Development of an Intelligent Real-Time Multiperson Respiratory Illnesses Sensing System Using SDR Technology

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Adaptive orthogonal frequency division multiplexing (OFDM) has been shown to have significant improvement over basic OFDM in terms of spectral efficiency and bit error rate performance. However, the most important condition to achieve these benefits is the availability of accurate channel state information (CSI) at the transmitter. Imperfect CSI arises from noise at the receiver and due to a delay in providing the CSI from the receiver to the transmitter for the next data transmission. In this paper, we have studied the impact of imperfect CSI on the spectral efficiency of adaptive OFDM systems. We have proposed a robust loading algorithm to improve the performance of such systems. This algorithm applies the bit under loading phenomenon and makes efficient use of M-ary Phase Shift Keying (MPSK) modulation. Plots of spectral efficiency via simulations are provided.

This paper relates to the application method of channel state information (CSI) to the Viterbi (maximum likelihood) decoder in the digital terrestrial television broadcasting system. The proposed Viterbi decoder uses the CSI derived from the pilots inserted in the transmitter of the COFDM (coded orthogonal frequency division multiplexing) system. The CSI is calculated by interpolation using the pilots in the receiver. The active real (I) and imaginary (Q) data after equalization are transferred to the branch metric calculation block that decides the euclidean distance for soft decision decoding and also the estimated CSI values are transferred to the same block. After calculating the euclidean distance for the soft decision decoding, the euclidean distance of the branch metric is multiplied by CSI. To do so, new branch metric values that consider each carrier state information are obtained. We simulated this method in Rayleigh fading defined in the ETSI standard. From the simulation, this method has better performance of about 0.15 dB to 0.7 dB and 2.2 dB to 2.9 dB in the Rayleigh channel than that of conventional soft decision Viterbi decoding with or without a bit interleaver where the constellation is QPSK, l6-QAM and 64-QAM.

A transmit chain test bed is used in the project presented in this paper to test an 802.11g OFDM (Orthogonal Frequency Division Multiplexing) Software Defined Radio (SDR) base station beacon frame transmitter. Several prior Gnuradio test bed setups show how a Gnuradio SDR Transmitter is able to communicate with another Gnuradio SDR. This paper additionally shows how a Gnuradio SDR Transmitter is able to visibly communicate with off-the-shelf 802.11 WLAN (Wireless Local Area Network) off-the-shelf chips. Results of the implementation show that a SDR base station is successfully able to communicate with traditional WLAN off-the-shelf communication chips found inside devices such as laptops, netbooks and mobile phones, which were used as the OFDM beacon frame receptors.

Real-time monitoring is an essential part in the development of healthcare monitoring systems. Research has shown that human movement affects the propagation of radio frequencies, as signals will reflect off the human body. Machine Learning techniques have been used in research to classify patterns observed in the signal propagation. This paper makes use of universal software radio peripheral devices to create a wireless communication link where the signal propagation data, known as channel state information, is collected while a user moves or remains still. A machine learning model which achieved an accuracy result of 93.25 % is used to classify between movement and no activity. Inference is then used to decide if the human position is sitting or standing and detected movements are used to differentiate between the two positions. The testbed implements cloud storage and a web-interface to present a visualisation of the human position.

Electret-based flexible pressure sensor for respiratory diseases auxiliary diagnosis system using machine learning technique

COVID-19 and Gastrointestinal Tract Symptoms: Recognition, Containment, and Learning From the Past

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, time-varying multipath fading of channel destroys the orthogonality among subcarriers and leads to serious intercarrier interference (ICI). The system performance degrades more severely as normalized Doppler frequency increases. In order to mitigate the effect of time-varying fading, a low-complexity iterative receiver with joint ICI cancellation and pilot-assisted channel estimation is proposed. The initial channel state information (CSI) is estimated by performing time-domain interpolation and least-square (LS) method on the received pilot symbols. The soft outputs are obtained from the decoders after low-complexity linear minimum mean-square error (LC-LMMSE) detection. In the following stages, the soft outputs are feedback to update the CSI estimation. Furthermore, a ¿linear statistics combining¿ (LSC) technique is used to improve the performance of the proposed equalizer by combining the outputs of LC-LMMSE and parallel interference canceler (PIC) with weighting coefficients estimated by maximizing the signal to interference-plus-noise ratio (SINR) at the output of LSC. The complexity of system is significantly reduced by restricting the interference to neighboring subcarriers and employing the LC-LMMSE by limiting the frequency-domain CSI into diagonal region. The simulation results show that the proposed iterative receiver with estimated CSI approaches the ICI-free bound even at very high mobility scenarios.

Few studies have evaluated teachers' respiratory health, especially its relationship with school/home environment, and school policies. This study assessed asthma and smoking prevalence among teachers in Romania, teacher's perception and knowledge of the school environment, policies and asthma management, and how school and home environment affected asthma, allergy, and respiratory infection symptoms. This cross-sectional study obtained information from 104 Romanian teachers utilizing teacher questionnaire data for Romania only, as part of the Schools Indoor Pollution and Health: Observatory Network in Europe (SINPHONIE) study, a multicenter European research project conducted between 2010 and 2012. The SINPHONIE questionnaire collected comprehensive information on school and home environment, respiratory symptoms, smoking, and school policies. We used unconditional logistic regression analysis to examine environment-outcome relationships while controlling for socio-demographics and co-exposures. Our results showed the prevalence of asthma-like symptoms and smoking among teachers in Romania was higher than in other SINPHONIE schools and among US teachers. Factors statistically associated with asthma, allergy, and respiratory infection (all p<0.05) include perception of health related to poor air quality in school, inappropriate cleaning of ventilation systems, dwelling proximity to busy traffic, and multiple school/home exposures. We also found lack of asthma management and environmental policies in the investigated Romanian schools. We concluded that multiple school and home environmental factors were related to respiratory and allergic symptoms. High asthma burden and smoking are important public health problems in Romania. Future studies including larger sample size and exposure measurements are needed to confirm our findings.

Orthogonal Frequency Division Multiplexing (OFDM) is now the de-facto modulation scheme of contemporary broadband wireless communication systems, such as 5G New Radio (NR), Wi-Fi 6/7, and emerging sixth-generation (6G) systems, because it has superior resistance to multipath fading, is a high spectral efficiency, and can support flexible resource allocation. Traditional OFDM receivers are highly dependent on pilot symbols or reference signals to estimate the channel, synchronize and equalize it. Yet, pilot insertion adds a large overhead in spectral, decreases the efficiency of the energy and constrains the system throughput, particularly in ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC) and high-mobility conditions. Such constraints have fueled the creation of pilotless OFDM reception techniques demand instinctive inferring of channel state information (CSI) at the receiver of received data without explicit pilot signals. The objective of implicit channel inference methods is to recover the response of the wireless channel based on statistical signal characteristics, data driven learning model, blind estimation applications, and sophisticated signal processing structures. These strategies minimize pilot overhead, enhance spectral efficiency, and support dynamic environment adaptive communication. The latest developments in machine learning, specifically deep neural networks, reinforcement learning and self-supervised learning, have further made pilotless reception possible as they have allowed the receivers to acquire channel information by simply observing signals. Further, novel intelligent wireless building of artificial intelligence and physical layer design has portrayed substantial gains of channel estimation precision, obstinacy to interference, and efficiency. This study is an intensive research study on implicit channel inference methods in case of pilo less OFDM reception among next-generation wireless networks. The proposed study is a hybrid framework that uses statistical signal modelling, blind estimation algorithms, and deep learning based inference to modify channel information without pilot symbols.

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-KuhnTucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic waterfilling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

In this paper, a simple decoded orthogonal frequency division multiplexing (OFDM) quasi orthogonal space time block coding (QOSTBC) wideband wireless system is proposed in the presence of Nakagami-m fading and imperfect channel state information (CSI). The proposed scheme preserves full diversity and full code rate with simple decoding complexity. The use of OFDM is to eliminate the problem of inter-symbol interference (ISI) caused by the frequency selectivity of the channel. Due to practical considerations, imperfect CSI is considered in this work to imitate real transmission scenarios. Simulation results show that in the case of Rayleigh fading and channel estimation error variance of 0, the proposed and the conventional schemes overlap. This shows the good capability of the OFDM technology on repealing the effect of ISI. In addition, it is noted that the performance improves as the Nakagami-m parameter increases. Moreover, it can be observed that as the imperfect estimation error variance increases the performance degrades.

Orthogonal Frequency Division Multiplexing (OFDM) systems are commonly used to mitigate frequency-selective multipath fading and provide high-speed data transmission. In this paper, we derive new union bounds on the error probability of a coded OFDM system in wireless environments. In particular, we consider convolutionally coded OFDM systems employing single and multiple transmit antennas over correlated block fading (CBF) channels with perfect channel state information (CSI). Results show that the new union bound is tight to simulation results. In addition, the bound accurately captures the effect of the correlation between sub-carriers channels. It is shown that as the channel becomes more frequency-selective, the performance get better due to the increased frequency diversity. Moreover, the bound also captures the effect of multi-antenna as space diversity. The proposed bounds can be applied for coded OFDM systems employing different coding schemes over different channel models.

Orthogonal frequency division multiplexing (OFDM) is a modulation and multiplexing technique widely used in communication systems since it guarantees reliable communications on noisy wireless channels. However, OFDM implementation is a very challenging task due to the high peak-to-average power ratio (PAPR) value. In this work, a PAPR reduction technique is applied, namely clipping technique. This technique is evaluated in a real OFDM system, which is implemented in software defined radio (SDR). We present and demonstrate that is possible to increase the range and coverage through the efficient use of the transmission power when the PAPR reduction technique is applied in the SDR technology. The results show that when a clipping technique with a threshold of 60% is used, the power required is reduced in 33.75%. Additionally, the reduction percentage could be transformed into 9.58% of increase in range and 63.07% in coverage.

The Universal Software Radio Peripheral (USRP) developed by Ettus research is emerging as one of the most promising hardware solution for building a Software Defined Radio (SDR) platform. Originally designed for supporting GNU radio, it can also be interfaced to customized C++ code, thus allowing a higher degree of flexibility in the design of the transceiver chain. In this paper we describe the implementation of a coded Orthogonal Frequency Division Multiplexing (OFDM) transceiver running over USRP2 boards. The baseband processing and the radio-frequency settings are designed for coping with a local area scenario as well as with the physical capabilities of the USRP2 boards. Moreover, a simple subcarrier blinding algorithm is proposed with the aim of compensating the common phase error in the symbol constellation due to the limited nominal accuracy of the local oscillators. Performance results show the effectiveness of the proposed architecture and settings for achieving Block Error Rate (BLER) results below 1% at 12 dB of Signal-to-Noise Ratio (SNR) without requiring a high precision reference clock.