A software system will be developed as a practical tool for accurately predicting alertness levels at work to help design bio-compatible work schedules and time the effective use of fatigue countermeasures. This software system can potentially benefit health and safety of a large portion of the working society. The core algorithm structure considers circadian and homeostatic aspects of sleep and alertness as well as time-on-task effects. The software system will use as input sleep and/or work patterns and individual characteristics (morningness/eveningness, habitual wake-up time) to generate a continuous alertness curve and statistical measures for work schedule evaluation. When the actual sleep pattern is not known, sleep will be predicted based on the work pattern. The algorithms of the modules for sleep and alertness prediction will be refined by optimization methods using error minimization techniques. The optimization process will use large training data sets on sleep and alertness patterns of shiftworkers with regular work schedules (to be collected during this project) and of workers with irregular work schedules (to be drawn from Circadian Technologies' extensive database). The predictive capability of the software system will be evaluated, using goodness-of-fit measures, by comparisons with Phase-I algorithms and by cross validations (using independent test data sets).
The progressive transition into a 24-hour society creates a large market for a software system for predicting alertness at work. This software system would be an attractive tool to design bio-compatible work schedules or to assess worker fatigue in accident investigations. The software system can also be used for worker training and public education regarding chronobiological and homeostatic aspects of sleep and sleepiness, and it can help increase awareness about the practical implications of fatigue at work.
| Moore-Ede, Martin; Heitmann, Anneke; Guttkuhn, Rainer et al. (2004) Circadian alertness simulator for fatigue risk assessment in transportation: application to reduce frequency and severity of truck accidents. Aviat Space Environ Med 75:A107-18 |
