用消费者可穿戴设备识别情绪的个性化和通用化方法的比较:机器学习研究
1Information and Computer Sciences, University of Hawai`i at Mānoa, Honolulu, HI, United States.
JMIR AI
|June 14, 2024
概括
个性化机器学习模型在使用可穿戴生物信号数据对压力等情绪进行分类时明显优于通用模型. 这突显了个性化方法在准确识别情绪方面的潜力.
科学领域:
- 情感计算是一种情感计算.
- 机器学习是机器学习.
- 可穿戴技术是可穿戴的技术.
背景情况:
- 慢性压力和负面情绪与不良健康结果有关.
- 早期发现压力对于及时进行医疗干预至关重要.
- 可穿戴生物传感器为监测生理压力指标提供了一种非侵入性方法.
研究的目的:
- 为了比较个性化和通用化机器学习模型对情绪分类的性能.
- 为了评估使用可穿戴生物信号数据的模型,对三个情绪状态进行评估:中性,压力和娱乐.
主要方法:
- 一个神经网络被开发为3类情绪分类.
- 利用了来自可穿戴式压力和影响检测 (WESAD) 数据集的数据,包括15名参与者的生理信号.
- 在参与者独占的通用化,参与者包含的通用化和个性化的深度学习模型之间进行了性能比较.
主要成果:
- 个性化的模型实现了95.06%的准确性和91.71%的F1得分.
- 包括参与者在内的通用模型实现了66.95%的准确性和42.50%的F1得分.
- 参与者独有的通用模型实现了67.65%的准确性和43.05%的F1分数.
结论:
- 个性化情绪识别模型在特定应用中表现出高于一般化模型的性能.
- 这项研究验证了个性化机器学习模型的可行性和高性能,用于情感分类.
关键词:
影响检测 影响检测有影响力的计算.深度学习是一种深度学习.数字健康数字健康情感识别 情感识别 情感识别机器学习是机器学习.心理健康 心理健康个性化个性化个性化压力检测 压力检测穿戴式技术是一种可穿戴技术.更多相关视频
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