Automatic pain intensity assessment from physiological signals has become an appealing approach, but it remains a largely unexplored research topic. Most studies have used machine learning approaches built on carefully designed features based on the domain knowledge available in the literature on the time series of physiological signals. However, a deep learning framework can automate the feature engineering step, enabling the model to directly deal with the raw input signals for real-time pain monitoring. We investigated a personalized Bidirectional Long short-term memory Recurrent Neural Networks (BiLSTM RNN), and an ensemble of BiLSTM RNN and Extreme Gradient Boosting Decision Trees (XGB) for four-category pain intensity classification. We recorded Electrodermal Activity (EDA) signals from 29 subjects during the cold pressor test. We decomposed EDA signals into tonic and phasic components and augmented them to original signals. The BiLSTM-XGB model outperformed the BiLSTM classification performance and achieved an average F1-score of 0.81 and an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 over four pain states: no pain, low pain, medium pain, and high pain. We also explored a concatenation of the deep-learning feature representations and a set of fourteen knowledge-based features extracted from EDA signals. The XGB model trained on this fused feature set showed better performance than when it was trained on component feature sets individually. This study showed that deep learning could let us go beyond expert knowledge and benefit from the generated deep representations of physiological signals for pain assessment.

Optimization of pain assessment and treatment is an active area of research in healthcare. The purpose of this research is to create an objective pain intensity estimation system based on multimodal sensing signals through experimental studies. Twenty eight healthy subjects were recruited at Northeastern University. Nine physiological modalities were utilized in this research, namely facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), and blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), blood pressure (BP). Statistical analysis and machine learning algorithms were deployed to analyze the physiological data. FE, EEG, SC, BVP, and BP proved to be able to detect different pain states from healthy subjects. Multi-modalities proved to be promising in detecting different levels of painful states. A decision-level multi-modal fusion also proved to be efficient and accurate in classifying painful states.

In the past few decades, the rapid development of haptic technology has made it widely used in the fields of virtual reality, video games, rehabilitation, education and manufacturing. This paper presents a study on rapid construction of a cost-effective virtual haptic device. In this connection, the study proposed a new design concept called haptic environment. A whole haptic system is divided into an environment, which consists of all common modules, and a haptic robot. As such, a customized haptic robot can be plugged into the environment to form a haptic system. The environment also contains adapters that facilitate the accommodation of the customized haptic robot into the environment. To show the effectiveness of this concept, a three-degree-of-freedom robot along with the environment was implemented in this study. Experiments were conducted to understand the performance of the prototype of the haptic system, as developed, and the results show that the developed haptic system has the graphical refresh frequency of about 60 HZ and the haptic rendering frequency of about 500 HZ, nearly three times faster than the Phantom Omni device commercially available. The contribution of the study reported in this paper includes: (1) the demonstration of the feasibility of rapid construction of a virtual haptic device and (2) the provision of a cost-effective haptic device for applications in robotic rehabilitation and human assistive systems

Patients’ pain level of the patients is a fundamental factor for the patients’ anesthetics usage. To investigate the responses of electroencephalogram (EEG) to cold pressor pain in the frequency domain, twenty healthy subjects’ EEG signals were recorded during a cold pressor test. A band-pass filter and independent component analysis were used to preprocess the EEG signal. Fast Fourier transformation as used to analyze the EEG power spectrum. Paired t-tests were performed on the EEG power to investigate the significance of the difference between pain and no-pain EEG power. In pain state, the EEG power increased significantly at EEG channel location Cz, O1, and O2 over most frequency bands: 1–4 Hz, 4–8 Hz, 8–13 Hz, 13–30 Hz, 30–50 Hz. The EEG power of the gamma band increased over extensive brain regions in pain condition. This study suggests that the power of EEG data in pain state has a higher value over most frequency bands. The central and occipital brain regions show the most significant differences between no-pain and pain state. The findings of this study provide evidence that EEG is a promising tool to objectively assess the pain level of human beings.

Objective: Pain assessment is of great importance in both clinical research and patient care. Facial expression analysis is becoming a key part of pain detection because it is convenient, automatic, and real-time. The aim of this study is to present a cold pain intensity estimation experiment, investigate the importance of the spatial-temporal information on facial expression based cold pain, and study the performance of the personalized model as well as the generalized model. Methods: A cold pain experiment was carried out and facial expressions from 29 subjects were extracted. Three different architectures (Inception V3, VGG-LSTM, and Convolutional LSTM) were used to estimate three intensities of cold pain: No pain, Moderate pain, and Severe Pain. Architectures with Sequential information were compared with single-frame architecture, showing the importance of spatial-temporal information on pain estimation. The performances of the personalized model and the generalized model were also compared. Results: A mean F1 score of 79.48% was achieved using Convolutional LSTM based on the personalized model. Conclusion: This study demonstrates the potential for the estimation of cold pain intensity from facial expression analysis and shows that the personalized spatial-temporal framework has better performance in cold pain intensity estimation. Significance: This cold pain intensity estimator could allow convenient, automatic, and real-time use to provide continuous objective pain intensity estimations of subjects and patients.