# SFF-DA: Sptialtemporal Feature Fusion for Detecting Anxiety Nonintrusively

arXiv: https://arxiv.org/abs/2208.06411 · HTML: https://mlsanity.com/paper/2208.06411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 71 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Rethinking pooling in graph neural networks](https://mlsanity.com/paper/2010.11418/) (2020)
- [Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification](https://mlsanity.com/paper/1803.09882/) (2018)
- [Instant Automated Inference of Perceived Mental Stress through Smartphone PPG and Thermal Imaging](https://mlsanity.com/paper/1901.00449/) (2018)
- [Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms](https://mlsanity.com/paper/2011.08482/) (2020)

## Abstract (arXiv)

Early detection of anxiety is crucial for reducing the suffering of individuals with mental disorders and improving treatment outcomes. Utilizing an mHealth platform for anxiety screening can be particularly practical in improving screening efficiency and reducing costs. However, the effectiveness of existing methods has been hindered by differences in mobile devices used to capture subjects' physical and mental evaluations, as well as by the variability in data quality and small sample size problems encountered in real-world settings. To address these issues, we propose a framework with spatiotemporal feature fusion for detecting anxiety nonintrusively. We use a feature extraction network based on a 3D convolutional network and long short-term memory ("3DCNN+LSTM") to fuse the spatiotemporal features of facial behavior and noncontact physiology, which reduces the impact of uneven data quality. Additionally, we design a similarity assessment strategy to address the issue of deteriorating model accuracy due to small sample sizes. Our framework is validated with a crew dataset from the real world and two public datasets: the University of Burgundy Franche-Comt'e Psychophysiological (UBFC-Phys) dataset and the Smart Reasoning for Well-being at Home and at Work for Knowledge Work (SWELL-KW) dataset. The experimental results indicate that our framework outperforms the comparison methods.