Drowsiness onset detection

Drowsiness onset detection implementations 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...

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Bibliographische Detailangaben
Hauptverfasser:	Kannan, Aadharsh, Ramaswamy, Govind, Bhaskar, Srinivas, Gujjar, Avinash
Format:	Patent
Sprache:	eng
Schlagworte:	ALARM SYSTEMS ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS INVEHICLES ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OFTRANSMISSIONS IN VEHICLES ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GASEXHAUST OR FUEL SUPPLY OF PROPULSION UNITS, IN VEHICLES AUXILIARY DRIVES FOR VEHICLES CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES DIAGNOSIS HUMAN NECESSITIES HYGIENE IDENTIFICATION INSTRUMENTATION OR DASHBOARDS FOR VEHICLES MEDICAL OR VETERINARY SCIENCE ORDER TELEGRAPHS PERFORMING OPERATIONS PHYSICS ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT SIGNALLING SIGNALLING OR CALLING SYSTEMS SURGERY TRANSPORTING VEHICLES IN GENERAL
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Beschreibung
Zusammenfassung:	Drowsiness onset detection implementations 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. Using Discrete Fourier Transform and DiscreteWavelet Transform, 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.