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1. Fusing UWB and Depth Sensors for Passive and Context-Aware Vital Signs Monitoring

   Xie, Zongxing; Zhou, Bing; Cheng, Xi; Schoenfeld, Elinor; Ye, Fan (January 2021, IEEE ACM Conference on Connected Health Applications, Systems and Engineering Technologies (CHASE))

   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. 

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

   https://doi.org/10.1145/3549941  

   Xie, Zongxing; Zhou, Bing; Cheng, Xi; Schoenfeld, Elinor; Ye, Fan (January 2022, ACM transactions on computing for healthcare)

   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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3. DeepVS: A Deep Learning Approach For RF-based Vital Signs Sensing

   https://doi.org/10.1145/3535508.3545554  

   Xie, Zongxing; Wang, Hanrui; Han, Song; Schoenfeld, Elinor; Ye, Fan (January 2022, In 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB ’22))

   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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4. Measurement Study of FMCW Radar Configurations for Non-contact Vital Signs Monitoring

   https://doi.org/10.1109/RadarConf2248738.2022.9764200  

   Xie, Zongxing; Hua, Yindong; Ye, Fan (January 2022, IEEE Radar Conference)

   Non-contact vital signs monitoring (NCVSM) with radio frequency (RF) is attracting increasing attention due to its non-invasive nature. Recent advances in COTS radar technologies accelerate the development of RF-based solutions. While researchers have implemented and demonstrated the feasibility of NCVSM with diverse radar hardware, most efforts have been focused on devising algorithms to extract vital signs, with limited understanding about the effects of radar configurations. The deficiency in such understanding hinders the design of software defined radar (SDR) optimally customized for NCVSM. In this work, we first hypothesize the effects of FMCW radar configurations using signal-to-interference-plus-noise ratio (SINR) based signal modeling, then we conduct extensive experiments with a COTS FMCW radar, TinyRad, to understand how various parameters impact NCVSM performance compared to a medical device. We find that a larger bandwidth or higher transmitting power in general improves vital sign estimation accuracy; however, coherent processing of consecutive chirps (time diversity) or multiple receiving antennas (space diversity) does not improve the performance. Observations on the baseband (BB) signal show that coherent processing contributes to a higher amplitude but similar phase patterns, whose periodic changes are the key in extracting vital signs. 

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5. SpaceBeat: Identity-aware Multi-person Vital Signs Monitoring Using Commodity WiFi

   https://doi.org/10.1145/3678590  

   Li, Bofan; Ren, Yili; Wang, Yichao; Yang, Jie (August 2024, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Vital signs monitoring has gained increasing attention due to its ability to indicate various human health and well-being conditions. The development of WiFi sensing technologies has made it possible to monitor vital signs using ubiquitous WiFi signals and devices. However, most existing approaches are dedicated to single-person scenarios. A few WiFi sensing approaches can achieve multi-person vital signs monitoring, whereas they are not identity-aware and sensitive to interferences in the environment. In this paper, we propose SpaceBeat, an identity-aware and interference-robust multi-person vital sign monitoring system using commodity WiFi. In particular, our system separates multiple people and locates each person in the spatial domain by leveraging multiple antennas. We analyze the change of signals at the location of each person to achieve identity-aware vital signs monitoring. We also design a contrastive principal component analysis-contrastive learning framework to mitigate interferences caused by other moving people. We evaluate SpaceBeat in various challenging environments, including interference scenarios, non-line-of-sight scenarios, different distances, etc. Our system achieves an average accuracy of 99.1% for breathing monitoring and 97.9% for heartbeat monitoring. 

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