Reference: Troy Steele, Tim Cutmore, Daniel A James, Andry Rakotonirainy, An Investigation into Peripheral Physiological Markers that Predict Monotony, 2004 Road Safety Research, Policing and Education Conference, 14-16 November 2004, Perth, WA.
Abstract: Behavioural performance gradually declines when individuals perform monotonous tasks. The road safety research community tends to agree that driver monotony alters driving performance, especially for professional drivers. Monotony is a well known but poorly understood driver state that can lead to road crashes. With sophisticated instrumentation it is possible to determine if a driver or test subject is likely to be in these or similar states. This paper investigates the state of monotony to determine if relatively simple instrumentation can measure wether or not a driver is likely to be in or approaching this state. First the Mackworth clock test, a well known protocol for producing a monotonous state is reproduced in a laboratory based experiment. A number of physiological indicators and Electroencephalograph (EEG) are monitored during the experiment. The stimuli (event) contains 24 different targets the participant reacts to during a 1-hour testing session. The state of monotony is verified by continuous EEG recording, a baseline is determined by examining pre and post event activity. A range of possible peripheral measures are then also simultaneously recorded such as Galvanic Skin Response (GSR), Electrocardiogram (EKG), Electrooculograph (EOG), Electromyograph EMG and 3D head tilt (using a custom built accelerometer based device) together with user inputs. Preliminary results from a test pool of eight subjects indicates that some of these peripheral physiological measures may contain markers that correlate well with alpha and theta EEG activity, thus indicating the state of monotony. A sensor that shows a lot of promise for future research is the GSR. Inexpensive instrumentation and an in-car based test procedure are recommended for further investigation in order to develop a sustainable Monotony Diagnosis Module (MDM).