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Drowsiness Prediction Based on an iToF Sensor for iCM Applications

Event: InCabin Phoenix
| Published: 28th March 2023
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Hear from:

Brenda Meza García
Innovation Leader,

Nextium by IDNEO

Nowadays most drowsiness detection systems are based on visible drowsiness manifestations e.g., yawning, closing eyelids, etc. and therefore have a very limited time window to act. We present here a system based on breathing that detects drowsiness several minutes before any visible drowsiness manifestation appears. Breathing is captured through a 3D iToF sensor in the interior of the vehicle.

The sensor measures the movements in the Z-axis of the driver’s chest corresponding to the breathing signal. Thoracic Effort Drowsiness Detection algorithm (TEDD) analyzes this signal to predict several minutes in advance if the driver will become impaired to drive due to drowsiness.

The proposed DMS shows promising results regarding the breathing signal extraction. When compared to a chest band we obtain a Pearson’s Correlation Coefficient (PCC) of 0.98. Regarding drowsiness prediction we obtain the exact same results for both systems.

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