基于生理特征的学术环境焦虑与压力识别系统研究

Towards an anxiety and stress recognition system for academic environments based on physiological features.

机构信息

Facultad de Ingeniería, Universidad Autónoma del Estado de México, Toluca, Mexico.

出版信息

Comput Methods Programs Biomed. 2020 Jul;190:105408. doi: 10.1016/j.cmpb.2020.105408. Epub 2020 Mar 2.

DOI:10.1016/j.cmpb.2020.105408

PMID:32139112

Abstract

BACKGROUND AND OBJECTIVE

Traditional methods to determine stress and anxiety in academic environments consist of the application of questionnaires, but the main disadvantage is that the results depend on the students' self-perception. Being able to detect anxiety-related stress levels in a simple and objective way contributes greatly to dealing with low performance and school drop-out by students.

METHODS

The main contribution of this study is to identify the physiological features that could be used as predictors of stressful activities and states of anxiety in academic environments using an Arduino board and low-cost sensors. A test with 21 students was conducted, and a stress-inducing protocol was proposed and 21 physiological features of five signals were analyzed. In addition, the State-Trait Anxiety Inventory (STAI) was used to assess the level of anxiety for each student. Four classifiers were compared to find the physiological feature subset that provides the best accuracy to identify states of stress and anxiety.

RESULTS

The stress due to activities performed by students can be identified with an accuracy greater than 90% (Kappa = 0.84) using the k-Nearest Neighbors classifier, using data from heart rate, skin temperature and oximetry signals and four physiological features. Meanwhile, the identification of anxiety was achieved with an accuracy greater than 95% (Kappa = 0.90) using the SVM classifier with data from the galvanic skin response (GSR) signal and three physiological features.

CONCLUSIONS

The results provide a clue that anxiety detection in academic environments could be done using the analysis of physiological signals instead of STAI test scores. Besides, the results suggest that physiological features could be used to develop stress recognition systems to help teachers to identify the stressful tasks in an academic environment or to develop anxiety recognition systems to help students to control their level of anxiety when they are performing either academic tasks or exams.

摘要

背景与目的

传统的学术环境下评估压力和焦虑的方法包括问卷调查，但主要缺点是结果取决于学生的自我感知。能够以简单和客观的方式检测与焦虑相关的压力水平，对于处理学生表现不佳和辍学问题有很大帮助。

方法

本研究的主要贡献是使用 Arduino 板和低成本传感器，确定可用于识别学术环境中应激活动和焦虑状态的生理特征。对 21 名学生进行了测试，提出了一个应激诱导协议，并分析了五个信号的 21 个生理特征。此外，还使用状态-特质焦虑量表（STAI）评估了每个学生的焦虑水平。比较了四种分类器，以找到提供最佳准确性以识别应激和焦虑状态的生理特征子集。

结果

使用 K-最近邻分类器（Kappa=0.84），可以使用心率、皮肤温度和血氧饱和度信号以及四个生理特征，以大于 90%的准确率识别学生活动引起的应激（Kappa=0.84）。同时，使用支持向量机分类器（Kappa=0.90），可以使用皮肤电反应（GSR）信号和三个生理特征，以大于 95%的准确率识别焦虑（Kappa=0.90）。