Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep learning that attain impressive results in many domains, pain estimation risks not being able to benefit from this due to the difficulty in obtaining data sets of considerable size. In this work we propose a combination of hand-crafted and deep-learned features that makes the most of deep learning techniques in small sample settings. Encoding shape, appearance, and dynamics, our method significantly outperforms the current state of the art, attaining a RMSE error of less than 1 point on a 16-level pain scale, whilst simultaneously scoring a 67.3 Pearson correlation coefficient between our predicted pain level time series and the ground truth.
Sound or cry analysis is mostly common in newborns as it is a major form of expression at that age. Acoustic characteristics have been analysed to discriminate between normal and pain induced cry in newborns @cite_10 . Compared to pain analysis from audio-visual signals, physiological signals have received less attention. They have been used for valence and arousal detection of other emotions (e.g. joy, sadness, anger and pleasure) but only a few have applied it to pain detection @cite_19 @cite_25 @cite_26 . The use of bio-signals is still far from practical use because it is still difficult to map physiological patterns to specific emotions. Nonetheless, physiological signals are reputed to be robust to intentional emotion suppression since they are directly controlled by the nervous system and again, information can be collected in real time using bio-sensors @cite_23 . Physiological signals commonly used include galvanic skin response (GSR), photoplethysmogram (PPG), electrocardiogram (ECG), respiration changes and skin conductivity.
@cite_26 @cite_19 @cite_23 @cite_10 @cite_25
1996089789 2066658540 2153771043 2536532022
How much does it hurt? Accurate assessment of pain is very important for selecting the right treatment, however current methods are not sufficiently valid and reliable in many cases. Automatic pain monitoring may help by providing an objective and continuous assessment. In this paper we propose an automatic pain recognition system combining information from video and biomedical signals, namely facial expression, head movement, galvanic skin response, electromyography and electrocardiogram. Using the BioVid Heat Pain Database, the system is evaluated in the task of pain detection showing significant improvement over the current state of the art. Further, we discuss the relevance of the modalities and compare person-specific and generic classification models. Pain is what the patient says it is. But what about these who cannot utter? Automatic pain monitoring opens up prospects for better treatment, but accurate assessment of pain is challenging due to the subjective nature of pain. To facilitate advances, we contribute a new dataset, the BioVid Heat Pain Database which contains videos and physiological data of 90 persons subjected to well-defined pain stimuli of 4 intensities. We propose a fully automatic recognition system utilizing facial expression, head pose information and their dynamics. The approach is evaluated with the task of pain detection on the new dataset, also outlining open challenges for pain monitoring in general. Additionally, we analyze the relevance of head pose information for pain recognition and compare person-specific and general classification models. Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech. In this paper, we discuss the most important stages of a fully implemented emotion recognition system including data analysis and classification. For collecting physiological signals in different affective states, we used a music induction method which elicits natural emotional reactions from the subject. Four-channel biosensors are used to obtain electromyogram, electrocardiogram, skin conductivity and respiration changes. After calculating a sufficient amount of features from the raw signals, several feature selection reduction methods are tested to extract a new feature set consisting of the most significant features for improving classification performance. Three well-known classifiers, linear discriminant function, k-nearest neighbour and multilayer perceptron, are then used to perform supervised classification Infant cry is a multimodal behavior that contains a lot of information about the infant, particularly, information about the health of the infant. In this paper a new feature in infant cry analysis is presented for recognition two groups: infants with pain and normal infants, by Mel frequency multi-band entropy extraction from infant's cry. In signal processing stage we made pre-processing included silence elimination, filtering, pre-emphasizing. After taking Fourier transform, spectral entropy was computed as single feature of signal. In classifying stage, by training artificial neural network, correction rate of recognition was obtained 66.9 . In order to enhancement in results, we used Mel filter bank. Entropy of each sub-band constitutes elements of next feature vector. We used PCA analysis for reducing in dimension of the recent feature vector. After ANN training, correction rate improved to 88.5 . So multiband spectral entropy enhanced results in salient correction rate.