Life detection using ultra-wideband impulse radar is susceptible to various kinds of interference, including dominant background clutter, radio frequency interference (RFI), disturbance caused by the radar hardware, thermal noise, etc. An interference suppression algorithm based on eigendecomposition is proposed. In the fast-time domain, the proposed algorithm has the ability to remove the interferences in the radar operating band. In the slow-time domain, the proposed algorithm can suppress the interferences in the respiratory signal frequency band, 0.17 to 2HZ. Experimental results demonstrate that the proposed algorithm further improves SNR without respiratory signal suppression.
Pulse transit time (PTT) has received considerable attention for noninvasive cuffless blood pressure measurement. However, this approach is inconvenient to deploy in wearable devices because two sensors are required for collecting two-channel physiological signals, such as electrocardiogram and pulse wave signals. In this study, we investigated the pressure pulse wave (PPW) signals collected from one piezoelectric-induced sensor located at a single site for cuffless blood pressure estimation. Twenty-one features were extracted from PPW that collected from the radial artery, and then a linear regression method was used to develop blood pressure estimation models by using the extracted PPW features. Sixty-five middle-aged and elderly participants were recruited to evaluate the performance of the constructed blood pressure estimation models, with oscillometric technique-based blood pressure as a reference. The experimental results indicated that the mean ± standard deviation errors for the estimated systolic blood pressure and diastolic blood pressure were 0.70 ± 7.78 mmHg and 0.83 ± 5.45 mmHg, which achieved a decrease of 1.33 ± 0.37 mmHg in systolic blood pressure and 1.14 ± 0.20 mmHg in diastolic blood pressure, compared with the conventional PTT-based method. The proposed model also demonstrated a high level of robustness in a maximum 60-day follow-up study. These results indicated that PPW obtained from the piezoelectric sensor has great feasibility for cuffless blood pressure estimation, and could serve as a promising method in home healthcare settings.
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 $\pm$ 6.5 mmHg for diastolic BP (DBP) and 0.7 $\pm$ 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are $-$ 0.4 $\pm$ 7.0 mmHg for DBP and $-$ 0.4 $\pm$ 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements. The dataset and source code are available at https://github.com/zdzdliu/HGCTNet.
Tip-tilt stage, also known as fast steering mirror, is a key assembly for many fine tuning applications. However, high performance tip-tilt stage often encounters the problem of parasitic error when moving in the desired directions. To address this problem, this paper presented the design and analysis of a novel tiptilt stage based on compliant remote center of motion mechanism. The proposed mechanism is proven to have less parasitic motion and with better pointing accuracy, meanwhile can suspend a relatively large payload (mirror). In this paper, the design details of the stage is elaborated, both theoretical analysis and numerical simulations are conducted to optimize and validate the design. The results indicate that the proposed tip-tilt stage can drive a 2-in size payload with 3nrad resolution and 300Hz bandwidth within 2mrad range in both axes. The proposed stage matches well with the requirements of most space applications.
The recent widespread pandemic of COVID-19 has put tremendous pressure on the healthcare system. The deployment of telehealth technology is crucial in solving this problem when patients are mildly ill and need to self-isolate at home or in a specific location. This paper proposes using a single radar sensor to continuously contact-less monitor the patients' vital signals in their daily lives. We use edge computing to handle high-priory tasks and combined cloud infrastructure for further process and storage to provide monitoring and telehealth services. A case study is presented to show how the approach can continuously monitor and recognize high-risk diseases and abnormal activity (e.g., sleep apnea). While an accident occurs, the system could provide fast and accurate emergency services. The work has been compared with a good standard. And the experimental results show that the proposed approach for heart rate (HR) and respiratory rate (RR) detection achieved a Mean Absolute Error (MAE) ± Standard Deviation of Absolute Error (SDAE) of 0.09±1.43 bpm and 0.23±3.23 bpm, respectively. This indicates the radar sensor can provide a high recognition accuracy to meet the requirements for a range of cardiopulmonary function monitoring. This kind of telemedicine service facilitates monitoring the self-isolated subjects to detect and recognize human physical and physiological activities.
Heart rate variability(HRV) is widely used to autonomic nerves system assessment and cardiac disease diagnosis in clinic. The traditional HRV analysis is based on Electrocardiogram (ECG). However, it is more convenient to measure HRV by Photoplethysmography (PPG) in most wearable applications, such as watches. This paper proposed a novel method called Temporal Difference Interval Pixels(TDIP) method to obtain PPG signal through smartphone camera. In the experiment, we compared the cost time of obtaining PPG between traditional Temporal Difference(TD) method and TDIP method. We used these two methods to obtain PPG from 5 minutes' smartphone videos, which was acquired from 10 subjects. The average cost time of these two methods are 152.72 seconds and 82.38 seconds respectively. Then, we compared the time domain parameters the standard deviation of NN intervals (SDNN)and the Root mean square of successive differences between NN intervals (RMSSD) obtained by the proposed method with that obtained by ECG. The Pearson coefficient is 0.91 for SDNN and 0.95 for RMSSD respectively. The result can meet the HRV accuracy requirement, and it also significantly reduces the amount of calculation to improve the real-time performance.
We developed a ballistocardiography (BCG)-based Internet-of-Medical-Things (IoMT) system for remote monitoring of cardiopulmonary health. The system composes of BCG sensor, edge node, and cloud platform. To improve computational efficiency and system stability, the system adopted collaborative computing between edge nodes and cloud platforms. Edge nodes undertake signal processing tasks, namely approximate entropy for signal quality assessment, a lifting wavelet scheme for separating the BCG and respiration signal, and the lightweight BCG and respiration signal peaks detection. Heart rate variability (HRV), respiratory rate variability (RRV) analysis and other intelligent computing are performed on cloud platform. In experiments with 25 participants, the proposed method achieved a mean absolute error (MAE)±standard deviation of absolute error (SDAE) of 9.6±8.2 ms for heartbeat intervals detection, and a MAE±SDAE of 22.4±31.1 ms for respiration intervals detection. To study the recovery of cardiopulmonary function in patients with coronavirus disease 2019 (COVID-19), this study recruited 186 discharged patients with COVID-19 and 186 control volunteers. The results indicate that the recovery performance of the respiratory rhythm is better than the heart rhythm among discharged patients with COVID-19. This reminds the patients to be aware of the risk of cardiovascular disease after recovering from COVID-19. Therefore, our remote monitoring system has the ability to play a major role in the follow up and management of discharged patients with COVID-19.
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