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Chronic pain is a major cause of disability worldwide. While acute pain may serve as a protective function, chronic pain and the associated changes in neural processing negatively impact function and quality of life. This neural plasticity may include changes to the autonomic nervous system (ANS) potentially detectable as changes in various physiological signals. Our aim is to evaluate differences in the physiological signals reflecting ANS changes, by comparing healthy subjects and patients with chronic low back pain during standardized pain stimuli. We extracted several features from photoplethysmography (PPG), electrodermal activity (EDA), and respiration, both at rest and during a repeated pinprick test. We found significant group differences in some PPG parameters at rest and in response to the repeated pinprick test. Chronic pain patients had consistently higher basal sympathetic activity, as well as a blunted autonomic response when subjected to nociceptive stimuli. 

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.