scholarly journals Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1558
Author(s):  
Muhammad Bilal Khan ◽  
Mubashir Rehman ◽  
Ali Mustafa ◽  
Raza Ali Shah ◽  
Xiaodong Yang
Keyword(s):  
Psychological Health ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Vital Signs ◽  
Wireless Channel ◽  
Machine Learning Algorithms ◽  
Breathing Patterns ◽  
Connected Health ◽  
Innovative Solution

The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.

scholarly journals Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence

2020 ◽  
Vol 10 (14) ◽  
pp. 4886 ◽  
Author(s):  
Mohammed Ali Mohammed Al-hababi ◽  
Muhammad Bilal Khan ◽  
Fadi Al-Turjman ◽  
Nan Zhao ◽  
Xiaodong Yang
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Nearest Neighbor ◽  
Wireless Channel ◽  
Weight Lifting ◽  
K Nearest Neighbor ◽  
Post Surgery ◽  
Contact Sensing

Non-contact health care monitoring is a unique feature in the emerging 5G networks that is achieved by exploiting artificial intelligence (AI). The ratio of the number of health care problems and patients is increasing exponentially and creating burgeoning data. The integration of AI and Internet of things (IoT) systems enables us to increase the huge volume of data to be generated. The approach by which AI is applied to the IoT systems enhances the intelligence of the health care system. In post-surgery monitoring of the patient, timely consultation is essential before further loss. Unfortunately, even after the advice of the doctor to the patient, he/she may forget to perform the activity in the correct way, which may lead to complications in recovery. In this research, the idea is to design a non-contact sensing testbed using AI for the classification of post-surgery activities. Universal software-defined radio peripheral (USRP) is utilized to collect the data of spinal cord operated patients during weight lifting activity. The wireless channel state information (WCSI) is extracted by using orthogonal frequency division multiplexing (OFDM) technique. AI applies machine learning to classify the correct and wrong way of weight lifting activity that was considered for experimental analysis. The accuracy achieved by the proposed testbed by using a fine K-nearest neighbor (FKNN) algorithm is 99.6%.

scholarly journals Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 626 ◽  
Author(s):  
Ernesto Cadena Muñoz ◽  
Luis Fernando Pedraza Martínez ◽  
Cesar Augusto Hernandez
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Radio Networks ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Phone Call ◽  
Radio System

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

scholarly journals Implementation of PCC-OFDM on a software-defined radio testbed

2017 ◽  
Vol 5 (2) ◽  
pp. 17
Author(s):  
Gayathri Kongara ◽  
Jean Armstrong
Keyword(s):  
Carrier Frequency ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Carrier Frequency Offset ◽  
Frequency Offset ◽  
Wireless Channel ◽  
Error Performance ◽  
Hardware Platform ◽  
Field Programmable ◽  
Comparative Results

A software-defined radio implementation of polynomial cancellation coded orthogonal frequency division multiplexing (PCC-OFDM) on a field programmable gate array (FPGA) based hardware platform is presented in this paper. Previous publications on PCC-OFDM have demonstrated that, in comparison to normal cyclic prefix based OFDM, it is robust in the presence of many impairments including carrier frequency offset, multipath distortion and phase noise. The error performance of the two multicarrier techniques is compared on a practical wireless channel under common channel impairments such as carrier frequency offset, multipath and noise. Based on the comparative results obtained on the hardware platform, the properties of PCC-OFDM make it a suitable candidate for consideration in future G applications requiring robust performance in asynchronous environments with minimal out of band spectral emissions.

A Coherent Optical OFDM Communication System with Nonlinear Distortion Compensation in the Channel and Receiver

2017 ◽  
Vol 38 (3) ◽  
Author(s):  
R. S. Asha ◽  
V. K. Jayasree
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Low Cost ◽  
Additive White Gaussian Noise ◽  
Wireless Channel ◽  
Noise Model ◽  
Channel Noise ◽  
Distortion Compensation ◽  
Sequence Estimation ◽  
Maximum Likelihood Sequence Estimation ◽  
Optical Ofdm

AbstractA simple and low-cost scheme is proposed for reducing the distortions in the coherent optical orthogonal frequency-division multiplexing (CO-OFDM) system. The total wireless channel noise and the distortions in the receiver can be considered as an additive white Gaussian noise model and all distortions can be reduced using maximum likelihood sequence estimation (MLSE) equalizers. The performance of the CO-OFDM is analyzed for different fiber lengths and laser powers. Results show that the MLSE-equalized system can outperform with a higher

scholarly journals OFDM comparison with FFT and DWT processing for DVB-T2 wireless channels

Inge CUC ◽  
2018 ◽  
Vol 14 (2) ◽  
pp. 97-105
Author(s):  
Hernan Paz Penagos
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Additive White Gaussian Noise ◽  
Average Power ◽  
Wireless Channel ◽  
Rician Fading ◽  
Discrete Wavelet ◽  
Research Experience ◽  
Radio Communications ◽  
Ofdm Signal ◽  
Future Work

Introduction: Recent studies on the FFT processing (Fast Fourier Transform) or DWT (Discrete Wavelet Transform) of the OFDM signal (Orthogonal Frequency Division Multiplexing) have shown pros and cons for DVB-T2 (Digital Video Broadcasting-Second Generation Terrestrial) radio communications; however, the benefits of both types of processing have yet to be compared for the same scenario. Objective: The objective of this research is to compare the response of the wireless channel with AWGN noise (Additive White Gaussian Noise Channel) and Rayleigh and Rician fading in the UHF (Ultra High Frequency) band. Methodology: The transmission of DVB-T2 information with OFDM modulation and FFT and DWT processing was simulated in Matlab®, specifically in Simulink. Results: The results of the study proved to be more efficient for DWT system than FFT system, due to the low rate of erroneous bits, spectral efficiency and reduction of the Peak-to-Average Power Ratio (PAPR), for Eb / No relations greater than 10dB. Conclusions: In this article, we present the designs of both systems and the results of the research experience; likewise, the practical applicability of these systems is discussed, and improvements are suggested for future work.

scholarly journals Implementation of PCC-OFDM on a software-defined radio testbed

2017 ◽  
Vol 5 (2) ◽  
pp. 17-34
Author(s):  
Gayathri Kongara ◽  
Jean Armstrong
Keyword(s):  
Carrier Frequency ◽  
Software Defined Radio ◽  
Orthogonal Frequency Division Multiplexing ◽  
Carrier Frequency Offset ◽  
Frequency Offset ◽  
Wireless Channel ◽  
Error Performance ◽  
Hardware Platform ◽  
Field Programmable ◽  
Comparative Results

A software-defined radio implementation of polynomial cancellation coded orthogonal frequency division multiplexing (PCC-OFDM) on a field programmable gate array (FPGA) based hardware platform is presented in this paper. Previous publications on PCC-OFDM have demonstrated that, in comparison to normal cyclic prefix based OFDM, it is robust in the presence of many impairments including carrier frequency offset, multipath distortion and phase noise. The error performance of the two multicarrier techniques is compared on a practical wireless channel under common channel impairments such as carrier frequency offset, multipath and noise. Based on the comparative results obtained on the hardware platform, the properties of PCC-OFDM make it a suitable candidate for consideration in future G applications requiring robust performance in asynchronous environments with minimal out of band spectral emissions.

Simulation modeling of free space optical communication system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Sharma ◽  
Rajinder Singh Kaler
Keyword(s):  
Optical Communication ◽  
Free Space ◽  
Simulation Modeling ◽  
Additive White Gaussian Noise ◽  
Wireless Channel ◽  
Free Space Optical Communication ◽  
Optical Wireless ◽  
Free Space Optical ◽  
Wireless Signal ◽  
Space Optical Communication

Abstract The optical wireless communication has been designed by developing a model with the support of MATLAB simulator using Simulink where channel considered to be a free space. In this model, Additive White Gaussian Noise (AWGN) channel has used to analyze bit error rate (BER) and power loss of optical wireless signal at receiver. The consequence due to turbulence in atmosphere of free space on transmitted signal has examined. The BER and signal power have extremely ruined on rigorous atmospheric unstable condition even for a short distance in an optical wireless channel. The BER of less than 10−3 has been achieved for free space optical communication considered to be an excellent BER for FSO.

scholarly journals Contactless Monitoring of Breathing Patterns and Respiratory Rate at the Pit of the Neck: A Single Camera Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Carlo Massaroni ◽  
Daniel Simões Lopes ◽  
Daniela Lo Presti ◽  
Emiliano Schena ◽  
Sergio Silvestri
Keyword(s):  
Respiratory Rate ◽  
Frequency Domain ◽  
Time Domain ◽  
Breathing Pattern ◽  
Vital Signs ◽  
Image Resolution ◽  
Measuring System ◽  
Respiratory Pattern ◽  
Breathing Patterns ◽  
Vital Signs Monitoring

Vital signs monitoring is pivotal not only in clinical settings but also in home environments. Remote monitoring devices, systems, and services are emerging as tracking vital signs must be performed on a daily basis. Different types of sensors can be used to monitor breathing patterns and respiratory rate. However, the latter remains the least measured vital sign in several scenarios due to the intrusiveness of most adopted sensors. In this paper, we propose an inexpensive, off-the-shelf, and contactless measuring system for respiration signals taking as region of interest the pit of the neck. The system analyses video recorded by a single RGB camera and extracts the respiratory pattern from intensity variations of reflected light at the level of the collar bones and above the sternum. Breath-by-breath respiratory rate is then estimated from the processed breathing pattern. In addition, the effect of image resolution on monitoring breathing patterns and respiratory rate has been investigated. The proposed system was tested on twelve healthy volunteers (males and females) during quiet breathing at different sensor resolution (i.e., HD 720, PAL, WVGA, VGA, SVGA, and NTSC). Signals collected with the proposed system have been compared against a reference signal in both the frequency domain and time domain. By using the HD 720 resolution, frequency domain analysis showed perfect agreement between average breathing frequency values gathered by the proposed measuring system and reference instrument. An average mean absolute error (MAE) of 0.55 breaths/min was assessed in breath-by-breath monitoring in the time domain, while Bland-Altman showed a bias of −0.03 ± 1.78 breaths/min. Even in the case of lower camera resolution setting (i.e., NTSC), the system demonstrated good performances (MAE of 1.53 breaths/min, bias of −0.06 ± 2.08 breaths/min) for contactless monitoring of both breathing pattern and breath-by-breath respiratory rate over time.

scholarly journals Remote Monitoring of Patients with COVID-19 after Hospital Discharge with Connected Health Platform: Outcomes and Quality of Life

2020 ◽  
pp. 1-5
Author(s):  
César Morcillo Serra ◽  
César Morcillo Serra ◽  
Domingo Marzal Martín ◽  
Jorge Velázquez Moro ◽  
José Francisco Tomás Martínez
Keyword(s):  
Quality Of Life ◽  
Oxygen Saturation ◽  
Hospital Discharge ◽  
Remote Monitoring ◽  
Vital Signs ◽  
Medical Team ◽  
Mobile Phone Application ◽  
Connected Health ◽  
Video Consultation

Background: Telemonitoring with applications and connected devices facilitates a more accessible and efficient attention. Its implementation has been accelerated thanks to the pandemic by COVID-19, where they have allowed the continuity of care. Objective: To evaluate the efficacy of a remote monitoring platform for the outpatient follow-up after hospital discharge by COVID-19. Methods: Prospective observational study of patients discharged from the hospital with COVID-19 infection between March 23 and May 25, 2020, who were followed for one month with the Connected Health telemonitoring platform. The mobile phone application connected to a pulse oximeter, allowed to measure vital signs and answer health questionnaires (EQ5D3L and CAT) daily, and alert the medical team that could be contacted by video consultation. Results: 95 patients (64% male) with a mean age of 54 (SD 26-81) years were included. The application allowed the detection of alerts for pain (80% of patients) and a decrease in oxygen saturation (12%). No patient required hospital readmission or presented complications. The application allowed strict monitoring of symptoms and quality of life. The main symptom was severe pain (59% of patients) followed by anxiety or depression (25%). The average state of health was 65 (SD 20-100). COVID-19 caused a low impact on the quality of life of 62% of the patients, although 8% reported a significant limitation, due to shortness of breath and leaving the house. Conclusion: telemonitoring allows a safe remote monitoring of patients after hospital discharge by COVID-19. The Connected Health application has allowed the measurement of oxygen saturation, symptoms and quality of life, and the detection and management of alerts by the medical team through video consultation.

Sign in / Sign up

Export Citation Format

Share Document