Electrodermal activity (EDA) is considered a standard marker of sympathetic
activity. However, traditional EDA measurement requires electrodes in steady
contact with the skin. Can sympathetic arousal be measured using only an
optical sensor, such as an RGB camera? This paper presents a novel approach to
infer sympathetic arousal by measuring the peripheral blood flow on the face or
hand optically. We contribute a self-recorded dataset of 21 participants,
comprising synchronized videos of participants' faces and palms and
gold-standard EDA and photoplethysmography (PPG) signals. Our results show that
we can measure peripheral sympathetic responses that closely correlate with the
ground truth EDA. We obtain median correlations of 0.57 to 0.63 between our
inferred signals and the ground truth EDA using only videos of the
participants' palms or foreheads or PPG signals from the foreheads or fingers.
We also show that sympathetic arousal is best inferred from the forehead,
finger, or palm.

通过光学传感器测量面部或手部的外周血流来推断交感兴奋是一种新颖的方法，并且该方法与基准的皮肤电活动（EDA）密切相关，其中前额、手指或手掌是推断交感兴奋的最佳区域。

通过外周血流估计基于视频的共情激动评估

Video-based sympathetic arousal assessment via peripheral blood flow  estimation

We present MDEAW, a multimodal database consisting of Electrodermal Activity
(EDA) and Photoplethysmography (PPG) signals recorded during the exams for the
course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order
to elicit the emotional reactions to the students in a classroom scenario.
Signals from 10 students were recorded along with the students' self-assessment
of their affective state after each stimulus, in terms of 6 basic emotion
states. All the signals were captured using portable, wearable, wireless,
low-cost, and off-the-shelf equipment that has the potential to allow the use
of affective computing methods in everyday applications. A baseline for
student-wise affect recognition using EDA and PPG-based features, as well as
their fusion, was established through ReMECS, Fed-ReMECS, and Fed-ReMECS-U.
These results indicate the prospects of using low-cost devices for affective
state recognition applications. The proposed database will be made publicly
available in order to allow researchers to achieve a more thorough evaluation
of the suitability of these capturing devices for emotion state recognition
applications.

使用 MDEAW 多模式数据库记录了学生在课堂上的情绪反应，结果表明了便携、低成本的设备的应用潜力，并建立了学生情感识别的基础。

基于无线可穿戴低成本设备 EDA 和 PPG 信号的情感分析的多模态数据集 MDEAW

MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG signals from wireless wearable low-cost off-the-shelf Devices

Considerable attention has been paid for physiological signal-based emotion
recognition in field of affective computing. For the reliability and user
friendly acquisition, Electrodermal Activity (EDA) has great advantage in
practical applications. However, the EDA-based emotion recognition with
hundreds of subjects still lacks effective solution. In this paper, our work
makes an attempt to fuse the subject individual EDA features and the external
evoked music features. And we propose an end-to-end multimodal framework, the
1-dimensional residual temporal and channel attention network (RTCAN-1D). For
EDA features, the novel convex optimization-based EDA (CvxEDA) method is
applied to decompose EDA signals into pahsic and tonic signals for mining the
dynamic and steady features. The channel-temporal attention mechanism for
EDA-based emotion recognition is firstly involved to improve the temporal- and
channel-wise representation. For music features, we process the music signal
with the open source toolkit openSMILE to obtain external feature vectors. The
individual emotion features from EDA signals and external emotion benchmarks
from music are fused in the classifing layers. We have conducted systematic
comparisons on three multimodal datasets (PMEmo, DEAP, AMIGOS) for 2-classes
valance/arousal emotion recognition. Our proposed RTCAN-1D outperforms the
existing state-of-the-art models, which also validate that our work provides an
reliable and efficient solution for large scale emotion recognition. Our code
has been released at this https URL

本研究采用凸优化的 EDA 方法将情绪识别中所需的动态和稳态特征分离，提出了一种融合了个体 EDA 特征和外部音乐特征的一维残余时间和通道注意力网络的多模态框架，证明其比现有模型更有效。