通过手腕设备收集的生理信号来识别情绪,并回忆情绪
Enni Mattern1, Roxanne R Jackson1, Roya Doshmanziari2
1Chair of Control Engineering, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany.
Bioengineering (Basel, Switzerland)
|November 25, 2023
概括
从生理信号中识别情绪在实验室之外是具有挑战性的. 这项研究发现,在使用特定主体数据时,情绪识别准确性显著提高,但在独立主体模型中仍然很低.
科学领域:
- 情感计算是一种情感计算.
- 人与计算机的互动.
- 物理计算的物理计算.
背景情况:
- 使用生理信号进行有效的情绪识别对于现实世界的情感工程应用至关重要.
- 在将基于实验室的情绪识别发现转化为更高技术准备水平方面存在差距.
- 这项研究调查了情绪识别在受控实验室环境之外的可行性.
研究的目的:
- 评估情绪识别的可行性使用生理测量在一个最小的侵入性,现实世界的设置.
- 探索主体依赖对情绪识别准确性的影响.
- 根据生理数据评估机器学习模型来对情绪进行分类.
主要方法:
- 使用Empatica腕带收集生理数据 (血液体积压力,皮电活动,皮肤温度,加速).
- 用自传情感记忆任务来进行情感回忆.
- 应用基于特征的监督学习,特别是支持矢量机器,使用各种细分方法.
- 进行了十倍交叉验证和一个主题的交叉验证.
主要成果:
- 取决于主体的模型在分类四种情绪方面达到高达75%的准确性.
- 独立于主体的模型 (留下一个主体) 的准确性很低,约为32%.
- 最初的独立于受试者的十倍交叉验证显示准确度仅略高于随机猜测 (36%).
结论:
- 从生理信号识别情绪高度依赖于主体.
- 对特定主体的数据显著提高了分类准确性.
- 需要进一步的研究来克服对实体情绪识别系统的强大主体依赖性挑战.
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