A grey-box identification approach for a human alertness model

Abstract

Many notorious disasters in the last few decades may have been correlated with fatigue or human error. Detecting the level of fatigue from a person, in order to monitor and predict possible risk situations, has become a major concern. A person alertness model is used to produce data in a realistic manner, similarly to a Karolinska Sleepiness Scale self-valuation or Psychomotor Vigilance Test, by considering white measurement noise and a non-uniform sampling rate that provides small data amounts during the day, with no data collected during sleep. An identification grey-box algorithm based upon several windows of data is developed to retrieve the real biological parameters of a person's alertness model. The alertness parametric model that describes both awake and sleep periods is non-linear, so the problem is solved by splitting the model into linear representations, one for awake and another for sleep periods. The first is solved by representing the parametric model in a canonical state-space form that leads to a straightforward least-squares estimation problem. Due to the lack of data during sleep periods, the second is addressed with a non-linear least squares algorithm. The performance of the proposed algorithm is evaluated by analyzing the ability to recover the stipulated biological parameters. © 2019 IEEE.