Emotion Recognition by Point Process Characterization of Heartbeat Dynamics
Emotion Recognition by Point Process Characterization of Heartbeat Dynamics
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DOI：
10.1109/hi-poct45284.2019.8962886
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
A. S. Ravindran;S. Nakagome;D. S. Wickramasuriya;J. Contreras-Vidal;R. T. Faghih
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文献类型：
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作者：
A. S. Ravindran;S. Nakagome;D. S. Wickramasuriya;J. Contreras-Vidal;R. T. Faghih
仅从心跳信息中识别人类情感是一个具有挑战性但仍在进行的研究领域。在这里，我们利用点过程模型来表征心跳动态，并用它来提取瞬时心率变异性（HRV）特征。然后，这些特征被输入到卷积神经网络（CNN）中，以从小窗口表征不同的情绪状态。平均而言，我们的分类准确率超过 60%，某些科目的分类准确率高达 77%。这与使用生理信号组合的其他研究相当，而不是像这里所做的那样仅使用 HRV 测量。确定了不同情感状态的信息特征。这些发现使得增强心电图或光电体积描记图监测可穿戴设备的可能性成为可能，这些设备具有自动人类情绪识别功能，适用于心理健康应用。它们还允许将 HRV 特征的瞬时估计与使用其他类型的生理信号进行瞬时情绪识别的模型结合使用。
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.