# Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video

Source: [Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video](<https://www.sota2.com/research/paper/68fa627e1e209131ce9f99a5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:20:33.540Z

Authors: Jing Zhou, Xiaopeng Hong, Fei Su, Guoying Zhao

Published: 2016-05-03T13:17:03.000Z

## Abstract

Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks among adjacent frames. To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation. Given vector sequences of AAM-warped facial images, we used a sliding-window strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to output continuous-valued pain intensity. The proposed end-to-end pain intensity regression framework can predict the pain intensity of each frame by considering a sufficiently large historical frames while limiting the scale of the parameters within the model. Our method achieves promising results regarding both accuracy and running speed on the published UNBC-McMaster Shoulder Pain Expression Archive Database.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Pain intensity estimation on UNBC-McMaster Shoulder-Pain dataset 25-fold leave-one-subject-out (val)](<https://www.sota2.com/research/sota/pain-intensity-estimation-on-unbc-mcmaster-shoulder-pain-dataset-25-fold-leave-one-subject-out-val.md>): Pain intensity estimation; UNBC-McMaster Shoulder-Pain dataset 25-fold leave-one-subject-out (val); MAE: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1605.00894>)

[PDF](<https://arxiv.org/pdf/1605.00894.pdf>)