一个基于物联网的智能情绪识别系统,通过使用身体内部参数来识别情绪
Tayyaba Rashid1, Imran Sarwar Bajwa2, Jungsuk Kim3
1The Islamia University of Bahawalpur, Bahawalpur, Pakistan. Tayyaba.Rashid@iub.edu.pk.
Scientific reports
|February 4, 2026
概括
这项研究证明了通过物联网 (IoT) 框架使用生理信号进行有效的情绪识别. 随机森林算法实现了高精度,验证了实时情绪检测的新方法.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 使用生理信号识别情绪对于心理健康监测和压力管理至关重要.
- 现有的方法往往缺乏实时功能或依赖于侵入性技术.
- 物联网 (IoT) 的整合为非侵入性,实时数据采集提供了一个有希望的途径.
研究的目的:
- 开发和验证一个新的框架来识别六种情绪状态 (中性,快乐,悲伤,恐惧,愤怒,惊喜) 使用生理信号.
- 评估各种机器学习算法对情绪分类的性能.
- 通过内部和外部验证来评估拟议系统的稳定性和通用性.
主要方法:
- 实时生理数据 (血压,氧和,血糖,心率,体温) 采集使用物联网 (IoT) 启用框架.
- 通过实验评估了11种不同的机器学习分类算法.
- 通过k-fold交叉检查进行内部验证,并使用DEAP情感任务进行外部验证.
主要成果:
- 随机森林算法以90.56%的准确性和93.34%的F1得分表现出卓越的性能.
- 通过k倍交叉检查进行内部验证,精度提高到93.18%.
- 使用DEAP任务的外部验证实现了约94%的集体准确度,在所有情感类别中获得了高精度,回忆和F1分数.
结论:
- 生理信号,加上物联网和机器学习,为实时情绪识别提供了有效和准确的框架.
- 设计的方法是稳健的,并且很好地概括,验证了所选生理特征的有效性.
- 这项研究为医疗保健,可穿戴设备和个性化用户体验中的非侵入性情绪识别系统铺平了道路.
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