to distinguish between said wakeful state of an individual and said drowsy state of an individual based on the extracted different HRV-related features, identify from an output of the ANN classifier if the drowsiness detection input indicates that the individual is exhibiting an onset of drowsiness, and whenever the drowsiness detection input indicates that the individual is exhibiting an onset of drowsiness, transmit a drowsiness onset notification.
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20. A drowsiness onset detection classifier training system, comprising:
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one or more computing devices, said computing devices being in communication with each other via a computer network whenever there is a plurality of computing devices; and a drowsiness onset detector training computer program having a plurality of sub-programs executable by the one or more computing devices, the one or more computing devices being directed by the sub-programs of the drowsiness onset detector training computer program to, receive the HR information output by one or more heart rate sensors for a plurality of individuals over time, as well as a drowsiness indicator for each of the individuals specifying whether that individual is in a wakeful state or in a drowsy state at the time the HR information was captured, the HR information comprising heart rate variability (HRV) information, for the HR information associated with each of the plurality of individuals, extract a plurality of different HRV-related features from the HR information, said extracted different HRV-related features, which are among many features that can be extracted from HR information, having been determined to be specifically indicative of a transition from a wakeful state of an individual to a drowsy state of an individual, and combine the extracted different HRV-related features to produce a drowsiness detection input, and train an artificial neural network (ANN) classifier to distinguish between said wakeful state of an individual and said drowsy state of an individual using said the drowsiness detection inputs and drowsiness indicator associated with each of the plurality of individuals.
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Specification