Emotion and stress recognition related sensors and machine learning technologies
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Date
2021
Authors
Journal Title
Journal ISSN
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Publisher
MPDI
Abstract
Intelligent sociotechnical systems are gaining momentum in today’s informationrich
society, where different technologies are used to collect data from such systems and
mine this data to make useful insights about our daily activities. These systems range
from driver-assistance systems, to medical-patient monitoring systems, to emotion-aware
intelligent systems, to complex collaborative robotics systems. They are built around
(i) intrusive technologies such as physiological sensors, used for example in EEG, ECG,
electrodermal activity and skin conductance and (ii) nonintrusive technologies that use
piezo-vibration sensors, facial images, chairborne differential vibration sensors and bedborne
differential vibration sensors. However, despite their undisputable advantages in
our daily lives, there are a number of issues relating to the design and development of such
systems, as they rely on emotion and stress classification from physiological signals. These
issues can be viewed from various perspectives including: (a) quality and reliability of
sensor data; (b) classification performance in terms of accuracy, precision, specificity, recall
and F1-measure; (c) robustness of subject-independent recognition; (d) portability of the
classification systems to different environments and (e) the estimation of the emotional
state for dynamic systems.
Description
Keywords
Machine learning technologies, Sensors, Stress recognition, Driver-assistance systems, Medical-patient monitoring systems
Citation
Bagula, A. et al. (2021). Emotion and stress recognition related sensors and machine learning technologies. Sensors ,21(7),2273