SmartCar: Detecting Driver Stress
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Warm or Cool, Large or Small? The Challenge of Thermal Displays
IEEE Transactions on Haptics
Active affective State detection and user assistance with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
Stress is a key indicator of wellness in human beings and a prime contributor to performance degradation and errors during various human tasks. The overriding purpose of this paper is to propose two algorithms (probabilistic and non-probabilistic) that iteratively track stress states to compute a wellness index in terms of the stress levels. This paper adopts the physiological view-point that high stress is accompanied with large deviations in biometrics such as body temperature, heart rate, etc., and the proposed algorithms iteratively track these fluctuations to compute a personalized wellness index that is correlated to the engagement levels of the tasks performed by the user. In essence, this paper presents a quantitative relationship between temperature, occupational stress, and wellness during different tasks. The simplicity of the statistical inference algorithms make them favorable candidates for implementation on mobile platforms such as smart phones in the future, thereby providing users an inexpensive application for self-wellness monitoring for a healthier lifestyle.