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• We investigate the application of the contactless BCG signal in emotion recognition. Emotion recognition is a major research point in the field of affective computing. Existing research on the application of physiological signals to emotion recognition mainly focuses on the processing of contact signals. However, there are issues with contact signal acquisition equipment, such as limited portability and poor user compliance, which make it difficult to promote its use. To explore a new method for emotion recognition based on contactless ballistocardiogram (BCG), we proposed a SE-CNN model with a multi-class focal loss function. To construct the dataset, we used audio-visual stimuli to evoke the subjects' emotions and collected data on the subjects' three discrete emotions, positive, neutral, and negative, through our established BCG signal acquisition system based on a piezoelectric ceramics sensor. Root mean square filter and thresholding were used to detect and eliminate motion artifacts of BCG signals. We did two kinds of preprocessing on BCG signals: wavelet transform and bandpass filtering, to explore the effect of different components of BCG on emotion recognition. Subsequently, we verified the model's performance and cross-time working ability through traditional K-Fold and our proposed K-Session cross-validation methods. The results showed that the band-pass filtering method was more beneficial to the current classification task. Under K-Fold cross-validation, the model's accuracy, precision, and recall were 97.21%, 97.00%, and 97.11%. Under K-Session cross-validation, the model's accuracy, precision, and recall were 94.66%, 93.92%, and 94.86%, respectively, all of which were better than the classification effect of synchronous ECG. The reliability of BCG in contactless emotion recognition was proved. [ABSTRACT FROM AUTHOR]

Silent speech recognition (SSR) is a system that can recognize verbal expressions when speech signals are not accessible. It has found versatile applications not only in helping those who suffer a speech impediment, but also in human–computer interaction. In this paper, we propose an SSR system based on surface electromyography (sEMG) signals. We first design a Mandarin corpus which contains 135-class utterances and collect 8-channel sEMG data from 12 subjects. After preprocessing the raw sEMG signals, we extract a time–frequency hybrid feature map for each sample. Then, we use two data augmentation strategies to obtain more data in order to improve the performance of our model. Finally, we test our proposed models on two tasks: the multi-subject mixed classification task and the single subject classification task. We use a 5-fold cross-validation to evaluate our model on both tasks. The GRU model performs best, achieving a recognition accuracy of 88.01% on the multi-subject mixed classification task and a maximum recognition accuracy of 97.19% on the single subject classification task. Experiments results demonstrate the effectiveness of our proposed system. • A silent speech system based on surface electromyography signals and deep learning models. • Time–frequency hybrid features are extracted from surface electromyography signals. • Speed perturbation and time–frequency masking are used for data augmentation. • Several deep learning models are evaluated on two different tasks; The GRU model performed the best. [ABSTRACT FROM AUTHOR]

• The MIF-AFNet can fuse the deep features of RRIs and ECG for AF binary detection. • The data augmentation method of vertical flipping can eliminate the morphological imbalance, thus effectively alleviating overfitting. • The MIF-AFNet is five-fold cross-validated on the CPSC2021 dataset and independently tested on four external databases. • The performance results verify excellent generalization performance for the method. The reliable detection of atrial fibrillation (AF) is important for diagnosing the disease, tracking its progression, and developing individualized care strategies. However, models based on limited data are prone to data dependency due to differences in data feature distribution, which generally degrades their performance on unseen external datasets. In this work, we propose a multi-input fusion AF detection network (MIF-AFNet), which cascades residual convolutional neural networks and bidirectional long short-term memory networks to capture the deep features of electrocardiogram (ECG) and RR intervals (RRIs), respectively. Additionally, the ECG signals use a data augmentation method to alleviate the morphological imbalance. MIF-AFNet learns a robust feature representation for accurate AF detection by fusing the available information from RRIs and ECG. The proposed method was developed and evaluated using 5 long-term ECG datasets (CPSC2021, AFDB, LTAF, MITDB, and NSRDB) from PhysioNet. The subject-wise five-fold cross-validation was performed on CPSC2021, and the proposed method achieved an AF detection accuracy of 98.63%. The generalization performance is further evaluated on four external independent datasets (AFDB, LTAF, MITDB, and NSRDB), achieving accuracies of 98.63%, 97.04%, 98.07%, and 100%, respectively. The results show that the proposed method can accurately detect AF from long-term ECG recordings. In addition, the low complexity of the model makes it less demanding on computing resources. Therefore, it has the potential to improve the automatic diagnosis and management of AF in wearable device-based long-term home monitoring. [ABSTRACT FROM AUTHOR]

• A novel thermodynamic control model was established. • Adding additional compensation function solved the problem of temperature gradient. • A chip-level oven-controlled sensor system was designed and implemented. • Oven-controlled system improved performance of silicon piezoresistive sensors. High precision is an important indicator for the silicon piezoresistive pressure sensor, which is deeply affected by the ambient temperature. This paper presented a chip-level oven-controlled system to operate the silicon piezoresistive pressure sensor at a constant temperature. However, the changes in ambient temperature still affect the temperature of the silicon piezoresistive pressure sensor due to the non-coplanar temperature control and the direct heat transfer between the piezoresistance on the surface of the pressure sensor and the environment. In order to decrease the effects and further improve the accuracy of the silicon piezoresistive sensor, a compensation function was introduced in the designed system. In addition, the oven-controlled structure fabricated by the MEMS process and the silicon piezoresistive pressure sensor were implemented at the chip level, which further reduced power consumption. The feasibility of the design was verified by the results of the thermodynamic model and COMSOL Multiphysics simulation analysis. Test results showed that when the pressure ranged from 100 hPa to 1100 hPa, the designed system performed a maximum measurement error no more than ±0.2 hPa from −45 °C to 45 °C. After compensation, the accuracy, sensitivity temperature coefficient, offset temperature coefficient of the silicon piezoresistive pressure sensor can reach 0.02% FS, 1.081 × 10−5/°C, 2.22 × 10−4% FS/°C respectively. [ABSTRACT FROM AUTHOR]

Heartbeat is a crucial vital sign, and radio-frequency technology can measure the micro-vibration of the body surface from a distance and extract the heartbeat signal, which can solve the compliance problem caused by wearable sensors. However, most existing works verify short-term controlled experiments and seldom consider the challenges posed by random body movements and posture changes during long-term monitoring in daily life scenarios. In this article, we propose a systematic solution called Radar-Beat, which enables accurate heartbeat monitoring using an mmWave FMCW radar device. Our goal is to promote the integration of contactless heartbeat monitoring into life scenarios. We proposes a sensitive body motion detection algorithm and an optimal Range-bin selection algorithm, which can automatically identify body motion and update the best heartbeat signal channel. Then, we construct a personalized heartbeat template for each signal segment and propose a global optimization model to improve the accuracy of heartbeat length estimation. We use the synchronized ECG signal as the ground truth, results show strong agreement between Radar-Beat and synchrony ECG devices for heart rate and inter-beat interval (IBI) measurements in healthy subjects. In the heartbeat monitoring experiment for a total of 72 h and 56 min involving 11 participants, under a time coverage of 91.2%, the median error of IBI estimation was 12 ms. Radar-Beat has strong robustness to different individuals in different postures and positions. It can be deployed in a hospital bed or home to enable continuous heartbeat monitoring in an unobtrusive way. • Non-contact FMCW radar enables beat-by-beat heartbeat monitoring in real-life scenarios. • Accurate automatic detection algorithm for body motion proposed. • Adaptive algorithm selects range bins for high-quality heartbeat in varied positions. • A template matching-based global optimization model achieves precise IBI estimation. [ABSTRACT FROM AUTHOR]

• Rapid and accurate determination of the volume set point V0 through PVM. • Continuous tracking of V0 changes using UVTM. • Development of a non-invasive continuous blood pressure monitor. • Experimental results demonstrate the high accuracy and reliability of PVM and UVTM. The volume-clamp method (VCM) is a widely used non-invasive continuous blood pressure (BP) monitoring technique, but determining the volume setpoint (V 0) in the open-loop phase can be time-consuming and may cause finger tissue displacement. Additionally, frequent interruptions in BP measurement are required during the closed-loop phase to reset V 0. We proposed two new approaches: the proportional value method (PVM) for rapid V 0 determination in the open-loop phase and the uninterrupted V 0 tracking method (UVTM) in the closed-loop phase. PVM adaptively searches for V 0 by analyzing the shape index (Prop) of the plethysmographic (PG) signal under constant cuff pressure, while UVTM introduces a new vascular compliance calculation method in closed-loop phase to track changes in V 0 without interrupting BP measurement. We describe the non-invasive continuous BP monitor (NC-BPM) based on these methods. Experimental results show that PVM can accurately determine V 0 , with a mean arterial pressure error of −1.72 ± 2.80 mmHg when compared to oscillometric method (OSCM). UVTM in NC-BPM produced a mean absolute error (MAE) of 2.44 ± 2.91 mmHg for mean arterial pressure (MAP) in induced BP fluctuation test compared to the Nexfin monitor. In the clinical study with invasive reference, the MAE in measuring MAP by NC-BPM was 4.69 ± 5.39 mmHg. Our results show that PVM and UVTM are highly accurate and reliable, with the potential to integrate effectively into VCM-based BP monitoring devices that could benefit patients, healthcare providers, and researchers. [ABSTRACT FROM AUTHOR]

• A novel end-to-end deep learning network for cross-scenario automatic sleep staging. • A joint distribution loss and a stage transition loss to solve the shift data distribution among domains. • The performance of sleep staging has been increased after using the proposed method. Sleep staging via manual inspection is time-consuming and inefficient, and as such, automatic sleep stage classification methods have been proposed and successfully implemented. However, these conventional supervised learning models are not suitable for cross-scenario sleep staging tasks due to the shift data distribution problem. This study aims to explore the use of transfer learning to transfer knowledge from a labeled source dataset to a new target domain where labels are difficult to obtain, so as to help solve the classification problem in a new domain in practical sleep staging scenarios. A novel end-to-end deep learning network that includes a feature extractor, a sleep stage classifier, and a domain adaptation network was proposed. The proposed domain adaptation network can accomplish distribution alignment between source and target domains, through the application of both joint distribution loss and stage transition loss. Cross-channel, cross-subject and channel, and cross-dataset experiments were designed to verify the feasibility of the proposed network using three publicly available datasets. The results indicate an average accuracy improvement ranging from 0.1% to 6.1% (average of 2.9%), a macro-averaging F1-score improvement ranging from 0.1% to 5.1% (average of 2.6%), and a Cohen's kappa coefficient improvement ranging from 0.016 to 0.083 (average of 0.045) after using transfer learning, when compared with a non-transfer model. Relative to similar existing transfer learning methods for sleep staging, the proposed method was implemented in an unsupervised manner and achieved success in cross-scenario sleep staging tasks, which is more suitable for practical applications in daily life. [ABSTRACT FROM AUTHOR]

This paper reports a Film Bulk Acoustic Resonator (FBAR) with micro through-hole array in the top electrode. When the FBAR device is used as a sensor based on adsorption and its piezoelectric actuation layer under the top electrode acts as sensitive layer, the through-hole array provide paths for gas to reach the sensitive layer directly and improves sensitivity of the sensor. In addition, thermal modules, a temperature sensor and a heater, are added to monitor and control working temperature of the resonator, and high temperature is available to enhance thermal desorption. We applied this FBAR system to a humidity sensor. The results show that the micro through-hole array design made sensitivity rise to 18.6 ppm/1%, namely 21.2 kHz/1%, which was 18 times higher than samples without. Meanwhile, the thermal modules kept working temperature stable and made it vary with the power supplied. [ABSTRACT FROM AUTHOR]

Ethnopharmacological relevance Aconitum tanguticum has been widely used as a remedy for infectious diseases in traditional Tibetan medicine in China. The total alkaloids of Aconitum tanguticum (TAA) are the main active components of Aconitum tanguticum and have been demonstrated to be effective in suppressing inflammation. Our aim was to investigate the protective effects of TAA on acute lung injury (ALI) induced by lipopolysaccharide (LPS) in rats. Materials and methods TAA was extracted in 95% ethanol and purified in chloroform. After vacuum drying, the TAA powder was dissolved in dimethyl sulfoxide. Adult male Sprague-Dawley rats were randomly divided into six groups. Rats were given dexamethasone (DXM, 4 mg/kg) or TAA (60 mg/kg, 30 mg/kg) before LPS injection. The PaO 2 and PaO 2 /FiO 2 values, lung wet/dry (W/D) weight ratio and histological changes in lung tissue were measured. The cell counts, protein concentration, tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-1β (IL-1β) in bronchoalveolar lavage fluid (BALF), and myeloperoxidase (MPO) activity in lung tissue were determined at 6, 12 or 24 h after LPS treatment. In addition, the NF-κ B activation in lung tissue was analyzed by western blot. Results In ALI rats, TAA significantly reduced the lung W/D ratio and increased the value of PaO 2 or PaO 2 /FiO 2 at 6, 12 or 24 h after LPS challenge. TAA also reduced the total protein concentration and the number of total cells, neutrophils or lymphocytes in BALF. In addition, TAA decreased MPO activity in the lung and attenuated histological changes in the lung. Furthermore, TAA inhibited the concentration of TNF-α, IL-6 and IL-1β in BALF at 6, 12 or 24 h after LPS treatment. Further study demonstrated that TAA significantly inhibited NF-κ B activation in lung tissue. Conclusions The current study proved that TAA exhibited a potent protective effect on LPS-induced ALI in rats through its anti-inflammatory activity. [ABSTRACT FROM AUTHOR]

• An end-to-end domain adaptation network is proposed for subject-specific sleep staging. • A single-lead Electrocardiograph (ECG) signal is utilized as the input. • Multi-class focal loss and a domain aligning layer are combined to solve data imbalances and domain shifts. • State-of-the-art performance of sleep staging based on single-lead ECG is obtained on the public SHHS2, SHHS1, and MESA datasets, respectively. • The method has excellent performance for accuracy and Cohen's Kappa in terms of cross datasets. Sleep plays a vital role in human physical and mental health. To accurately identify sleep structure under comfortable and convenient conditions, many machine-learning methods have been applied in the classification of sleep staging based on an Electrocardiogram (ECG); however, few works have solved the problem of generalization in the subject-specific sleep staging. The main reason for the problem is the difference of domain distribution across subjects. Most works are also classified based on ECG-derived signals and manual features. In this paper, we describe an end-to-end deep adaptation framework that classifies sleep stages into four classes based on a single-lead ECG to overcome the above problems. In particular, multi-class focal loss and a domain aligning layer based on the maximum mean discrepancy have been combined to solve data imbalances and domain shifts during three-step processing. We evaluate the method based on the three public datasets contained in SHHS2, SHHS1, and MESA. Compared to the performance of the model without domain aligning, the accuracy of the model for the public datasets has been improved by more than 20%, and the Kappa coefficient has also been improved by close to 0.4, which achieves state-of-the-art solutions compared to the baseline. In addition, the method has excellent performance for accuracy and Cohen's Kappa in terms of cross datasets. The proposed method, which confuses the domain-variant features, makes important contributions to the prediction of different subjects' sleep structures. The domain-adaptation setup, which varies across subjects and across environments, may provide a new approach to health monitoring. [ABSTRACT FROM AUTHOR]

• We explore radar-based heartbeat identification under open-set condition initially. • We propose a novel DDLM model without heartbeat segmentation and feature engineering. • The method shows great effectiveness in both open-set and closed-set environment. Non-contact continuous biometric identification system based on the heartbeat signals has attracted more attention due to its privacy-friendly properties. Current methods, however, mostly focused on the traditional machine learning methods under the close-set condition. This paper aims to investigate the feasibility of using the cardiac radar heartbeat signals and deep learning techniques to identify person in the open-set environment. A novel dipole deep learning model (DDLM) was proposed for the open-set person identification problem without heartbeat segmentation and feature engineering. The normalized heartbeat samples with time duration of 5 s were used as input and encoded into the feature space, where the encoded features of the same person cluster closely around the corresponding negative pole and repel far from positive pole, and those of different persons separate loosely from each other. Finally, threshold on the distance from the features to the dipoles in the feature space was set for each known identity. Extensive experiments conducted on a public dataset of clinically recorded vital signs indicate that:(1) The proposed model shows high stability under close-set condition with an accuracy higher than 99 % with 30 subjects.(2) The accuracy and the F1-score attain 93.42 % and 93.57 % under the open-set condition with the maximum openness of 29.3 %, respectively. The proposed model shows high effectiveness in person identification using heartbeat signals. The DDLM outperforms most of the existing methods under the close-set condition. And the DDLM shows a promising future for person identification in open-set environment. [ABSTRACT FROM AUTHOR]

• Multiple physiological parameters analysis during Head-Up Tilt Test (HUTT). • Machine learning to achieve early prediction of HUTT outcome. • Genetic algorithm for feature selection and a derived index for results interpretation. Head-up Tilt Testing (HUTT) is a widely used medical tool for the diagnosis of unexplained syncope. Current HUTT protocols, however, are time-consuming. This study aims to investigate the feasibility of using hemodynamic monitoring and machine learning techniques to achieve early prediction of HUTT outcome for syncope patients. A total of 209 subjects participated in this study from June 2016 to November 2019, and hemodynamic signals were collected via a Finometer device (Finapres Medical Systems BV, The Netherlands). We extracted features with a total dimension of 4,313 from the early 18 min (5 min of supine position and 13 min of tilting position). A genetic algorithm (GA) was introduced for feature selection, and an index called the selection ratio (SR) was proposed to further analyze the GA selection result. Four machine learning models were established for this classification task, and their performance results were compared. The maximum tilting duration was shortened from 35 min to 13 min, and a best area under receiver operating characteristic curve of 0.94 via 5-fold cross-validation was obtained by the SVR model, with a sensitivity of 0.86 and a specificity of 0.82. The performance of all algorithms improved after feature selection by GA. The proposed approach is a promising method to shorten the diagnosis time compared to the existing diagnosis process. The GA introduced in this study is an effective feature selection tool to improve model performance. The proposed SR index effectively contributes to the usability and interpretability of the model. [ABSTRACT FROM AUTHOR]

Gesture recognition has found versatile applications in natural human–computer interaction (HCI). Compared with traditional camera-based or wearable sensors-based solutions, gesture recognition using the millimeter wave (mmWave) radar has attracted growing attention for its characteristics of contact-free, privacy-preserving and less environment-dependence. Recently, most of studies adopted one of the Range Doppler Image (RDI), Range Angle Image (RAI), Doppler Angle Image (DAI) or Micro-Doppler Spectrogram extracted from the raw radar signal as the input of a deep neural network to realize gesture recognition. However, the effectiveness of these four inputs in gesture recognition has attracted little attention so far. Moreover, the lack of large amounts of labeled data restricts the performance of traditional supervised learning network. In this paper, we first conducted extensive experiments to compare the effectiveness of these four inputs in the gesture recognition, respectively. Then we proposed a semi-supervised leaning framework by utilizing few labeled data in the source domain and large amounts of unlabeled data in the target domain. Specially, we combine the ∏-model and some specific data augmentation tricks on the mmWave signal to realize the domain-independent gesture recognition. Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Ethnopharmacological relevance Compound turmeric has been widely used as a remedy for infectious diseases in China. It is a classic multi-herb prescription in traditional Chinese medicine, commonly used in the treatment of enteritis, pneumonia, and abdominal pain for hundreds of years. However, throughout this history, the powder of multi-herbs was directly swallowed, which is currently difficult to administer to patients. The extract of Chinese herbal medicine is made by semi-bionic extraction technology, which is great progress in the modernization of powders of traditional Chinese medicine. The aim of this work is to investigate the protective effects of semi-bionic extraction of compound turmeric (SET) on acute enteritis (AE) induced by dextran sulfate sodium (DSS) in rats. Materials and methods SET was extracted in artificial gastric juice or artificial intestinal juice and mixed. After vacuum drying, the SET powder was dissolved in distilled water. Adult male Sprague–Dawley rats were randomly divided into six groups. Rats were given salazosulfapyridine (SASP, 175.0 mg/kg) or SET (0.42 or 0.21 g/kg) before intragastric administration of 5% DSS solutions (0.75 g/kg). The treatments lasted 7 days. The food intake in 24 h, disease activity index (DAI), and wet/dry (W/D) weight ratios and histological changes in colon tissue were measured. The tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), IL-1β, IL-8, and IL-10 in serum were determined at 1, 4, or 7 d after DSS challenge. Myeloperoxidase (MPO), malonaldehyde (MDA), diamine oxidase (DAO), and glutathione peroxidase (GSH-Px) activities in colon tissue were determined at 7 d. In addition, the nuclear factor-kappa (NF-κ B) and intercellular cell adhesion molecule-1 (ICAM-1) activations in colon tissue were analyzed by reverse transcription-polymerase chain reaction (RT-PCR) and Western blot. Results In rats with AE, SET significantly reduced DAI at 7 d after DSS treatment, increased the body weight of rats and the food intake in 24 h at 3 or 6 d after DSS challenge, and reduced the colon W/D ratio. SET also reduced the TNF-α, IL-6, IL-1β, and IL-8 in serum and increased IL-10 in serum at 4 and 7 d. In addition, SET decreased MPO, MDA, DAO, and GSH-Px activities in colon and attenuated histological changes in the colon at 7 d after DSS treatment. Further studies demonstrated that SET significantly inhibited NF-κB and ICAM-1 activations in colon tissue. Conclusions The current study demonstrated that SET has potent protective effects on DSS-induced AE in rats through its anti-inflammatory and anti-oxidant activities. [ABSTRACT FROM AUTHOR]

Human identification is vital in health monitoring, human-computer interaction, safety detection, and other fields. Compared with traditional vision-based methods, millimeter wave radar sensors can protect users' privacy and work in dark environments, which has a wide range of application prospects in iot fields such as smart homes and smart medical care. Previous studies need to manually collect labeled data, which makes the data collection work need substantial human resources and is unsuitable for popularization and application. We automatically collect multi-modal radar signals in users ' daily lives without requiring researchers to label data manually. Based on the proposed data collection method, we established the first semi-supervised data set for human identification, which includes synchronous radar point cloud data and range-velocity map data. The dataset contains four experiments, including ten monitoring users and ten other users. We propose a semi-supervised co-training framework based on multi-modal data fusion for human identification. The framework guides the models to learn from unlabeled data using the complementary characteristics of point cloud data and range-velocity map data. In addition, we propose an information fusion method to fuse the radar data of two modes to further improve the model's performance. The experimental results show that the proposed method achieves 93.7% human identification accuracy, showing radar-based human identification technology's application and promotion potential. [ABSTRACT FROM AUTHOR]

• An image-free ultrasound system includes integrated multi-ultrasound transceiver channel hardware and real-time signal processing software was developed for continuous, non-invasive PP and blood pressure waveform measurement. • The software evaluates local PWV with a low-computation-consuming method using high frame rate ultrasound line, ensuring online presentation of PP and blood pressure waveform in an automated manner. • Comprehensive in vitro and human experiments (20 + 7) demonstrated the accuracy and reliability of this system in measuring PP and blood pressure waveforms. • This work presents a simple, scalable image-free ultrasound blood pressure waveform measurement method, with potential application prospects in the diagnosis and prevention of CVDs. Noninvasive measurement of local pulse pressure (PP) and blood pressure waveforms, especially in central arteries, is crucial for diagnosing and preventing cardiovascular diseases (CVDs). This work presents an ultrasound system that uses a low-computational method to evaluate local pulse wave velocity (PWV) and diameter waveforms online, enabling continuous and noninvasive measurement of PP and blood pressure waveforms without calibration. The system was validated both in vitro and in vivo, demonstrating high accuracy in PP and blood pressure waveform measurements in cardiovascular phantom experiments. In human carotid experiments, the system was compared to an arterial tonometer, showing excellent accuracy in PP and pressure waveform similarity. Comparative experiments with the volume clamp device further demonstrated the system's ability to accurately trace blood pressure changes induced by deep breathing. Thus, the developed system holds promise for noninvasively measuring arterial PP and blood pressure waveforms continuously, facilitating the diagnosis and prevention of CVDs. [ABSTRACT FROM AUTHOR]

• A new system is first-time established to measure blood pressure contactlessly according to the central-artery pulse transit time which is acquired from the neck and chest areas simultaneously with single millimeter wave radar. • The correlation between the PTT measured by radar and contact device is verified for the first time which is up to 0.91. • Twenty-seven subjects were invited for the blood pressure estimation experiment, and the estimation error is very close to the AAMI standard. • This work provides a new method for radar-based blood pressure measurement, which is expected to promote the measurement of blood pressure in daily life. Continuous measurement of blood pressure (BP) is very important for assessing the health of the cardiovascular system. In this paper, a new system is first-time established to contactlessly and continuously measure BP according to the central-artery pulse transit time (caPTT) which is acquired from the neck and chest areas simultaneously with single millimeter wave (mmWave) radar. The two signals are separated by beam-component focusing and then BP is estimated by regression fitting. The correlation of the PTT's variation measured by radar and contact device is verified by data from 16 subjects. The effectiveness of BP estimation is evaluated by data from 27 subjects. It is illustrated that the correlation of PTT's variation is up to 0.91 and the errors of SBP and DBP are 5.54 ± 7.62 mmHg and 4.68 ± 6.15 mmHg respectively. The developed system in this work suggested that single radar has the potential to continuously measure BP in daily life. [ABSTRACT FROM AUTHOR]

• A ball blocking differential pressure flowmeter replaced the traditional restriction block of the fixed orifice to obtain lower flow resistance for the diagnosis of COPD was proposed. • Special pressure tapping was selected to gain a biggest possible measured differential pressure. • Superiority of sensitivity and flow resistance was reflected in comparison to the single-hole and multi-hole flowmeters. We aimed to develop a low-resistance and high-accuracy way to measure expiratory volume for the accurate classification of chronic obstructive pulmonary disease (COPD). In this paper, using computer-aided design (CAD), a ball-blocking differential pressure flowmeter (BBDPF) has been developed and then fabricated using 3D printing. Ball blocking is used for the designed flowmeter to replace the traditional restriction of the differential pressure flowmeter for lower flow resistance, and special pressure tapping is selected for high accuracy. The BBDPF is theoretically and experimentally characterized, using ANSYS fluent® software with turbulent model simulations. Then, we validate the flowmeter, using pulmonary waveforms generator with flow resistance tests and the standard spirometry tests (ATS24/26). The results demonstrate that in comparison with other type differential pressure flowmeters, the structure of BBDPF effectively reduces flow resistance (144.41 Pa/L/s at 14 L/s) with accuracy(±3% of reading or ± 0.050 L,whichever is greater) in the range of 0 − 17L/s with a resolution of 0.01 L/s. This is confirmed by the application in expiratory volume measurement of the reported work functions well. [ABSTRACT FROM AUTHOR]

• The influence of phase noise in the front-end circuit on the performance of FBAR sensor systems was analyzed and discussed. • A frequency shift detecting system was designed based on a dual FBAR differential circuit. • The mechanism of phase noise change in the front-end circuit was analyzed. • The FBAR humidity sensor was adopted for verification in this detecting system. The FBAR sensor can be affected severely by its front-end circuit, especially the phase noise; however, it has rarely been discussed up to now. Herein, we analyze the influence of phase noise in the front-end circuit on an FBAR humidity sensor. A frequency shift detecting system was designed combined with the theoretical calculation and Advanced Design System (ADS) simulation. The available range of frequency shift is seriously influenced by phase noise particularly for its lower limit, improved from 380 kHz to 200 kHz during the reduction of phase noise from − 107.92 dBc/Hz@1 MHz to − 135.97 dBc/Hz@1 MHz. Simultaneously, the display range for the FBAR humidity sensor system increases by 23% thanks to the reduction of it by − 28 dBc/Hz@1 MHz. Summarily, the phase noise optimization in the front-end circuit can effectively improve the display range of sensor systems, which is not limited to the application of FBAR humidity sensors. [ABSTRACT FROM AUTHOR]