Person will normally experience mental distress when they are doing activities like working, studying or even browsing the internet. In addition, everybody have different behavior to cope with stress, and such kind of behavior could be monitored from biosignal generated by human body. In view of fact, identifying mental workload pattern of persons who's were doing calculation task and browsing the internet were investigated by measuring heart rate signal. The two experiments, i.e., doing calculation tasks and browsing the internet, were conducted to understand mental workload behavior of students. During the experiments, the heart activities of each participant were measured using ECG sensors. Furthermore, the heart rate variability (HRV) features, i.e., the ratio of the low-high frequency band (LF/HF), which was derived from ECG signal were extracted to obtain mental workload pattern using Symbolic Aggregate Approximation (SAX) and gray level co-occurrence matrices (GLCM). The cluster analysis was then employed to analyze pattern of mental workload when persons were doing calculation task and browsing the internet. The results showed several behavior patterns which indicating persons when they coped with stress and fatigue.

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