通过深度学习利用植物的敏感性来识别人类的情绪
Jakob Adrian Kruse1,2, Leon Ciechanowski2,3, Ambre Dupuis2,4
1School of Computation, Information and Technology, Technische Universität München (TUM), Arcisstr. 21, 80333 München, Germany.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
植物可以通过测量它们的电信号来检测人类的情绪,为当前的方法提供了成本效益和私密的替代方案. 随机森林模型在识别恐惧和快乐等情绪方面显示出有希望的结果.
科学领域:
- 生物技术是生物技术.
- 人工智能的人工智能
- 行为科学 行为科学
背景情况:
- 目前的情绪识别工具依赖于昂贵,侵入性和受监管的数据源,如文本,视频,音频和生理信号.
- 植物对人类的存在和行为表现出敏感性,表明它们作为生物传感器的潜力.
研究的目的:
- 提出和评估一种新的方法来检测人类的情绪,使用来自植物的电信号.
- 探索植物作为一种具有成本效益和保护隐私的情感检测替代品的可行性.
主要方法:
- 来自植物的电信号被记录并与视频数据同步,用于标记人类情绪.
- 多个机器学习模型 (MLP,biLSTM,MFCC-CNN,MFCC-ResNet,随机森林,1D CNN) 通过网格搜索和交叉验证进行训练和优化.
- 模型的表现通过一个涉及54名参与者观看情感视频的案例研究来评估,面部情感分析作为基本真相.
主要成果:
- 随机森林模型实现了最高的整体加权准确率 (55.2%),显著的回忆是高兴奋情绪,如恐惧 (61.0%) 和快乐 (60.4%).
- MFCC-ResNet模型表现出平衡的结果,准确度为0.318和回忆率为0.324,分别识别了75%和50%的恐惧和愤怒回忆.
结论:
- 这项研究表明,植物显示出作为人类情感识别的可行工具的潜力.
- 这种基于植物的方法解决了与现有的情绪检测技术相关的重大成本和隐私问题.
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