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.

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.

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.

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.