Detection of Driver Workload Using Wrist-Worn Wearable Sensors: A Feasibility Study

Abstract

In recent years, driver’s delayed recognition has caused many traffic accidents. Cognitive workload decreases awareness and delays the driver’s attention on the surrounding environment. Conventionally, the degree of cognitive workload on a driver, namely, the driving workload, is estimated from the steering pattern of the steering wheel. Direct measurements of the hand motions operating the vehicle might more easily and accurately detect the small changes caused by driving workload than conventional methods. Therefore, we investigate the effect of cognitive workload on the steering operation and hand motions of drivers, and verify the applicability of our approach to driving-workload estimation. The hand motions refers to the behavior of the hands operating the steering wheel. From the acceleration of the hands, we derive an index of the driving workload. The proposed method was experimentally evaluated on seven participants performing a dual task. The estimation accuracy of the proposed method at least matched that of the conventional steering-entropy method.