Model Predictive Control of Shallow Drowsiness: Improving Productivity of Office Workers
For office workers and employers, this work addresses the problem of productivity loss due to drowsiness, but the results are preliminary with only six subjects over five days.
This paper proposes a model predictive control methodology that dynamically schedules air conditioning and lighting to reduce shallow drowsiness in office workers, achieving an 8.3% improvement in task processing speed without degrading comfort.
This paper proposes a methodology of model predictive control for alleviating shallow drowsiness of office workers and thus improving their productivity. The methodology is based on dynamically scheduling setting values for air conditioning and lighting to minimize drowsiness level of office workers on the basis of a prediction model that represents the relation between future drowsiness level and combination of indoor temperature and ambient illuminance. The prediction model can be identified by utilizing state-of-the-art drowsiness estimation method. The proposed methodology was evaluated in regard to a real routine task (performed by six subjects over five workdays), and the evaluation results demonstrate that the proposed methodology improved the processing speed of the task by 8.3% without degrading comfort of the workers.