Respiration-based emotion recognition with deep learning

Respiration-based emotion recognition with deep learning

Computers in Industry 92 (2017) 84–90 Contents lists available at ScienceDirect Computers in Industry journal homepage:

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Computers in Industry 92 (2017) 84–90

Contents lists available at ScienceDirect

Computers in Industry journal homepage:

Respiration-based emotion recognition with deep learning Qiang Zhanga,b , Xianxiang Chena , Qingyuan Zhanc, Ting Yangc , Shanhong Xiaa,* a

Institute of Electronics, Chinese Academy of Sciences, China University of Chinese Academy of Sciences, China c China-Japan Friendship Hospital, China b


Article history: Received 2 December 2016 Accepted 24 April 2017 Available online xxx Keywords: Emotion recognition Deep learning Wearable computing Respiration Arousal-valence theory


Different physiological signals are of different origins and may describe different functions of the human body. This paper studied respiration (RSP) signals alone to ﬁgure out its ability in detecting psychological activity. A deep learning framework is proposed to extract and recognize emotional information of respiration. An arousal-valence theory helps recognize emotions by mapping emotions into a twodimension space. The deep learning framework includes a sparse auto-encoder (SAE) to extract emotionrelated features, and two logistic regression with one for arousal classiﬁcation and the other for valence classiﬁcation. For the development of this work an international database for emotion classiﬁcation known as Dataset for Emotion Analysis using Physiological signals (DEAP) is adopted for model establishment. To further evaluate the proposed method on other people, after model establishment, we used the affection database established by Augsburg University in Germany. The accuracies for valence and arousal classiﬁcation on DEAP are 73.06% and 80.78% respectively, and the mean accuracy on Augsburg dataset is 80.22%. This study demonstrates the potential to use respiration collected from wearable deices to recognize human emotions. © 2017 Published by Elsevier B.V.

1. Introduction With great advance of artiﬁcial intelligence, it is promising to conduct affective computing with physiological signals. Many physiological signals are detected based on wearable devices, such as electrocardiogram (ECG), electroencephalography (EEG), electromyogram (EMG), blood volume pressure (BVP), galvanic skin response (GSR), temperature (TEMP), respiration pattern (RSP), photoplethysmogram (PPG). There is increasing evidence that these signals contain information related to human emotions [1– 6]. Emotion recognition based on physiological signals is promising because these signals are involuntary manifestation of human body and people cannot control them intentionally. Moreover, continuous emotion assessments can be obtained through measurements of physiological signals. Human emotions can be affected by many factors [7,8], and different emotions usually have fuzzy boundaries. Recent studies developed different kinds of emotion recognition models and tested them on their own dataset. In 2001, Professor Picard applied artiﬁcial intelligence to recognize human emotional states given

* Corresponding author. E-mail address: [email protected] (S. Xia). 0166-3615/© 2017 Published by Elsevier B.V.

physiological signals [9]. They extracted statistical time and frequency values and achieved 81% recognition accuracy on eight emotional classes. After that, more complicated features have been extracted. Duan et al. proposed differential entropy to represent EEG feature related to emotional states and achieved average accuracy of 81.17% [10]. Giakoumis et al. [11] introduced the Legendre and Krawtchouk moments to extract biosignal features. Yannakakis and Hallam used the approximate entropy feature [12] and preference learning [13]. Lin et al. applied machine learning algorithms to categorize EEG signals and obtained an average classiﬁcation accuracy of 82.29% for four emotions [14]. Wang et al. systematically compared three kinds of EEG features (power spectrum feature, wavelet feature and nonlinear dynamical feature) for emotion classiﬁcation [15]. Although different features have been tried to describe emotion-related characteristics of physiological signals, manual feature extraction inherits some primary limitations. First of all, performance of hand-crafted feature largely depends on the signal type and human experience. Poor domain knowledge may lead to an inappropriate feature that cannot capture the characteristics of certain signals. Second, there is no general guarantee that any feature selection algorithms will end to the optimal feature set. Third, the most manual features are statistical and can’t depict signal details, which means a loss of information.

Q. Zhang et al. / Computers in Industry 92 (2017) 84–90

Distinctively, deep learning can automatically derive features from the raw signals, as opposed to manually pre-designed statistical features. Deep learning allows automatically feature selection and bypasses the computational cost of feature selection. Recently deep learning methods have been tried to process physiological signal like EEG and skin resistance, and achieved comparable results in comparison with other conventional methods [16–18]. In 2013, Martinez et al. introduced Convolutional Neural Network (CNN) to establish physiological models of affect [16]. To the best knowledge of the authors, this is the ﬁrst attempt to use deep learning for computational modeling of affect. Since that, some studies on deep emotion recognition have been published [19–21]. For example, Zheng trained a Deep Belief Network (DBN) to classify two emotional categories (high and low valence) from EEG data, and Jirayucharoensak implemented a sparse auto-encoder whose input features are from 32-channel EEG signals [24]. To detect sleep stage, Martin et al. compared the manual features and a model combining DBN and hidden Markov model (HMM) [25]. After choosing deep learning as the feature extraction method, we move forward to talk about physiological signals. Different physiological signals are of different origins and may describe different functions of human body. For instance, the ECG and BVP relate to the cardiovascular system, while the EMG describes electrical activities of muscle. It is important to investigate the dynamics of every signal in order to clearly ﬁgure out its feasibility and limitation in assessing psychological activity. To this end, this work investigates RSP signals alone. Respiratory pattern contains rich information about emotional states. Respiration velocity and depth usually varies with human emotion. For example, deep and fast breathing shows excitement that is accompanied by happy, angry or afraid emotion; shallow and fast breathing shows tension; relaxed people often have deep and slow breathing; shallow and slow breathing shows a calm or


negative state. When in calm states, people usually breathe about 20 times per minute while in excitement, people breathe 40 to 50 times per minute. From RSP data, we selected four segments corresponding four kinds of emotions, as shown in Fig. 1. As the RSP signal contains a wealth of emotional information, and can be easily detected in wearable devices, therefore in this paper, we focus on emotion recognition via respiration signals. To help recognize emotions, we used the Russel’s Circumplex theory of emotion [26]. Speciﬁcally, each emotion is seen as a linear combination of two affective dimensions: arousal and valence. Fig. 2 shows the general architecture of the deep learning framework. We used a deep sparse auto-encoder (SAE) to extract hidden features of RSP. Two logistic regression categorize the features, with one for the arousal classiﬁcation, and the other for the valence classiﬁcation. To validate the efﬁcacy of the SAE-based approach, an emotion classiﬁcation experiment was carried out using the DEAP database, which is the largest, most comprehensive physiological signalemotion dataset publicly available to date. To further evaluate the proposed method on other people, after model establishment, we used the affection database established by Augsburg University in Germany. The paper is organized as follows: Section 2 introduces the arousal-valence theory, Section 3 describes the deep learning framework that consists of sparse auto-encoder and logistic regression, Experiment data, setting and results are presented in Section 4, and discussion and conclusion are shown in Sections 5 and 6, respectively. 2. Arousal-valence emotion theory In this study, we used the Russel’s Circumplex theory to help emotion recognition. This theory indicates that emotional states are distributed in a two-dimensional circular space, with arousal and valence dimensions [26]. Arousal is the vertical axis and

Fig. 1. Four 20-s respiration signal segments under different emotional states: (a) high valence and high arousal, (b) low valence and high arousal, (c) low valence and low arousal, (d) high valence and low arousal. The horizontal axis represents the sampling points while the vertical axis is the locally normalized magnitude of respiration signals.



Fig. 2. Example of general architecture of the deep learning framework. The architecture contains: (a) a sparse autoencoder (SAE) with two hidden layers, and (b) two logistic classiﬁers. In the illustrated SAE, the ﬁrst hidden layer (200 neurons) processes a respiration signal with path length of 640 samples. A second hidden layer (50 neurons) processes the 200 neurons from the ﬁrst hidden layer. The ﬁnal 50 neurons (features) form the output of the SAE which provides a number of extracted (learned) features which feed the input of the logistic regression.

Fig. 3. Arousal-valence theory of emotions. Y-axis represents the arousal while the x-axis represents the valence.



element of SAE, an auto-encoder (AE) converts the input data to the hidden representation (extracted features) by the encoder (see Fig. 4). The AE also learns how to map back the features to the input space by a decoder. The original and the reconstructed input data are as similar as possible, despite of small reconstruction error on the training examples. The whole structure takes the extracted features of one AE as the input of another AE, to ﬁnd general representation from the input hierarchically [28–30]. In the following procedures, we focused on pre-training an AE, which is taken as the ﬁrst SAE hidden layer. The techniques can also be applied to train the second and other hidden layers. Once pretrained, the ﬁrst layer will not be changed during training other hidden layers. Generally, the encoder maps an input example x 2 Rn to a hidden representation hðxÞ 2 Rm hðxÞ ¼ f ðW1  x þ b1 Þ;


where W 1 2 Rmn is a weight matrix, b1 2 Rm is a bias vector, and f ðzÞ is a non-linear activation function, typically a sigmoid function f ðzÞ ¼ 1=ð1 þ expðzÞÞ. The decoder maps the hidden representa~ 2 Rn : tion back to a reconstructed input x ~ ¼ f ðW2  hðxÞ þ b2 Þ; x




is a weight matrix, and b2 2 R is a bias vector where W 2 2 R and f is the same function as that of the encoder. To minimize the dissimilarity between the original and the reconstructed input, we consider decreasing the reconstruction D X ~ ðiÞ k.Given the training dataset with D examples k xðiÞ  x gap Fig. 4. The structure of an auto-encoder. The encoder generates thelearned representation (extracted features) from the input signals.The features are fed to adecoder that attempts to reconstruct the input.

measures the intensity of emotional activation, while valence is the horizontal axis and describes the negative or positive mind. The arousal-valence emotion theory suggests that a common and interconnected neurophysiological system is responsible for all affective states [27]. We can see in Fig. 3 how the emotions are classiﬁed by this theory. As we can see the “High Valence, Low Arousal” is mapped in the lower right quadrant, whereas the “Low Valence, High Arousal” is mapped in the higher left quadrant. The “High Valence, High Arousal” is somewhere in the higher right quadrant, while the “Low Valence, Low Arousal” is in the lower left quadrant. By determining the arousal valence value of certain emotion, we can recognize emotions as the four classes. In this way, the emotion recognition task is divided into two binary classiﬁcation tasks.

3. Deep learning framework We investigated an effective method that maps signals of user behavior to affective states. As shown in Fig. 2, we use a deep learning framework consisting of the sparse auto-encoder that transforms the raw signals into extracted features and two logistic regression that predict affective states. Our hypothesis is that automatic feature extraction via deep learning will yield physiological affect detectors of higher predictive power, which, in turn, will deliver affective models of higher accuracy.

3.1. Sparse auto-encoder Sparse auto-encoder is a method of deep learning that is used to automatically learn features from unlabeled data. As the basic


xðiÞ , i = 1, . . ., D, the weight matrices W1and W2 and the bias vector b1and b2 are adjusted by back-propagation. Further, we constrain the expected activation of the hidden units to be sparse by adding a penalty term [31]. Thus it turns out to be the following optimization problem: P m X X ~ðiÞ k2 þ b SPðr k r ^ j Þ; k xðiÞ  x min i¼1



where ^ j Þ ¼ rlog SPðr k r

r 1r þ ð1  rÞlog ^j ^j r 1r


XD   ^ j ¼ D1 h xðiÞ is the average is a sparse penalty term, r i¼1 j

activation of hidden unit j, r is a sparsity level, and b leverages the weight of the sparsity penalty term. We minimized the cost function by the L-BFGS [32], so as to obtain the optimal W1 and b1that is used to ﬁnd the inner features h(x).

3.2. Logistic regression After extracting features via the sparse auto-encoder, we fed the features into two logistic regression (LR) to recognize the arousal and valence, respectively. LR processes the binary classiﬁcation problem: yðiÞ 2 f0; 1g. To train the logistic regression, we consider the following procedure. We have a training set  1 1    of D labeled examples, where the input x ; y ; . . . ; xD ; yD features are xðiÞ 2 Rn . Given an input xðiÞ , we expect our hypothesis   to estimate the probability P yðiÞ ¼ kjxðiÞ for each class. Thus, our hypothesis will output a 2-dimensional vector (whose elements sum to 1) giving us two probabilities. Concretely, our hypothesis   hu xðiÞ takes the form: hu ðxðiÞ Þ ¼

1 : 1 þ expðu  xðiÞ Þ




Here u 2 Rn is the vector of parameters of LR. The parameters were trained by minimizing the cost function: D 1X 1 2 ðh ðxðiÞ Þ  yðiÞ Þ : JðuÞ ¼  D i¼1 2 u


We optimized the cost function by the L-BFGS method so as to obtain the optimal parameters of LR. 4. Experiment 4.1. Experiment data To test the efﬁcacy of our method, we adopted an international database known as Dataset for Emotion Analysis using Physiological signals (DEAP). DEAP consists of physiological signals from the spontaneous reactions of 32 participants in response to 40 oneminute long music video clips. These 32 participants age between 19 and 37 (mean age 26.9) and half of them are females. In each of the 40 trials, signal recordings include electroencephalogram, electromyography, electrooculogram, galvanic skin response, temperature, respiration pattern, plethysmography, audio and video signals. Participants were asked to do self-assessment by assigning values from 1 to 9 to ﬁve different status, namely, valence, arousal, dominance, liking, and familiarity. In order to realize fast on-site emotion detection, we tried to evaluate RSP segments with different time length between 5 s and 60 s. Some choices result in bad performance, some other choices maybe contain more information, but they are too long for on-site detection. We gave consideration to both the efﬁcacy and efﬁciency and ﬁnally decided to use 20-s segments. The RSP signal was sampled at 32 Hz. From the start of each record, we cut the RSP data in 20-s segments and the cutting window moves every second. I.e., in two adjacent segments, the later one starts from the 2rd second of the former segment, as a result there is 19-s overlapping between adjacent segments. So in a 60-s trial, we could obtain 41 segments. In total of 32 participants, with 40 trails in every participant and 41 segments in each trial, we have 52,480 (41 segments  40 trails  32 participants) data samples. Every segment was not normalized using global mean and standard deviation of the 60-s trial; instead, they were normalized by the local mean value and standard deviation within each segment, making learned features only sensitive to variation within the segment and insensitive to the baseline level. To further evaluate the proposed method on other people, after model establishment, we used the affection database established by Augsburg University. The database consists of 4 affective states over 25 days. Musical induction method spontaneously leads subjects to real emotional states. The RSP signal was also sampled at 32 Hz. Following the abovementioned procedure, the 120-s RSP data wad cut into 101 20-s segments. In total of 25 days, with 4 emotions in every day and 101 segments in each trial, we have 10,100 (101 segments  4 emotions  25 days) data samples.

Fig. 5. The original (Blue) and the reconstructed (Red) RSP. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

4.2. Experiment setting We trained several sparse auto-encoders to extract features for the RSP signal and across all affective states in the dataset. The topologies of the auto-encoders were selected after preliminary experiments with 1- and 2-layer SAE and 20-step trial neuron numbers in each hidden layer. To test the efﬁcacy of our method with previous results, we divided the value of arousal and valence into two binary classes according to the assigned values. The threshold we chose is 5, and the tasks can be treated as binary classiﬁcation problems, namely, high/low valence and high/low arousal. Among all of the DEAP data, 80% samples were used as training data and the rest 20% samples were used as test data. After the emotion recognition model establishment, the whole dataset of Augsburg University is used to evaluate its generalization capability on other people. 4.3. Experiment results We have systematically tested critical parameters of SAE (e.g., the number of layers, the numbers of neurons in each layer, and the sparsity penalty). The number of neurons in each hidden layer was searched with step of 20 in the range of [10:500]. The learning rate was 0.01 and the momentum regarding the weight update was used to prevent being struck in local minima. In our experiment, suppose the inputs are the respiration signal values from a 20-s segment (32 Hz, 640 points) so x 2 R640 , we used 200 neurons in the ﬁrst hidden layer and 50 neurons in the second hidden layer, so h1 ðxÞ 2 R200 and h2 ðh1 ðxÞÞ 2 R50 .

Table 1 Comparison with representative studies in the area of physiological signal-based emotion recognition in recent years.

Zhuang et al. [34] Torres-Valencia et al. [35] Martinez et al. [36] Xu et al. [37] Liu et al. [38] Our work, 2017

Signal Type



Support Vector Machine Hidden Markov Models Convolutional Neural Network Deep Belief Networks Multimodal Deep Learning Deep Sparse Auto-Encoders

Classiﬁcation Accuracy Valence


70.9 58.75 63.3 66.88 85.2 73.06

67.1 75.00 69.1 69.84 80.5 80.78


After each layer of SAE was pretrained, we ﬁne-tuned the deep learning framework include the SAE and the logistic regression. We demonstrated results on the optimal SAE that has two hidden layers. As shown in Fig. 1, the ﬁrst and second hidden layer contains 200 and 50 neurons, respectively. To show the reconstruction of SAE, as in Fig. 5, the same RSP signal (blue) with Fig. 1(b) is fed into the SAE. The red line represents the output (reconstructed input) of SAE. We can indicate that the output is able to track changes of input even when the input RSP signal is complex. Table 1 compares our method with some representative studies that are also tested on the DEAP dataset. Our work achieved similar accuracy with these methods. The classiﬁcation accuracy of valence and arousal is 73.06% and 80.78%, respectively. Further, we evaluated the proposed method using the affection database established by Augsburg University. The established model achieved 85.89% accuracy for arousal classiﬁcation and 83.72% for valence classiﬁcation. The mean accuracy of four types of emotions is 80.22%.


the hope of seizing the intrinsic characteristics of respiration activities. Second, deep learning makes use of the unlabeled data and reduce the number of labeled samples needed for the learning process where labeled data can guide a learning algorithm towards faster hyper-parameter selection. As a result, the emotion recognition model is fully-data driven and application speciﬁc. Although studies in Table 1 all used the DEAP dataset, they were evaluated in different ways, such as different fold cross validation and different partition of test dataset, so the results of these studies cannot be compared directly. Nevertheless, we can still roughly ﬁnd that our method almost achieved the same performance as the best study did. Respiration signals have a simple structure that is similar to a sinusoidal function or a triangular function, which made it very easy to capture the inner features. Moreover, respiration signals are of non-invasiveness, cost effectiveness and simple acquisition, thus is promising in wearable applications, such as wrist-based emotion detection. It is now possible to implement RSP-based emotion recognition from laboratories to real-world applications.

5. Discussion 6. Conclusion In this study we propose an effective emotion recognition system based on RSP alone. The main contributions of this paper can be described as the following aspects. First, considering the feature extraction and feature selection properties of deep learning, we introduced the sparse auto-encoder to extracted respiration features for emotion recognition. Second, the experiment results indicate that the respiration signal is so powerful in affective state recognition that wearable devices do not have to fully rely on EEG. Deep learning is of computational complexity in model training but takes little time in testing. On average, it takes our algorithm 2.81 s to test one data sample on the Matlab 2014b in the 32-bit Windows 7 operating system with an Intel i5 CPU and 4G RAM. At least, we believe the 2.81 s is an acceptable response time in practical emotion recognition applications. In Fig. 1, RSP signals under four emotional states are compared. We can indicate that emotions with high valence have more steady respiration with uniform RSP magnitude than those with low valence. As for difference in arousal, high arousal has large respiration frequency while low arousal has small frequency. Low arousal has fast snifﬁng and slow breath with long tail. About signal types, most studies use EEG, this is mainly based on the consideration that the brain is primarily responsible for emotion processing and corresponding responses. Consequently, an increasing amount of researchers are dedicated to detect affective states using neurosignal paradigms such as EEG. However, brain activity nature of emotional functions is partially localized in both space (some cortical and subcortical regions) and frequency (mostly upon the neural oscillation bands) [33]. Although EEG provided high temporal resolution, it suffers from its poor spatial resolution and raised susceptibility to noise. More attention should be transferred from the mainstreaming EEG to other psychophysiological signals. As for the classiﬁers, Table 1 clearly shows the ever growing proliferation of deep learning in emotion classiﬁcation. Different deep models (CNN, DBN) has gradually replaced shallow models (SVM, HMM) and achieved superior performance. This is mainly due to several facts. First of all, deep models are more ﬂexible in feature extraction and eliminate the assumptions used by current feature extraction algorithms, such as the linearity assumption in PCA. Some irregular respiration activities, such as whimper and guffaw, may lead to complex signal waveform and cessation of respiration, which couldn’t be fully characterized by statistical features. Deep learning can automatically derive features, which may contain information that manually pre-designed statistical features don’t involve. Therefore we selected deep learning with

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