Non-contact Pain Recognition from Video Sequences with Remote Physiological Measurements Prediction
Authors: Ruijing Yang, Ziyu Guan, Zitong Yu, Xiaoyi Feng, Jinye Peng, Guoying Zhao
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments and Results |
| Researcher Affiliation | Academia | 1Northwest University 2University of Oulu 3Northwestern Polytechnical University |
| Pseudocode | No | The paper describes the architecture and modules (r STAN, STA, VFE, Deep-r PPG) but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate the performance of the proposed method on: the Bio Vid Heat Pain Database (the Bio Vid for short) [Walter et al., 2013] and the UNBC-Mc Master Shoulder Pain Expression Archive Database (the UNBC for short) [Lucey et al., 2011]. |
| Dataset Splits | Yes | For the parameter selection, we randomly split all the 87 subjects into 5 folds and use 5-fold cross-validation to determine the best parameters. While for the comparisons to the state of the arts, we follow the leave-one-subject-out protocol (LOO) as previous works [Werner et al., 2014; Werner et al., 2016]. |
| Hardware Specification | Yes | The proposed method is trained on Nvidia P100 using Py Torch. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'MTCNN' as software used, but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Adam is used as the optimizer with a learning rate of 2e 4 which is decayed after 10 epochs with a multiplicative factor gamma=0.8. To learn the parameters of the two branches more efficiently, we train them separately and fine-tune the whole framework jointly. For the input videos, the original video is downsampled to L = 64 since it produces the best performance. |