Monitoring of mental workload levels during an everyday life office-work scenario

Personal and ubiquitous 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 quantification of mental load, different load levels that occur during a workday have to be discriminated. In this work, we present how mental workload levels in everyday life scenarios can be discriminated with data from a mobile ECG logger by incorporating individual calibration measures. We present an experiment design to induce three different levels of mental workload in calibration sessions and to monitor mental workload levels in everyday life scenarios of seven healthy male subjects. Besides the recording of ECG data, we collect subjective ratings of the perceived workload with the NASA Task Load Index (TLX), whereas objective measures are assessed by collecting salivary cortisol. According to the subjective ratings, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation can be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous system activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous system activity or predominance, show an increase in their values with increased workload. We employ multiple regression analysis to model the relationship between relevant HRV features and the subjective ratings of NASA-TLX in order to predict the mental workload levels during office-work. The resulting predictions were correct for six out of the seven subjects. In addition, we compare the performance of three classification methods to identify the mental workload level during office-work. The best results were obtained with linear discriminant analysis (LDA) that yielded a correct classification for six out of the seven subjects. The k-nearest neighbor algorithm (k-NN) and the support vector machine (SVM) resulted in a correct classification of the mental workload level during office-work for five out of the seven subject

Monitoring of mental workload levels

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

Towards continuous monitoring of mental workload

Continuous monitoring of mental workload offers new opportunities to support preventing mental disorders and maintaining mental health. In order to achieve a quantification of mental workload, different load levels have to be discriminated. This work goes towards continuous monitoring of mental workload in daily life: we present our experimental designs and the achieved results in discriminating three levels of mental workload in a laboratory simulation and in daily life. In the lab setting we achieved an average recognition rate of 82.14% for all 7 subjects under study. In daily life two subjects were monitored during low, medium and high workload days. We achieved average accuracy rates of 72.03% and 77.66% for both subjects respectively. Finally we have investigated whether the data from the lab experiment are suitable to discriminate low, medium and high workload days

Monitoring of mental workload levels during an everyday life office-work scenario

Personal and ubiquitous healthcare applications offer new opportunities to prevent long-term health dam- age 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 quantification of mental load, different load levels that occur during a workday have to be discrimi- nated. In this work, we present how mental workload levels in everyday life scenarios can be discriminated with data from a mobile ECG logger by incorporating individual calibration measures. We present an experiment design to induce three different levels of mental workload in cali- bration sessions and to monitor mental workload levels in everyday life scenarios of seven healthy male subjects. Besides the recording of ECG data, we collect subjective ratings of the perceived workload with the NASA Task Load Index (TLX), whereas objective measures are asses- sed by collecting salivary cortisol. According to the sub- jective ratings, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation can be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous system activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous system activity or predominance, show an increase in their values with increased workload. We employ multiple regression analysis to model the relationship between relevant HRV features and th