# Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging

arXiv: https://arxiv.org/abs/2609.05550 · HTML: https://mlsanity.com/paper/2609.05550/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Eigen-CAM: Class Activation Map using Principal Components](https://mlsanity.com/paper/2008.00299/) (2020)
- [TDN: Temporal Difference Networks for Efficient Action Recognition](https://mlsanity.com/paper/2012.10071/) (2020)
- [TEA: Temporal Excitation and Aggregation for Action Recognition](https://mlsanity.com/paper/2004.01398/) (2020)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [Algorithmic Principles of Camera-based Respiratory Motion Extraction](https://mlsanity.com/paper/2105.07537/) (2021)
- [Privacy-Preserving Remote Heart Rate Estimation from Facial Videos](https://mlsanity.com/paper/2306.01141/) (2023)
- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)

## Abstract (arXiv)

Near-infrared (NIR) video is a promising modality for contactless sleep monitoring, but recent video-based sleep staging methods often use it as a route to reconstructed respiratory/cardiac proxies or cross-modal physiological representations. We study video-only sleep staging under labels defined by polysomnography (PSG), where the model infers sleep stages from NIR video alone without explicit physiological proxy reconstruction or auxiliary physiological signal supervision. This tests whether NIR video itself can provide informative sleep-stage evidence, rather than only serving as an input for recovering physiological proxies. We propose ViNUSS (Video-Native Unmediated Sleep Staging), a framework that combines subject-relative micro-motion learning with full-night sleep dynamics modeling. Spatially anchored pre-spatial micro-motion encoding preserves localized temporal variation together with its spatial context. Within-subject stage contrast learns stage cues with respect to each subject's night-specific baseline. Two-scale sleep dynamics modeling captures within-epoch motion evolution and organizes epoch-level evidence into a coherent full-night sleep-stage trajectory. On 475 overnight NIR recordings (~3,250 hours), ViNUSS achieves 0.80 accuracy and 0.78 macro-F1 for four-class sleep staging. Interpretability analysis suggests attention to thoraco-abdominal periodic motion and gross body movements associated with arousals and position changes. These results support NIR video as an independently informative and complementary modality for PSG-defined sleep-stage estimation