Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge

• URL: http://arxiv.org/abs/2403.06947v1
• Date: Mon, 11 Mar 2024 17:33:25 GMT
• Title: Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge
• Authors: Yuting Zhang, Hao Lu, Xin Liu, Yingcong Chen, Kaishun Wu
• Abstract summary: Remote photoplethysmography (r) is a promising technology that captures physiological signals from face videos. Most existing methods have overlooked the prior knowledge of r, resulting in poor generalization ability. We propose a novel framework that simultaneously utilizes explicit and implicit prior knowledge methods in the r task.
• Score: 30.31568804817144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition. The demand for rPPG tasks has expanded from demonstrating good performance on intra-dataset testing to cross-dataset testing (i.e., domain generalization). However, most existing methods have overlooked the prior knowledge of rPPG, resulting in poor generalization ability. In this paper, we propose a novel framework that simultaneously utilizes explicit and implicit prior knowledge in the rPPG task. Specifically, we systematically analyze the causes of noise sources (e.g., different camera, lighting, skin types, and movement) across different domains and incorporate these prior knowledge into the network. Additionally, we leverage a two-branch network to disentangle the physiological feature distribution from noises through implicit label correlation. Our extensive experiments demonstrate that the proposed method not only outperforms state-of-the-art methods on RGB cross-dataset evaluation but also generalizes well from RGB datasets to NIR datasets. The code is available at https://github.com/keke-nice/Greip.

Related papers

• Deep Homography Estimation for Visual Place Recognition [49.235432979736395]
  We propose a transformer-based deep homography estimation (DHE) network. It takes the dense feature map extracted by a backbone network as input and fits homography for fast and learnable geometric verification. Experiments on benchmark datasets show that our method can outperform several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-25T13:22:17Z)
• A Bipartite Graph is All We Need for Enhancing Emotional Reasoning with Commonsense Knowledge [16.410940528107115]
  We propose a Bipartite Heterogeneous Graph (BHG) method for enhancing emotional reasoning with commonsense knowledge. BHG-based knowledge infusion can be directly generalized to multi-type and multi-grained knowledge sources.
  arXiv  Detail & Related papers  (2023-08-09T09:09:17Z)
• Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG [2.82697733014759]
  r (pg photoplethysmography) is a technology that measures and analyzes BVP (Blood Volume Pulse) by using the light absorption characteristics of hemoglobin captured through a camera. This study is to provide a framework to evaluate various r benchmarking techniques across a wide range of datasets for fair evaluation and comparison.
  arXiv  Detail & Related papers  (2023-07-24T09:35:47Z)
• rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement [36.54109704201048]
  Remote photoplethysmography (r-MAE) is an important technique for perceiving human vital signs. In this paper, we develop a self-supervised framework for extracting inherent self-similar prior in physiological signals. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-r.
  arXiv  Detail & Related papers  (2023-06-04T08:53:28Z)
• Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare [0.757843972001219]
  This paper presents an end-to-end representation learning framework to extract entity-centric KGs from structured and unstructured data. We introduce a star-shaped classifier to represent the multiple facets of a person and use it to guide KG creation. We highlight that this approach has several potential applications across domains and is open-sourced.
  arXiv  Detail & Related papers  (2023-05-09T17:39:45Z)
• DRNet: Decomposition and Reconstruction Network for Remote Physiological Measurement [39.73408626273354]
  Existing methods are generally divided into two groups. The first focuses on mining the subtle volume pulse (BVP) signals from face videos, but seldom explicitly models the noises that dominate face video content. The second focuses on modeling noisy data directly, resulting in suboptimal performance due to the lack of regularity of these severe random noises.
  arXiv  Detail & Related papers  (2022-06-12T07:40:10Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• Complex-valued Federated Learning with Differential Privacy and MRI Applications [51.34714485616763]
  We introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of $f$-DP, $(varepsilon, delta)$-DP and R'enyi-DP. We present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task.
  arXiv  Detail & Related papers  (2021-10-07T14:03:00Z)
• Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview [89.09270073549182]
  Terahertz (THz) signal generation and radiation methods are shaping the future of wireless systems. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. We present an overview of these techniques, with an emphasis on signal pre-processing. We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band.
  arXiv  Detail & Related papers  (2021-04-09T01:38:34Z)
• SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization [64.56399911605286]
  We propose SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module. SumGNN outperforms the best baseline by up to 5.54%, and the performance gain is particularly significant in low data relation types.
  arXiv  Detail & Related papers  (2020-10-04T00:14:57Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

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.