A grey–box identification approach for a human alertness model

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.