Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple modalities - audio, visual, and physiological - to classify emotional state. However, practical considerations 'in the wild' limit collection of this physiological data to commoditised heartbeat sensors. Furthermore, real-world applications often require some measure of uncertainty over model output. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat data. We further propose a Bayesian framework for modelling uncertainty over valence predictions, and describe a procedure for tuning output according to varying demands on confidence. We benchmarked our framework against two established datasets within the field and achieved peak classification accuracy of 90 . These results lay the foundation for applications of affective computing in real-world domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.
Signals from peripheral physiology (e.g., ECG, EMG, and GSR) in conjunction with machine learning techniques can be used for the automatic detection of affective states. The affect detector can be user-independent, where it is expected to generalize to novel users, or user-dependent, where it is tailored to a specific user. Previous studies have reported some success in detecting affect from physiological signals, but much of the work has focused on induced affect or acted expressions instead of contextually constrained spontaneous expressions of affect. This study addresses these issues by developing and evaluating user-independent and user-dependent physiology-based detectors of nonbasic affective states (e.g., boredom, confusion, curiosity) that were trained and validated on naturalistic data collected during interactions between 27 students and AutoTutor, an intelligent tutoring system with conversational dialogues. There is also no consensus on which techniques (i.e., feature selection or classification methods) work best for this type of data. Therefore, this study also evaluates the efficacy of affect detection using a host of feature selection and classification techniques on three physiological signals (ECG, EMG, and GSR) and their combinations. Two feature selection methods and nine classifiers were applied to the problem of recognizing eight affective states (boredom, confusion, curiosity, delight, flow -engagement, surprise, and neutral). The results indicated that the user-independent modeling approach was not feasible; however, a mean kappa score of 0.25 was obtained for user-dependent models that discriminated among the most frequent emotions. The results also indicated that k-nearest neighbor and Linear Bayes Normal Classifier (LBNC) classifiers yielded the best affect detection rates. Single channel ECG, EMG, and GSR and three-channel multimodal models were generally more diagnostic than two--channel models.  Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition. Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological data set to a feature-based multiclass classification. In order to collect a physiological data set from multiple subjects over many weeks, we used a musical induction method that spontaneously leads subjects to real emotional states, without any deliberate laboratory setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity, and respiration changes. A wide range of physiological features from various analysis domains, including time frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive high arousal, negative high arousal, negative low arousal, and positive low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. An improved recognition accuracy of 95 percent and 70 percent for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.