The congruence between affective experiences and physiological changes has
been a debated topic for centuries. Recent technological advances in
measurement and data analysis provide hope to solve this epic challenge. Open
science and open data practices, together with data analysis challenges open to
the academic community, are also promising tools for solving this problem. In
this entry to the Emotion Physiology and Experience Collaboration (EPiC)
challenge, we propose a data analysis solution that combines theoretical
assumptions with data-driven methodologies. We used feature engineering and
ensemble selection. Each predictor was trained on subsets of the training data
that would maximize the information available for training. Late fusion was
used with an averaging step. We chose to average considering a ``wisdom of
crowds'' strategy. This strategy yielded an overall RMSE of 1.19 in the test
set. Future work should carefully explore if our assumptions are correct and
the potential of weighted fusion.

测量和数据分析等最新技术的应用为解决情感体验和生理变化之间的相似性问题提供了希望，开放科学和开放数据实践以及面向学术界的数据分析挑战也是解决这一问题的有希望的工具。

基于集成学习的评估情感体验和生理变化动态

Ensemble Learning to Assess Dynamics of Affective Experience Ratings and  Physiological Change

Facial landmark tracking for thermal images requires tracking certain
important regions of subjects' faces, using images from thermal images, which
omit lighting and shading, but show the temperatures of their subjects. The
fluctuations of heat in particular places reflect physiological changes like
bloodflow and perspiration, which can be used to remotely gauge things like
anxiety and excitement. Past work in this domain has been limited to only a
very limited set of architectures and techniques. This work goes further by
trying a comprehensive suit of various models with different components, such
as residual connections, channel and feature-wise attention, as well as the
practice of ensembling components of the network to work in parallel. The best
model integrated convolutional and residual layers followed by a channel-wise
self-attention layer, requiring less than 100K parameters.

通过使用来自红外图像的热图像，利用人脸的重要区域进行面部标志跟踪，忽略了光照和阴影，但显示了主体的温度，通过特定位置上的热量波动反映生理变化，如血液流动和出汗，用于远程判断焦虑和兴奋。本研究进一步尝试了各种模型，包括残差连接、通道和特征关注以及网络组件的并行运作，最佳模型整合了卷积和残差层以及通道自注意层，只需要不到 100,000 个参数。

热力面部标记追踪

The Heat is On: Thermal Facial Landmark Tracking

This paper presents a comprehensive review of methods covering significant
subjective and objective human stress detection techniques available in the
literature. The methods for measuring human stress responses could include
subjective questionnaires (developed by psychologists) and objective markers
observed using data from wearable and non-wearable sensors. In particular,
wearable sensor-based methods commonly use data from electroencephalography,
electrocardiogram, galvanic skin response, electromyography, electrodermal
activity, heart rate, heart rate variability, and photoplethysmography both
individually and in multimodal fusion strategies. Whereas, methods based on
non-wearable sensors include strategies such as analyzing pupil dilation and
speech, smartphone data, eye movement, body posture, and thermal imaging.
Whenever a stressful situation is encountered by an individual, physiological,
physical, or behavioral changes are induced which help in coping with the
challenge at hand. A wide range of studies has attempted to establish a
relationship between these stressful situations and the response of human
beings by using different kinds of psychological, physiological, physical, and
behavioral measures. Inspired by the lack of availability of a definitive
verdict about the relationship of human stress with these different kinds of
markers, a detailed survey about human stress detection methods is conducted in
this paper. In particular, we explore how stress detection methods can benefit
from artificial intelligence utilizing relevant data from various sources. This
review will prove to be a reference document that would provide guidelines for
future research enabling effective detection of human stress conditions.

本研究综合评估了文献中存在的重要主观和客观人体应激检测技术。特别是，使用可穿戴传感器和非穿戴传感器获得数据的心理、生理、物理和行为测量方法被详细研究，从而为未来的研究提供参考建议，利用人工智能技术实现有效检测人类应激状态。