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Technology development in wearable sensors and biosignal processing has made it possible to detect human stress from the physiological features. However, the intersubject difference in stress responses presents a major challenge for reliable and accurate stress estimation. This research proposes a novel cluster-based analysis method to measure perceived stress using physiological signals, which accounts for the intersubject differences. The physiological data are collected when human subjects undergo a series of task-rest cycles, incurring varying levels of stress that is indicated by an index of the State Trait Anxiety Inventory. Next, a quantitative measurement of stress is developed by analyzing the physiological features in two steps: 1) a k -means clustering process to divide subjects into different categories (clusters), and 2) cluster-wise stress evaluation using the general regression neural network. Experimental results show a significant improvement in evaluation accuracy as compared to traditional methods without clustering. The proposed method is useful in developing intelligent, personalized products for human stress management.

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In this paper, a novel system for detection of human stress and emotion in speech is proposed. The system makes use of FFT based linear short time Log Frequency Power Coefficients (LFPC) and TEO based nonlinear LFPC features in both time and frequency domains. The performance of the proposed system is compared with the traditional approaches which use features of LPCC and MFCC. The comparison of each approach is performed using SUSAS (Speech Under Simulated and Actual Stress) and ESMBS (Emotional Speech of Mandarin and Burmese Speakers) databases. It is observed that proposed system outperforms the traditional systems. Results show that, the system using LFPC gives the highest accuracy (87.8% for stress, 89.2% for emotion classification) followed by the system using NFD-LFPC feature. While the system using NTD-LFPC feature gives the lowest accuracy.

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