Mobile healthcare applications offer new opportunities to prevent long-term health damage due to increased mental
workload by continuously monitoring physiological signs related to prolonged high workload and providing just-in-time feedback. In order to achieve a day-by-day quantification of mental load, first different load levels which occur during a workday have to be discriminated. This work goes one step towards this goal: we present our experiment design and preliminary results in discriminating different levels of mental workload based on heart rate features obtained from a mobile ECG system. Based on the subjective ratings of the participants under study, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under
investigation could be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous activity, show an increase of their values with increased workload. These results provide evidence that a mobile ECG system is suited to discriminate different levels of mental workload. This would enable the development of mobile applications to monitor mental workload levels and to prevent long-term damage by giving early warning signs in case of prolonged high workload
