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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DeepVS: A Deep Learning Approach For RF-based Vital Signs Sensing
Vital signs (e.g., heart and respiratory rate) are indicative for health status assessment. Efforts have been made to extract vital signs using radio frequency (RF) techniques (e.g., Wi-Fi, FMCW, UWB), which offer a non-touch solution for continuous and ubiquitous monitoring without users’ cooperative efforts. While RF-based vital signs monitoring is user-friendly, its robustness faces two challenges. On the one hand, the RF signal is modulated by the periodic chest wall displacement due to heartbeat and breathing in a nonlinear manner. It is inherently hard to identify the fundamental heart and respiratory rates (HR and RR) in the presence of higher order harmonics of them and intermodulation between HR and RR, especially when they have overlapping frequency bands. On the other hand, the inadvertent body movements may disturb and distort the RF signal, overwhelming the vital signals, thus inhibiting the parameter estimation of the physiological movement (i.e., heartbeat and breathing). In this paper, we propose DeepVS, a deep learning approach that addresses the aforementioned challenges from the non-linearity and inadvertent movements for robust RF-based vital signs sensing in a unified manner. DeepVS combines 1D CNN and
attention models to exploit local features and temporal correlations. Moreover, it leverages a two-stream scheme to integrate features from both time and frequency domains. Additionally, DeepVS unifies the estimation of HR and RR with a multi-head structure, which only adds limited extra overhead (<1%) to the existing model, compared to doubling the overhead using two separate models for HR and RR respectively. Our experiments demonstrate that DeepVS achieves 80-percentile HR/RR errors of 7.4/4.9 beat/breaths per
minute (bpm) on a challenging dataset, as compared to 11.8/7.3 bpm of a non-learning solution. Besides, an ablation study has been conducted to quantify the effectiveness of DeepVS.
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- Award ID(s):
- 2119299
- NSF-PAR ID:
- 10356921
- Date Published:
- Journal Name:
- In 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB ’22)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3535508.3545554
