通过可穿戴设备识别情绪的集体深度学习框架 多模式生理信号
Durgesh Nandini1, Jyoti Yadav1, Vijander Singh1
1Department of ICE, Netaji Subhas University of Technology, Sector 3, Dwarka, New Delhi, India.
Scientific reports
|May 18, 2025
概括
这项研究引入了一种新的情绪识别系统,使用可穿戴设备和深度学习. 该系统准确地识别了离散和维度的情绪,为情感意识技术铺平了道路.
科学领域:
- 计算机科学 计算机科学
- 情感计算是一种情感计算.
- 人与计算机的交互
背景情况:
- 可穿戴的健身追踪器使健康监测成为可能.
- 通过可穿戴设备识别情绪可以增强人机交互.
- 准确的情绪检测对于开发情绪意识系统至关重要.
研究的目的:
- 提出并通过实验分析使用可穿戴设备的情绪识别系统.
- 为了评估离散和维度情绪模型.
- 为了比较不同可穿戴设备在情绪识别方面的性能.
主要方法:
- 采用了一个集体深度学习架构,结合了长期短期记忆 (LSTM) 和门式循环单元 (GRU) 模型.
- 使用了EMOGNITION数据库,其中包括来自Samsung Galaxy Watch,Empatica E4和MUSE 2脑电图头带的生理数据.
- 通过使用各种生物信号组合,研究了九种离散情绪和二维价值-激发维度模型.
主要成果:
- 获得了高分类准确率:三星Galaxy Watch的99.14%和MUSE 2的99.41%用于离散情绪.
- 对于Valence-Arousal模型,三星Galaxy Watch实现了Valence的97.81%精度和Arousal的72.94%精度.
- 该研究表明,与现有方法相比,使用可穿戴技术识别情绪的结果很有希望.
结论:
- 可穿戴设备,特别是三星Galaxy Watch和MUSE 2EEG,显示出精确情绪识别的巨大潜力.
- 拟议的深度学习方法有效地捕捉了情绪检测的生理信号中的时间依赖性.
- 这项研究有助于推进情感意识系统和人机交互的发展.
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