Physiological measurements involves observing variables that attribute to the
normative functioning of human systems and subsystems directly or indirectly.
The measurements can be used to detect affective states of a person with aims
such as improving human-computer interactions. There are several methods of
collecting physiological data, but wearable sensors are a common, non-invasive
tool for accurate readings. However, valuable information is hard to extract
from the raw physiological data, especially for affective state detection.
Machine Learning techniques are used to detect the affective state of a person
through labeled physiological data. A clear problem with using labeled data is
creating accurate labels. An expert is needed to analyze a form of recording of
participants and mark sections with different states such as stress and calm.
While expensive, this method delivers a complete dataset with labeled data that
can be used in any number of supervised algorithms. An interesting question
arises from the expensive labeling: how can we reduce the cost while
maintaining high accuracy? Semi-Supervised learning (SSL) is a potential
solution to this problem. These algorithms allow for machine learning models to
be trained with only a small subset of labeled data (unlike unsupervised which
use no labels). They provide a way of avoiding expensive labeling. This paper
compares a fully supervised algorithm to a SSL on the public WESAD (Wearable
Stress and Affect Detection) Dataset for stress detection. This paper shows
that Semi-Supervised algorithms are a viable method for inexpensive affective
state detection systems with accurate results.

本文研究了使用可穿戴传感器收集生理数据来探测情感状态的方法，指出了使用机器学习算法对已标记的生理数据进行情感状态检测的方法存在的问题，提出了使用半监督学习算法来减少标记成本的解决方案，并通过在公开数据集 WESAD 进行实验比较了半监督算法和全监督算法的性能，结果表明半监督算法是一种价格低廉且具有准确性的检测情感状态的有效方法。