ArticleSensors (Basel, Switzerland)2016
Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sensing Systems for Respiration Monitoring: A Technical Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection.Sensors (Basel, Switzerland) · 2026Article
- Finger-Individuating Exoskeleton System with Non-Contact Leader-Follower Control Strategy.Bioengineering (Basel, Switzerland) · 2024Article
- Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera.Sensors (Basel, Switzerland) · 2023Article
- AAPM task group report 302: Surface-guided radiotherapy.Medical physics · 2022Review
- Automated Non-Contact Respiratory Rate Monitoring of Neonates Based on Synchronous Evaluation of a 3D Time-of-Flight Camera and a Microwave Interferometric Radar Sensor.Sensors (Basel, Switzerland) · 2021Article
- Noncontact Respiratory Monitoring Using Depth Sensing Cameras: A Review of Current Literature.Sensors (Basel, Switzerland) · 2021Review
- A Review on the Use of Microsoft Kinect for Gait Abnormality and Postural Disorder Assessment.Journal of healthcare engineering · 2021Review
- Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration.Sensors (Basel, Switzerland) · 2020Review
- Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition.Sensors (Basel, Switzerland) · 2020Article
- A Clinically Evaluated Interferometric Continuous-Wave Radar System for the Contactless Measurement of Human Vital Parameters.Sensors (Basel, Switzerland) · 2019Article
- Automatic Respiratory Gating Hepatic DCEUS-based Dual-phase Multi-parametric Functional Perfusion Imaging using a Derivative Principal Component Analysis.Theranostics · 2019Article
- Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller.Sensors (Basel, Switzerland) · 2018Review
- Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data.Sensors (Basel, Switzerland) · 2018Article
- Human Identification by Cross-Correlation and Pattern Matching of Personalized Heartbeat: Influence of ECG Leads and Reference Database Size.Sensors (Basel, Switzerland) · 2018Article
- Article
- Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor.Sensors (Basel, Switzerland) · 2017Article
- Real-Time External Respiratory Motion Measuring Technique Using an RGB-D Camera and Principal Component Analysis.Sensors (Basel, Switzerland) · 2017Article
- Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices.Sensors (Basel, Switzerland) · 2017Article
- Breathing Analysis Using Thermal and Depth Imaging Camera Video Records.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.