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Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion

Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Computing for Healthcare (HEALTH) Association for Computing Machinery

Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Association for Computing Machinery.
ISSN
2691-1957
eISSN
2637-8051
DOI
10.1145/3549941
Publisher site
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Abstract

Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.

Journal

ACM Transactions on Computing for Healthcare (HEALTH) – Association for Computing Machinery

Published: Nov 3, 2022

Keywords: Non-touch vital signs monitoring

References

  • d
    [,
  • d
    [,
  • UbiBreathe: A ubiquitous non-invasive WiFi-based breathing estimator
    [,
  • Capturing the human figure through a wall
    [,
  • Smart homes that monitor breathing and heart rate
    [,
  • Zephyr: Ubiquitous accurate multi-sensor fusion-based respiratory rate estimation using smartphones
    [,
  • A 118-mW pulse-based radar SoC in 55-nm CMOS for non-contact human vital signs detection
    [,
  • Comparative study of radar architectures for human vital signs measurement
    [,
  • Active noise cancellation using MEMS accelerometers for motion-tolerant wearable bio-sensors
    [,
  • A stacked LSTM-based approach for reducing semantic pose estimation error
    [,
  • Bates’ Guide to Physical Examination and History-taking
    [,
  • Realtime multi-person 2D pose estimation using part affinity fields
    [,
  • Tracking free-weight exercises
    [,
  • EAR: Exploiting uncontrollable ambient RF signals in heterogeneous networks for gesture recognition
    [,
  • 60-GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring
    [,
  • Independent component analysis, a new concept? Signal Processing 36, 3 (1994), 287–314
    [,
  • Handbook of Blind Source Separation: Independent Component Analysis and Applications
    [,
  • A tutorial on the cross-entropy method
    [,
  • Chest wall motion during tidal breathing
    [,
  • Hierarchical recurrent neural network for skeleton based action recognition
    [,
  • On the estimation of respiration and heart rates via an IR-UWB radar: An algorithmic perspective
    [,
  • Electrocardiogram signals de-noising using lifting-based discrete wavelet transform
    [,
  • BodyScan: Enabling radio-based sensing on wearable devices for contactless activity and vital sign monitoring
    [,
  • HappyFeet: Recognizing and assessing dance on the floor
    [,
  • Normality tests for statistical analysis: A guide for non-statisticians
    [,
  • Clinical diagnosis of pediatric obstructive sleep apnea validated by polysomnography
    [,
  • A hybrid radar-camera sensing system with phase compensation for random body movement cancellation in Doppler vital sign detection
    [,
  • Deep residual learning for image recognition
    [,
  • Experience: Cross-technology radio respiratory monitoring performance study
    [,
  • Long short-term memory
    [,
  • RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering
    [,
  • VitaMon: Measuring heart rate variability using smartphone front camera
    [,
  • [n
    [,
  • The impact of digital filtering to ECG analysis: Butterworth filter application
    [,
  • Monitoring a person’s heart rate and respiratory rate on a shared bed using geophones
    [,
  • A detailed algorithm for vital sign monitoring of a stationary/non-stationary human through IR-UWB radar
    [,
  • Adam: A method for stochastic optimization
    [,
  • Laser monitoring of chest wall displacement
    [,
  • Analysis of vital signs monitoring using an IR-UWB radar
    [,
  • Recent advances in Doppler radar sensors for pervasive healthcare monitoring
    [,
  • Practical human sensing in the light
    [,
  • Deep learning for rfid-based activity recognition
    [,
  • Improved denoising method for through-wall vital sign detection using UWB impulse radar
    [,
  • Cardiac scan: A non-contact and continuous heart-based user authentication system
    [,
  • Tracking vital signs during sleep leveraging off-the-shelf WiFi
    [,
  • Contactless respiration monitoring via off-the-shelf WiFi devices
    [,
  • Caesar: Cross-camera complex activity recognition
    [,
  • d
    [,
  • Heartsense: Ubiquitous accurate multi-modal fusion-based heart rate estimation using smartphones
    [,
  • An electromagnetic model of human vital signs detection and its experimental validation
    [,
  • Contactless sleep apnea detection on smartphones
    [,
  • Continuous and fine-grained breathing volume monitoring from afar using wireless signals
    [,
  • Spectrum-averaged harmonic path (SHAPA) algorithm for non-contact vital sign monitoring with ultra-wideband (UWB) radar
    [,
  • RGBD-camera based get-up event detection for hospital fall prevention
    [,
  • d
    [,
  • Risq: Recognizing smoking gestures with inertial sensors on a wristband
    [,
  • Glasses for the third eye: Improving the quality of clinical data analysis with motion sensor-based data filtering
    [,
  • Breathfinding: A wireless network that monitors and locates breathing in a home
    [,
  • Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography
    [,
  • Acousticcardiogram: Monitoring heartbeats using acoustic signals on smart devices
    [,
  • WiBreathe: Estimating respiration rate using wireless signals in natural settings in the home
    [,
  • Video pulse rate variability analysis in stationary and motion conditions
    [,
  • Moving human respiration sign detection using mm-wave radar via motion path reconstruction
    [,
  • Harmonics-based multiple heartbeat detection at equal distance using UWB impulse radar
    [,
  • How does batch normalization help optimization? Advances in Neural Information Processing Systems 31 (2018)
    [,
  • FaceNet: A unified embedding for face recognition and clustering
    [,
  • Human skeleton tracking from depth data using geodesic distances and optical flow
    [,
  • Surface chest motion decomposition for cardiovascular monitoring
    [,
  • Weighted autocorrelation for pitch extraction of noisy speech
    [,
  • Real-time human pose recognition in parts from single depth images
    [,
  • Detection of breathing and heart rates in UWB radar sensor data using FVPIEF-based two-layer EEMD
    [,
  • Respiration rate measurement under 1-D body motion using single continuous-wave Doppler radar vital sign detection system
    [,
  • Multi-user gesture recognition using WiFi
    [,
  • Position and orientation agnostic gesture recognition using WiFi
    [,
  • Contactless infant monitoring using white noise
    [,
  • Experimental comparison of IR-UWB radar and FMCW radar for vital signs
    [,
  • Human respiration detection with commodity WiFi devices: Do user location and body orientation matter?
    [,
  • Understanding and modeling of WiFi signal based human activity recognition
    [,
  • PhaseBeat: Exploiting CSI phase data for vital sign monitoring with commodity WiFi devices
    [,
  • E-eyes: Device-free location-oriented activity identification using fine-grained WiFi signatures
    [,
  • mD-Track: Leveraging multi-dimensionality for passive indoor Wi-Fi tracking
    [,
  • A measurement study of FMCW radar configurations for non-contact vital signs monitoring
    [,
  • DeepVS: A deep learning approach for RF-based vital signs sensing
    [,
  • Fusing UWB and depth sensors for passive and context-aware vital signs monitoring
    [,
  • VitalHub: Robust, non-touch multi-user vital signs monitoring using depth camera-aided UWB
    [,
  • Signal quality detection towards practical non-touch vital sign monitoring
    [,
  • Breathlistener: Fine-grained breathing monitoring in driving environments utilizing acoustic signals
    [,
  • Through-wall multiple targets vital signs tracking based on VMD algorithm
    [,
  • Monitoring vital signs using millimeter wave
    [,
  • Challenges: Device-free passive localization for wireless environments
    [,
  • Extracting multi-person respiration from entangled RF signals
    [,
  • CrossSense: Towards cross-site and large-scale WiFi sensing
    [,
  • Harmonic multiple loop detection (HMLD) algorithm for not-contact vital sign monitoring based on ultra-wideband (UWB) radar
    [,
  • RF-based 3D skeletons
    [,
  • MoRe-Fi: Motion-robust and fine-grained respiration monitoring via deep-learning UWB radar
    [,
  • Zero-effort cross-domain gesture recognition with Wi-Fi
    [,
  • Multi-modal face authentication using deep visual and acoustic features
    [,
  • Robust human face authentication leveraging acoustic sensing on smartphones
    [,