Model Predictive Control of Shallow Drowsiness: Improving Productivity of Office Workers

TL;DR

This paper introduces a model predictive control approach to reduce office workers' drowsiness by optimizing indoor temperature and lighting, leading to an 8.3% increase in task processing speed without comfort loss.

Contribution

It presents a novel predictive control methodology that dynamically adjusts environmental settings to mitigate drowsiness based on a new prediction model.

Findings

Improved task processing speed by 8.3%.

Effective reduction of drowsiness levels.

Maintained comfort while optimizing productivity.

Abstract

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.