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This paper proposes a multi-modal sensor fusion algorithm for the estimation of driver drowsiness. Driver
sleepiness is believed to be responsible for more than 30% of passenger car accidents and for 4% of all accident
fatalities. In commercial vehicles, drowsiness is blamed for 58% of single truck accidents and 31% of commercial
truck driver fatalities. This work proposes an innovative automatic sleep-onset detection system. Using multiple
sensors, the driver’s body is studied as a mechanical structure of springs and dampeners. The sleep-detection
system consists of highly sensitive triple-axial accelerometers to monitor the driver’s upper body in 3-D. The
subject is modeled as a linear time-variant (LTV) system. An LMS adaptive filter estimation algorithm generates
the transfer function (i.e. weight coefficients) for this LTV system. Separate coefficients are generated for the
awake and asleep states of the subject. These coefficients are then used to train a neural network. Once trained, the
neural network classifies the condition of the driver as either awake or asleep. The system has been tested on a
total of 8 subjects. The tests were conducted on sleep-deprived individuals for the sleep state and on fully awake
individuals for the awake state. When trained and tested on the same subject, the system detected sleep and
awake states of the driver with a success rate of 95%. When the system was trained on three subjects and then retested
on a fourth “unseen” subject, the classification rate dropped to 90%. Furthermore, it was attempted to
correlate driver posture and sleepiness by observing how car vibrations propagate through a person’s body. Eight
additional subjects were studied for this purpose. The results obtained in this experiment proved inconclusive
which was attributed to significant differences in the individual habitual postures.
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