Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals

• URL: http://arxiv.org/abs/2311.01367v1
• Date: Thu, 2 Nov 2023 16:23:13 GMT
• Title: Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals
• Authors: Md Zobaer Islam, Brenden Martin, Carly Gotcher, Tyler Martinez, John F. O'Hara, Sabit Ekin
• Abstract summary: We present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot. The developed system can be utilized at home or healthcare facilities as a smart, non-contact and discreet respiration monitoring method.
• Score: 0.18641315013048293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous light source (e.g., infrared light emitting diode) and sensor (e.g., photodetector). This light-wave sensing (LWS) system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range. The anomaly detection model demonstrates up to 96.6% average accuracy in classifying 7 different types of breathing data using machine learning. The model can also detect faulty data collected by the system that does not contain breathing information. The developed system can be utilized at home or healthcare facilities as a smart, non-contact and discreet respiration monitoring method.

Related papers

• AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones [6.927186681726127]
  Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria. Current mobile-based AF detection systems offer a portable solution. We present AcousAF, a novel AF detection system based on acoustic sensors of smartphones.
  arXiv  Detail & Related papers  (2024-08-09T07:43:16Z)
• Non-Contact Breathing Rate Detection Using Optical Flow [0.0]
  Breathing rate is a vital health metric that is an invaluable indicator of the overall health of a person. This paper presents an investigation into a method of non-contact breathing rate detection using a motion detection algorithm, optical flow.
  arXiv  Detail & Related papers  (2023-11-13T10:26:18Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing [0.1759252234439348]
  Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. This study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Time-series respiration data were collected using infrared light-wave sensing technology.
  arXiv  Detail & Related papers  (2023-01-09T23:19:40Z)
• Near-infrared and visible-light periocular recognition with Gabor features using frequency-adaptive automatic eye detection [69.35569554213679]
  Periocular recognition has gained attention recently due to demands of increased robustness of face or iris in less controlled scenarios. We present a new system for eye detection based on complex symmetry filters, which has the advantage of not needing training. This system is used as input to a periocular algorithm based on retinotopic sampling grids and Gabor spectrum decomposition.
  arXiv  Detail & Related papers  (2022-11-10T13:04:03Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Identification of deep breath while moving forward based on multiple body regions and graph signal analysis [45.62293065676075]
  This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. In validation experiments, the proposed approach outperforms the comparative methods with the accuracy, precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2%, respectively.
  arXiv  Detail & Related papers  (2020-10-20T08:26:50Z)
• Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea [7.300192965401497]
  Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. We present a novel algorithm based on multispectral data fusion that aims at estimating respiratory rate (RR) during apnea. Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.
  arXiv  Detail & Related papers  (2020-04-21T09:07:09Z)
• DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices [0.0]
  We develop a multi-task deep learning method to assess signal quality and arrhythmia event detection in wearable photoplethysmography devices for real-time detection of atrial fibrillation (AF) We train our algorithm on over one million simulated unlabeled physiological signals and fine-tune on a curated dataset of over 500K labeled signals from over 100 individuals from 3 different wearable devices. We show that two-stage training can help address the unbalanced data problem common to biomedical applications where large well-annotated datasets are scarce.
  arXiv  Detail & Related papers  (2020-01-01T07:41:28Z)

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.