Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals

Yingzi Lin; Yan Xiao; Li Wang; Yikang Guo; Wenchao Zhu; Biren Dalip; Sagar Kamarthi; Kristin L Schreiber; Robert R Edwards; Richard D Urman

doi:10.3389/fnins.2022.831627

Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals

Front Neurosci. 2022 Feb 11:16:831627. doi: 10.3389/fnins.2022.831627. eCollection 2022.

Authors

Affiliations

¹ Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States.
² College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX, United States.
³ Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard University, Boston, MA, United States.

Abstract

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.

Keywords: machine learning algorithms; multi-modality sensor fusion; pain measurement; physiological signals; sensors.