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

Abstract

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.

Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study

“…A statistically significant difference was observed for the features SDNN and RMSSD. Cinaz et al (2013) demonstrated that the features RMSSD and pNN50 decrease significantly with increased MWL. In contrast, a statistically significant difference was observed for HR, LF/HF ratio, and EMG amplitude.…”

“…Increased demand on available resources can cause human operator overload (Dadashi et al, 2013). In such cases, the operator experiences an extensive level of MWL in his or her daily work without enough time to rest, and health problems such as chronic stress, depression, or burnout can occur (Cinaz et al, 2013), which can influence the worker's performance and well-being (Johnson and Widyanti, 2011). Van Daalen et al (2009) indicated that a mismatch between task demand and the capabilities of the worker can cause work-related stress.…”

“…Usually, operators managing complex work environments are exposed to different stressors, which can present risks to safety, performance, and well-being, as well as pose long-term consequences for their health (Saueret al 2013). Thus it is important to evaluate MWL in real working conditions to understand how to prevent mental disorders and maintain mental health; however, most research has evaluated different levels of MWL in laboratory conditions (Cinaz et al, 2013). The ECG and EMG measurements can be carried out during real work tasks, at least in jobs where most work is performed at a computer in an office-like environment.…”

Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

“…By comparing within sessions, dfa2 and RPadet were found significantly different between session 1 and 4, while ShanEn between session 3 and 4. Previous studies [15][16][17], showed that as one task's demand increases, HRV becomes more depressed. Added to this, here we found that also on repetitive tasks, there is a depressed HRV over sessions, which is possibly due to an increase in mental workload.…”

Course Scheduling According to Student Stress

“…Mental fatigue can be reflected in physiological features, such as heart rate variability (HRV) and galvanic skin response (GSR). Cinaz et al [4] measured HRV features to quantify the mental workload levels during office-work. Besides, additional subjective difficulty levels were rated by NASA task load index (TLX).…”