个性化集群用于情绪识别改进情绪识别改进
Laura Gutiérrez-Martín1, Celia López-Ongil1,2, Jose M Lanza-Gutiérrez3
1Departamento de Tecnología Electrónica, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, 28911 Leganés, Spain.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了半个性化的AI模型,用于使用生理信号识别情绪. 这些定制模型提高了准确性,并减少了用于增强安全和福祉应用的变化.
科学领域:
- 情感计算是一种情感计算.
- 人工智能的人工智能是人工智能.
- 人与计算机的互动.
背景情况:
- 通过人工智能和智能传感识别情绪识别显示出希望,但需要对现实世界的安全应用进行改进.
- 目前的系统需要快速,离散和实时,特别是在暴力检测和心理健康等敏感领域.
- 一般的人工智能模型不足以保护多用户,因为情绪反应的个体差异.
研究的目的:
- 开发定制的,轻量级的人工智能模型来识别情绪,以适应具有相似生理反应的个人群.
- 在现实应用中解决半个性化模型的需求,增强安全和福利系统.
- 介绍一种用于集群用户的方法,并创建可适应的人工智能模型,这些模型可以包含新的,未标记的数据.
主要方法:
- 根据标记的生理数据和情绪反应对象的聚类.
- 为每个已识别的集群开发个性化的AI模型.
- 实施一种方法,将未标记数据的新用户注册到现有模型中.
- 人工智能模型的持续更新与新的传入数据.
主要成果:
- 与一般基线模型相比,准确度提高了5%.
- 与基线模型相比,F1得分增加了4%.
- 报告说,模型变异性显著减少,从32%到58%不等.
结论:
- 为半个性化情绪识别模型提出的方法提高了性能,减少了变性.
- 这种方法适用于处理未标记数据和各种用户配置文件的专家系统.
- 这些发现支持开发更有效和个性化的AI解决方案,用于安全和心理健康监测.
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