基于心跳动态点过程特征的情绪识别

A. S. Ravindran, Sho Nakagome, D. S. Wickramasuriya, J. Contreras-Vidal, R. Faghih
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引用次数: 10

摘要

仅从心跳信息中识别人类情感是一个具有挑战性但正在进行的研究领域。在这里,我们利用点过程模型来表征心跳动态,并使用它来提取瞬时心率变异性(HRV)特征。然后将这些特征输入卷积神经网络(CNN),以从小窗口描述不同的情绪状态。平均而言,我们的分类准确率达到了60%以上,在一些科目中达到了77%。这与其他使用生理信号组合的研究相媲美,而不是像本研究那样只测量HRV。信息特征被识别为不同的情感状态。这些发现使增强心电图或光电容积图监测可穿戴设备具有自动人类情感识别功能的可能性成为可能,用于心理健康应用。它们还允许将HRV特征的瞬时估计与使用其他类型生理信号的模型相结合,用于瞬时情感识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotion Recognition by Point Process Characterization of Heartbeat Dynamics
Recognizing human emotion from heartbeat information alone is a challenging but ongoing research area. Here, we utilize a point process model to characterize heartbeat dynamics and use it to extract instantaneous heart rate variability (HRV) features. These features are then fed into a convolutional neural network (CNN) to characterize different emotional states from small windows. On average, we achieved over 60% classification accuracy and as high as 77% in some subjects. This is comparable to other studies that use a combination of physiological signals as opposed to only HRV measures as done here. Informative features were identified for the different affective states. These findings enable the possibility of augmenting electrocardiogram or photoplethysmogram monitoring wearable devices with automated human emotion recognition capabilities for mental health applications. They also allow for the use of instantaneous estimation of HRV features to be used in combination with models that use other types of physiological signals for instantaneous emotion recognition.
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