Background and objective: Affect provides contextual information about the emotional state of a person as he/she communicates in both verbal and/or non-verbal forms. While human’s are great at determining the emotional state of people while they communicate in person, it is challenging and still largely an unsolved problem to computationally determine the emotional state of a person.

Methods: Emotional states of a person manifest in the physiological biosignals such as electrocardiogram (ECG) and electrodermal activity (EDA) because these signals are impacted by the peripheral nervous system of the body, and the peripheral nervous system is strongly coupled with the mental state of the person. In this paper, we present a method to accurately recognize six emotions using ECG and EDA signals and applying autoregressive hidden Markov models (AR-HMMs) and heart rate variability analysis on these signals. The six emotions include happiness, sadness, surprise, fear, anger, and disgust.

Results: We evaluated our method on a comprehensive new dataset collected from 30 participants. Our results show that our proposed method achieves an average accuracy of 88.6% in distinguishing across the 6 emotions.

Conclusions: The key technical depth of the paper is in the use of the AR-HMMs to model the EDA signal and the use of LDA to enable accurate emotion recognition without requiring a large number of training samples. Unlike other studies, we have taken a hierarchical approach to classify emotions, where we first categorize the emotion as either positive or negative and then identify the exact emotion.

背景和目的：情感提供有关一个人以语言和/或非语言形式进行交流时的情绪状态的背景信息。尽管人在与人进行交流时很擅长确定人的情绪状态，但在计算上确定人的情绪状态仍是挑战，并且在很大程度上仍然是一个未解决的问题。

方法：人的情绪状态会在生理生物信号中显示出来，例如心电图（ECG）和皮肤电活动（EDA），因为这些信号会受到身体周围神经系统的影响，并且周围神经系统与精神状态密切相关人的。在本文中，我们提出了一种使用ECG和EDA信号准确识别六种情绪并在这些信号上应用自回归隐马尔可夫模型（AR-HMM）和心率变异性分析的方法。六种情绪包括幸福，悲伤，惊奇，恐惧，愤怒和厌恶。

结果：我们在从30名参与者那里收集的综合新数据集上评估了我们的方法。我们的结果表明，我们提出的方法在区分6种情绪方面达到了88.6％的平均准确度。

结论：本文的关键技术深度在于使用AR-HMMs对EDA信号进行建模，以及使用LDA来实现准确的情绪识别，而无需大量的训练样本。与其他研究不同，我们采用了分层的方法对情绪进行分类，在这种方法中，我们首先将情绪归为正面或负面，然后确定确切的情绪。