# Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training

arXiv: https://arxiv.org/abs/2101.03251 · HTML: https://mlsanity.com/paper/2101.03251/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One](https://mlsanity.com/paper/1912.03263/) (2019)
- [S3FD: Single Shot Scale-invariant Face Detector](https://mlsanity.com/paper/1708.05237/) (2017)
- [Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing](https://mlsanity.com/paper/1704.03067/) (2017)
- [AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection](https://mlsanity.com/paper/1812.05788/) (2018)
- [Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video](https://mlsanity.com/paper/1605.00894/) (2016)
- [Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia](https://mlsanity.com/paper/1905.07446/) (2019)
- [Automated Pain Detection from Facial Expressions using FACS: A Review](https://mlsanity.com/paper/1811.07988/) (2018)
- [Multi-Channel Neural Network for Assessing Neonatal Pain from Videos](https://mlsanity.com/paper/1908.09254/) (2019)
- [Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation](https://mlsanity.com/paper/1701.04540/) (2017)
- [Toward an Automatic System for Computer-Aided Assessment in Facial Palsy](https://mlsanity.com/paper/1910.11497/) (2019)
- [Deep Spatiotemporal Representation of the Face for Automatic Pain Intensity Estimation](https://mlsanity.com/paper/1806.06793/) (2018)

## Built on by (most cited first, 6 of 6)

- [Intelligent Sight and Sound: A Chronic Cancer Pain Dataset](https://mlsanity.com/paper/2204.04214/) (2022)
- [SynPAIN: A Synthetic Dataset of Pain and Non-Pain Facial Expressions](https://mlsanity.com/paper/2507.19673/) (2025)
- [Fully-attentive and interpretable: vision and video vision transformers for pain detection](https://mlsanity.com/paper/2210.15769/) (2022)
- [Pain Detection in Masked Faces during Procedural Sedation](https://mlsanity.com/paper/2211.06694/) (2022)
- [Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment](https://mlsanity.com/paper/2509.16727/) (2025)
- [LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training](https://mlsanity.com/paper/2509.20786/) (2025)

## Abstract (arXiv)

Although pain is frequent in old age, older adults are often undertreated for pain. This is especially the case for long-term care residents with moderate to severe dementia who cannot report their pain because of cognitive impairments that accompany dementia. Nursing staff acknowledge the challenges of effectively recognizing and managing pain in long-term care facilities due to lack of human resources and, sometimes, expertise to use validated pain assessment approaches on a regular basis. Vision-based ambient monitoring will allow for frequent automated assessments so care staff could be automatically notified when signs of pain are displayed. However, existing computer vision techniques for pain detection are not validated on faces of older adults or people with dementia, and this population is not represented in existing facial expression datasets of pain. We present the first fully automated vision-based technique validated on a dementia cohort. Our contributions are threefold. First, we develop a deep learning-based computer vision system for detecting painful facial expressions on a video dataset that is collected unobtrusively from older adult participants with and without dementia. Second, we introduce a pairwise comparative inference method that calibrates to each person and is sensitive to changes in facial expression while using training data more efficiently than sequence models. Third, we introduce a fast contrastive training method that improves cross-dataset performance. Our pain estimation model outperforms baselines by a wide margin, especially when evaluated on faces of people with dementia. Pre-trained model and demo code available at https://github.com/TaatiTeam/pain_detection_demo