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

• URL: http://arxiv.org/abs/2609.05550v1
• Date: Thu, 03 Sep 2026 06:47:26 GMT
• Title: Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging
• Abstract summary: Near-infrared (NIR) video is a promising modality for contactless sleep monitoring.<n>Recent video-based sleep staging methods often use it as a route to reconstructed respiratory/cardiac proxies.<n>We propose ViNUSS (Video-Native Unmediated Sleep Staging), a framework that combines subject-relative micro-motion learning with full-night sleep dynamics modeling.
• Score: 3.6257074818543007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics [13.009463605294604]
  We introduce Omni-Sleep, a sleep foundation model that uses the CNS/ANS partition as a physiological prior for representation learning.<n>Pre-trained on over 100,000 hours of multi-center multimodal PSG data, Omni-Sleep is evaluated on sleep staging and multi-disease classification.
  arXiv  Detail & Related papers  (2026-07-04T13:45:39Z)
• Towards Athlete Fatigue Assessment from Association Football Videos [53.20057980542362]
  This paper builds on state-of-theart Game State Reconstruction methods to extract player trajectories in pitch coordinates.<n>We then construct acceleration--speed profiles from these signals and analyze their behavior as fatigue-related performance indicators.<n>Our results indicate that monocular broadcast videos can recover kinematic patterns that are compatible with A-S-temporal while also revealing sensitivity to trajectory noise, calibration errors, and temporal discontinuities inherent to broadcast footage.
  arXiv  Detail & Related papers  (2026-04-07T09:36:28Z)
• Scaling Video Pretraining for Surgical Foundation Models [51.92777479821822]
  SurgRec is a scalable and reproducible pretraining recipe for surgical video understanding.<n>We curate a large corpus of 10,535 videos and 214.5M frames spanning endoscopy, laparoscopy, cataract, and robotic surgery.
  arXiv  Detail & Related papers  (2026-03-31T16:31:25Z)
• PhysNeXt: Next-Generation Dual-Branch Structured Attention Fusion Network for Remote Photoplethysmography Measurement [50.524262997433546]
  rmography enables measurement of heart rate and other vital signs by analyzing color variations in facial skin induced by cardiac pulsation.<n>Current methods are mainly based on either end-to-end modeling from raw videos or subtle heartbeat-related map (ST) representations.<n>We propose PhysMapXt, a dual-input deep learning framework that jointly exploits video frames and ST representations.
  arXiv  Detail & Related papers  (2026-03-20T08:37:02Z)
• sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals [15.989293188183069]
  We present textttsleep2vec, a foundation model for diverse and incomplete nocturnal biosignals.<n>textttsleep2vec is contrastively pre-trained on 42,249 overnight recordings spanning nine modalities.<n>On downstream sleep staging and clinical outcome assessment, ttexttsleep2vec consistently outperforms strong baselines.
  arXiv  Detail & Related papers  (2026-02-14T19:40:04Z)
• Physiology as Language: Translating Respiration to Sleep EEG [43.02603129452492]
  This paper introduces a novel cross-physiology translation task: synthesizing sleep electroencephalography (EEG) from respiration signals.<n>We propose a waveform-conditional generative framework that preserves fine-grained respiratory dynamics while constraining the EEG target space through discrete tokenization.<n>Trained on over 28,000 individuals, our model achieves a 7% Mean Absolute Error in EEG spectrogram reconstruction.
  arXiv  Detail & Related papers  (2026-01-31T05:44:45Z)
• Learning Cortico-Muscular Dependence through Orthonormal Decomposition of Density Ratios [39.3721526159124]
  We present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations.<n>We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects.
  arXiv  Detail & Related papers  (2024-10-04T16:05:08Z)
• What Radio Waves Tell Us about Sleep [34.690382091650314]
  We develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. We show that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM) and detects sleep apnea (AUROC = 0.88) The model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders.
  arXiv  Detail & Related papers  (2024-05-20T02:41:21Z)
• SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers [0.6599755599064447]
  We introduce SleepVST, a transformer model which enables state-of-the-art performance in camera-based sleep stage classification. We show that SleepVST can be successfully transferred to cardio-respiratory waveforms extracted from video, enabling fully contact-free sleep staging.
  arXiv  Detail & Related papers  (2024-04-04T23:24:14Z)
• Diffusion Priors for Dynamic View Synthesis from Monocular Videos [59.42406064983643]
  Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. We first finetune a pretrained RGB-D diffusion model on the video frames using a customization technique. We distill the knowledge from the finetuned model to a 4D representations encompassing both dynamic and static Neural Radiance Fields.
  arXiv  Detail & Related papers  (2024-01-10T23:26:41Z)
• Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera [1.0499611180329802]
  We use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We achieve an accuracy of 73.4% and a Cohen's kappa of 0.61 in four-class sleep stage classification.
  arXiv  Detail & Related papers  (2023-06-06T14:21:22Z)
• Automatic Sleep Stage Classification with Cross-modal Self-supervised Features from Deep Brain Signals [35.141080847207355]
  Sleep stage classification based on deep brain recording has great potential to provide more precise treatment for patients. We proposed an applicable cross-modal transfer learning method for sleep stage classification with implanted devices. We tested the model with deep brain recording data from 12 patients with Parkinson's disease.
  arXiv  Detail & Related papers  (2023-02-07T03:21:33Z)
• ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation [4.014524824655106]
  This work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint-temporal graphs. The proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.
  arXiv  Detail & Related papers  (2022-12-09T14:34:58Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• Automatic detection of microsleep episodes with deep learning [55.41644538483948]
  Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs) maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. MSEs are mostly not considered in the absence of established scoring criteria defining MSEs. We aimed for automatic detection of MSEs with machine learning based on raw EEG and EOG data as input.
  arXiv  Detail & Related papers  (2020-09-07T11:38:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.