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Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications.

 

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 an attempt to understand human physiological signals when an individual is subjected to pain, we set up a tonic pain experiment in a laboratory setting. The subjects’ physiological signals were recorded, timestamped, and compared to an initial 30 second baseline measurement. Subjects were also asked to verbally state their level of pain based on a visual analog scale in order to compare reported pain levels with physiological signals. The physiological signals measured were: Electroencephalography (EEG), Pupillary Unrest Under Ambient Light (PUAL), Skin Conductance (SC), Electromyography (EMG), Respiration Rate (RR), Blood Volume Pulse (BVP), Skin Temperature (ST), Blood Pressure (BP), and Facial Expression (FE). ANOVA and frequency domain analyses were conducted on the data in order to determine whether there was a significant difference between the ‘pain’ and ‘no pain’ (baseline) states of an individual. Based on our results, skin conductance, PUAL, facial expression, and EEG signals were theorized to be good signals for the classification of tonic pain, or any pain applied directly to an individual.