Recurrent Network based Automatic Detection of Chronic Pain Protective Behavior using MoCap and sEMG data

Abstract

In chronic pain physical rehabilitation, physiotherapists adapt
exercise sessions according to the movement behavior of
patients. As rehabilitation moves beyond clinical sessions,
technology is needed to similarly assess movement behaviors
and provide such personalized support. In this paper, as a first
step, we investigate automatic detection of protective behavior
(movement behavior due to pain-related fear or pain) based on
wearable motion capture and electromyography sensor data. We
investigate two recurrent networks (RNN) referred to as stacked-
LSTM and dual-stream LSTM, which we compare with related
deep learning (DL) architectures. We further explore data
augmentation techniques and additionally analyze the impact of
segmentation window lengths on detection performance. The
leading performance of 0.815 mean F1 score achieved by stacked-
LSTM provides important grounding for the development of
wearable technology to support chronic pain physical
rehabilitation during daily activities.