Continuous Pain Related Behavior Recognition from Muscle Activity and Body Movements

Abstract

Chronic pain is a disease that the patients suffers a lot in their daily life and it is difficult to be released completely. It is difficult to manage because pain can come anytime and it is unpredictable. However, the pain can be represented by the pain related behaviors such as guiding and abrupt actions. In this paper, we will develop a machine learning based system that can detect the pain related behaviors from patient’s Electromyography (EMG) signals and body movements continuously. The system includes data collection, feature extraction, modeling and classification. The data were collected using biosensor sensor for EMG and motion capture for body movement. Specific features are extracted from the body movement data. Then Random Forest and a Two Stage Classification (TSC) scheme (KNN coupled with Hidden Markov Model (HMM)) were used for pain related behavior detection in a continuous manner. The proposed method was tested on Emo-Pain corpus dataset provided by UCL and experimental results demonstrate the efficiency of the proposed method.