Multi-task Neural Networks for Personalized Pain Recognition from Physiological Signals

TL;DR

This paper introduces a multi-task neural network approach for personalized pain recognition using physiological signals, addressing individual differences and measurement challenges in pain assessment.

Contribution

It presents a novel multi-task learning method that personalizes pain detection from physiological data, improving over generic models.

Findings

Effective in recognizing pain intensity from physiological signals.

Accounts for individual differences in pain responses.

Demonstrates promising results on a multi-modal dataset.

Abstract

Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.