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This demonstration presents a working prototype of VitalHub, a practical solution for longitudinal in-home vital signs monitoring. To balance the trade-offs between the challenges related to an individual’s efforts thus compliance, and robustness with vital signs monitoring, we introduce a passive monitoring solution, which is free of any on-body device or cooperative efforts from the user. By fusing the inputs from a pair of co-located UWB and depth sensors, VitalHub achieves robust, passive, context-aware and privacy-preserving sensing. We use a COTS UWB sensor to detect chest wall displacement due to the respiration and heartbeat for vital signs extraction. We use the depth information from Microsoft Kinect to detect and locate the users in the field of view and recognize the activities of the respective users for further analysis. We have tested the prototype extensively in engineering and medical lab environments. We will demonstrate the features and performance of VitalHub using real-world data in comparison with an FDA approved medical device.
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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 paper, 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 signs 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 signs monitoring.
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- Award ID(s):
- 1951880
- PAR ID:
- 10439717
- Date Published:
- Journal Name:
- ACM transactions on computing for healthcare
- ISSN:
- 2637-8051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This demonstration presents a working prototype of VitalHub, a practical solution for longitudinal in-home vital signs monitoring. To balance the trade-offs between the challenges related to an individual’s efforts thus compliance, and robustness with vital signs monitoring, we introduce a passive monitoring solution, which is free of any on-body device or cooperative efforts from the user. By fusing the inputs from a pair of co-located UWB and depth sensors, VitalHub achieves robust, passive, context-aware and privacy-preserving sensing. We use a COTS UWB sensor to detect chest wall displacement due to the respiration and heartbeat for vital signs extraction. We use the depth information from Microsoft Kinect to detect and locate the users in the field of view and recognize the activities of the respective users for further analysis.We have tested the prototype extensively in engineering and medical lab environments. We will demonstrate the features and performance of VitalHub using realworld data in comparison with an FDA approved medical device.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3549941
