Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userRespiratory Rate Estimation Based on WiFi Frame Capture
107
0
0.0
(
0
)
Added by
Takamochi Kanda
Publication date
2021
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
This paper presents a method that estimates the respiratory rate based on the frame capturing of wireless local area networks. The method uses beamforming feedback matrices (BFMs) contained in the captured frames, which is a rotation matrix of channel state information (CSI). BFMs are transmitted unencrypted and easily obtained using frame capturing, requiring no specific firmware or WiFi chipsets, unlike the methods that use CSI. Such properties of BFMs allow us to apply frame capturing to various sensing tasks, e.g., vital sensing. In the proposed method, principal component analysis is applied to BFMs to isolate the effect of the chest movement of the subject, and then, discrete Fourier transform is performed to extract respiratory rates in a frequency domain. Experimental evaluation results confirm that the frame-capture-based respiratory rate estimation can achieve estimation error lower than 3.2 breaths/minute.
rate research
Read More
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNS research project that is investigating the feasibility of wearable devices and smart phones to monitor individuals with multiple sclerosis (MS), depression or epilepsy. Aspart of the project protocol, heart-rate data was collected from participants using a Fitbit wristband. The presence of COVID-19 in the cohort in this work was either confirmed through a positive swab test, or inferred through the self-reporting of a combination of symptoms including fever, respiratory symptoms, loss of smell or taste, tiredness and gastrointestinal symptoms. Experimental results indicate that our proposed contrastive convolutional auto-encoder (contrastive CAE), i. e., a combined architecture of an auto-encoder and contrastive loss, outperforms a conventional convolutional neural network (CNN), as well as a convolutional auto-encoder (CAE) without using contrastive loss. Our final contrastive CAE achieves 95.3% unweighted average recall, 86.4% precision, anF1 measure of 88.2%, a sensitivity of 100% and a specificity of 90.6% on a testset of 19 participants with MS who reported symptoms of COVID-19. Each of these participants was paired with a participant with MS with no COVID-19 symptoms.
This paper presents a robust method to monitor heart rate (HR) from BCG (Ballistocardiography) signal, which is acquired from the sensor embedded in a chair or a mattress. The proposed algorithm addresses the shortfalls in traditional Fast Fourier Transform (FFT) based approaches by introducing Hilbert Transform to extract the pulse envelope that models the repetition of J-peaks in BCG signal. The frequency resolution is further enhanced by applying FFT and phase vocoder to the pulse envelope. The performance of the proposed algorithm is verified by experiment from 7 subjects. For HR estimation, mean absolute error (MAE) of 0.90 beats per minute (BPM) and standard deviation of absolute error (STD) of 1.14 BPM are obtained. Pearson correlation coefficient between estimated HR and ground truth HR of 0.98 is also achieved.
We present an IoT-based intelligent bed sensor system that collects and analyses respiration-associated signals for unobtrusive monitoring in the home, hospitals and care units. A contactless device is used, which contains four load sensors mounted under the bed and one data processing unit (data logger). Various machine learning methods are applied to the data streamed from the data logger to detect the Respiratory Rate (RR). We have implemented Support Vector Machine (SVM) and also Neural Network (NN)-based pattern recognition methods, which are combined with either peak detection or Hilbert transform for robust RR calculation. Experimental results show that our methods could effectively extract RR using the data collected by contactless bed sensors. The proposed methods are robust to outliers and noise, which are caused by body movements. The monitoring system provides a flexible and scalable way for continuous and remote monitoring of sleep, movement and weight using the embedded sensors.
Channel estimation is crucial for modern WiFi system and becomes more and more challenging with the growth of user throughput in multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms without pre-assumed channel characteristics in this paper. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks, and directly apply them in some practical WiFi prototype systems. As shown in this paper, the proposed super-resolution based channel estimation scheme can outperform the conventional approaches in both LOS and NLOS scenarios, which we believe can significantly change the current channel estimation method in the near future.
Respiratory rate (RR) is a clinical sign representing ventilation. An abnormal change in RR is often the first sign of health deterioration as the body attempts to maintain oxygen delivery to its tissues. There has been a growing interest in remotely monitoring of RR in everyday settings which has made photoplethysmography (PPG) monitoring wearable devices an attractive choice. PPG signals are useful sources for RR extraction due to the presence of respiration-induced modulations in them. The existing PPG-based RR estimation methods mainly rely on hand-crafted rules and manual parameters tuning. An end-to-end deep learning approach was recently proposed, however, despite its automatic nature, the performance of this method is not ideal using the real world data. In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals. Our results demonstrate a higher RR estimation accuracy of up to 2$times$ (mean absolute error of 1.9$pm$0.3 using five fold cross validation) compared to the state-of-th-art using a identical publicly available dataset. Our results suggest that CycleGAN can be a valuable method for RR estimation from raw PPG signals.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
