Publications by authors named "Aifeng Ren"
Article Synopsis
- * Using emergency call data from 2013 to 2020, researchers applied statistical methods to assess the acute impact of cold wave days on CO poisoning, revealing that lower temperature thresholds significantly elevate the risk.
- * The findings indicate a clear link between cold waves and increased CO poisoning risk, emphasizing the need for timely cold wave warnings and protective measures to safeguard public health.
Objective: Non-invasive respiration detection methods are of great value to healthcare applications and disease diagnosis with their advantages of minimizing the patient's physical burden and lessen the requirement of active cooperation of the subject. This method avoids extra preparations, reduces environmental constraints, and strengthens the possibility of real-time respiratory detection. Furthermore, identifying abnormal breathing patterns in real-time is necessary for the diagnosis and monitoring of possible respiratory disorders.
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- The study focuses on the need for effective water resource management in agriculture, emphasizing the importance of water distribution for plant health and crop productivity.
- A novel approach using machine learning and terahertz (THz) technology is proposed to accurately estimate the water content in plant leaves over a period of four days.
- The results showed that the Support Vector Machine (SVM) algorithm performed best with high accuracy across different plant types, and improvements in computational time were achieved using a feature selection technique.
Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol.
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- Parkinsonian gait significantly impacts the quality of life for individuals with shaking palsy, necessitating a reliable detection system.
- The proposed system utilizes S-band perception techniques to differentiate between abnormal and normal walking patterns by analyzing wireless signal variations.
- Through data preprocessing, principal component analysis, and a support vector machine classification algorithm, the system boasts over 90% accuracy in detecting Parkinsonian gait non-invasively.
Human respiratory activity parameters are important indicators of vital signs. Most respiratory activity detection methods are naïve abd simple and use invasive detection technology. Non-invasive breathing detection methods are the solution to these limitations.
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- A non-intrusive sleep apnea detection system uses C-Band channel sensing to monitor sleep apnea in real-time by analyzing RF signal perturbations.
- The system calculates peak distances to determine respiratory rates and compares its data with a wearable sensor to assess accuracy.
- Results show over 80% concordance between the two methods, indicating that this detection technique could be an effective and reliable solution for real-time sleep apnea monitoring.
Article Synopsis
- The paper addresses the challenges caregivers face due to dementia patients' wandering behaviors, which can occur from boredom or memory issues.
- It explores using S-band sensing techniques to monitor different wandering patterns, like random movement, lapping, and pacing, within indoor environments.
- Results indicate that the technique achieves an impressive classification accuracy of up to 90%, suggesting it could be beneficial for improving healthcare for dementia patients.*
Detection of Essential Tremor at the [Formula: see text]-Band.
IEEE J Transl Eng Health Med
January 2018
Article Synopsis
- Essential tremor (ET) is a neurological disorder causing involuntary shaking, primarily in the hands and fingers.
- The paper discusses a method to monitor ET using small wireless devices that capture finger-to-nose test measurements, analyzing amplitude and phase signals to detect tremors.
- A support vector machine algorithm is employed to classify tremor and non-tremor data with over 90% accuracy by transforming and calibrating the signal data effectively.
In our daily life, inadvertent scratching may increase the severity of skin diseases (such as atopic dermatitis etc.). However, people rarely pay attention to this matter, so the known measurement behaviour of the movement is also very little.
View Article and Find Full Text PDFAs an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end.
View Article and Find Full Text PDFWireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer security arrangements are not sufficient to employ their full potential, and novel solutions are necessary.
View Article and Find Full Text PDFPurpose: The aim of this study is to qualify the network properties of the brain networks between two different mental tasks (play task or rest task) in a healthy population.
Methods And Materials: EEG signals were recorded from 19 healthy subjects when performing different mental tasks. Partial directed coherence (PDC) analysis, based on Granger causality (GC), was used to assess the effective brain networks during the different mental tasks.
The face recognition ability varies across individuals. However, it remains elusive how brain anatomical structure is related to the face recognition ability in healthy subjects. In this study, we adopted voxel-based morphometry analysis and machine learning approach to investigate the neural basis of individual face recognition ability using anatomical magnetic resonance imaging.
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- The study explores using compressive sensing (CS) to reduce costs and power usage in on-body ultra-wideband (UWB) channel estimation.
- It shows that CS effectively recovers the sparse UWB channel impulse response, closely matching the original sparse channel.
- This method allows for on-body channel estimation with devices that sample at lower speeds.
We used resting-state functional magnetic resonance imaging (fMRI) to investigate changes in the thalamus functional connectivity in early and late stages of amnestic mild cognitive impairment. Data of 25 late stages of amnestic mild cognitive impairment (LMCI) patients, 30 early stages of amnestic mild cognitive impairment (EMCI) patients and 30 well-matched healthy controls (HC) were analyzed from the Alzheimer's disease Neuroimaging Initiative (ADNI). We focused on the correlation between low frequency fMRI signal fluctuations in the thalamus and those in all other brain regions.
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- Parametric probability models are often used to characterize wireless channels, but they struggle with accuracy due to limited samples and uncertain environments in body area networks.
- This paper introduces a sparse nonparametric probability model that improves the characterization of wireless channels in these body area networks by learning key parameters like path loss and root-mean-square delay.
- The new model is shown to be more effective than traditional parametric models, offering a valuable addition to existing approaches in this field.
The aim of this work is to investigate the differences of effective connectivity of the default mode network (DMN) in Alzheimer's disease (AD) patients and normal controls (NC). The technique of independent component analysis (ICA) was applied to identify DMN components and multivariate Granger causality analysis (mGCA) was used to explore an effective connectivity pattern. We found that: (i) connections in AD were decreased than those in NC, in terms of intensity and quantity.
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