Mobile Heart Health: Project Highlight
IEEE Pervasive Computing
Heart on the road: HRV analysis for monitoring a driver's affective state
Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications
What does your chair know about your stress level?
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Discriminating stress from cognitive load using a wearable EDA device
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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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 subjects.