Non-invasive hydration level estimation in human body using Galvanic Skin Response

Dehydration and overhydration, both have mild to severe medical implications on human health. Tracking Hydration Level (HL) is, therefore, very important particularly in patients, kids, elderly, and athletes. The limited solutions available for the estimation of HL are commonly inefficient, invasive, or require clinical trials. Need for a non-invasive auto-detection solution is imminent to track HL on a regular basis. To the best of authors’ knowledge, it is for the first time a Machine Learning (ML) based auto-estimation solution is proposed that uses Galvanic Skin Response (GSR) as a proxy of HL in the human body. Various body postures, such as sitting and standing, and distinct hydration states, hydrated vs dehydrated, are considered during the data collection and analysis phases. Six different ML algorithms are trained using real GSR data, and their efficacy is compared for different parameters (i.e., window size, feature combinations etc). It is reported that a simple algorithm like K-NN outperforms other algorithms with accuracy upto 87.78% for the correct estimation of the HL

Development of an Intelligent Real-time Multi-Person Respiratory Illnesses Sensing System using SDR Technology

Respiration monitoring plays a vital role in human health monitoring, as it is an essential indicator of vital signs. Respiration monitoring can help determine the physiological state of the human body and provide insight into certain illnesses. Recently, non-contact respiratory illness sensing methods have drawn much attention due to user acceptance and great potential for real-world deployment. Such methods can reduce stress on healthcare facilities by providing modern digital health technologies. This digital revolution in the healthcare sector will provide inexpensive and unobstructed solutions. Non-contact respiratory illness sensing is effective as it does not require users to carry devices and avoids privacy concerns. The primary objective of this research work is to develop a system for continuous real-time sensing of respiratory illnesses. In this research work, the non-contact software-defined radio (SDR) based RF technique is exploited for respiratory illness sensing. The developed system measures respiratory activity imprints on channel state information (CSI). For this purpose, an orthogonal frequency division multiplexing (OFDM) transceiver is designed, and the developed system is tested for single-person and multi-person cases. Nine respiratory illnesses are detected and classified using machine learning algorithms (ML) with maximum accuracy of 99.7% for a single-person case. Three respiratory illnesses are detected and classified with a maximum accuracy of 93.5% and 88.4% for two- and three-person cases, respectively. The research provides an intelligent, accurate, continuous, and real-time solution for respiratory illness sensing. Furthermore, the developed system can also be deployed in office and home environments

Spatio-Temporal Modelling of Noise Pollution

An undesired or hazardous outdoor sound produced by human activities is referred to as environmental noise. For example, the noise emitted through industrial activities and transportation networks such as road, rail and air traffic. In Malaysia, most of the schools located very close to the roadside and near busy places such as cities, shops, and residential areas. This study aims to analyze the environmental noise in terms of spatial and temporal analysis in two primary schools in Terengganu State.  The noise monitoring had conducted in two (2) primary schools with different land use; residential area (Batu Rakit Primary School) and commercial area (Paya Bunga Primary School) on the school and non-school days by using Sound Level Meter (SLM). The spatial mapping had constructed by using SketchUp® 2018 and Surfer® version 11 software. The noise level between both study areas was significantly different based on a p-value of less than 0.05. It also surpassed the Department of Environment (DOE) of Malaysia's permitted limit, with the Equivalent Noise Level (LAeq) in residential areas being greater than in commercial areas due to traffic volume and noise from nearby activities. Lastly, the area near the roadside has higher critical noise pollution compared with the location that furthers from the roadside. In conclusion, this study is useful in creating awareness to the public about the noise pollution effect on primary school students and is also can be used for mitigation measures to have a better place for students to study

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions

A comprehensive survey on hybrid communication in context of molecular communication and terahertz communication for body-centric nanonetworks

With the huge advancement of nanotechnology over the past years, the devices are shrinking into micro-scale, even nano-scale. Additionally, the Internet of nano-things (IoNTs) are generally regarded as the ultimate formation of the current sensor networks and the development of nanonetworks would be of great help to its fulfilment, which would be ubiquitous with numerous applications in all domains of life. However, the communication between the devices in such nanonetworks is still an open problem. Body-centric nanonetworks are believed to play an essential role in the practical application of IoNTs. BCNNs are also considered as domain specific like wireless sensor networks and always deployed on purpose to support a particular application. In these networks, electromagnetic and molecular communications are widely considered as two main promising paradigms and both follow their own development process. In this survey, the recent developments of these two paradigms are first illustrated in the aspects of applications, network structures, modulation techniques, coding techniques and security to then investigate the potential of hybrid communication paradigms. Meanwhile, the enabling technologies have been presented to apprehend the state-of-art with the discussion on the possibility of the hybrid technologies. Additionally, the inter-connectivity of electromagnetic and molecular body-centric nanonetworks is discussed. Afterwards, the related security issues of the proposed networks are discussed. Finally, the challenges and open research directions are presented

Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19

The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence

Enhancing system performance through objective feature scoring of multiple persons' breathing using non-contact RF approach

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

Selectivity function scheduler for IEEE 802.11e HCCA access mode

ABSTRACT In this paper, we present a scheduling algorithm that enhances the performance of the standard IEEE 802.11e scheduler for the Hybrid Coordination Function Controlled Channel Access mode. The main contribution in designing the proposed scheduler is the ability to accommodate multiple streams with different levels of Quality of Service requirements concurrently running on the same station. This is achieved by dynamically calculating the Transmission Opportunities of each active traffic stream (TS) and the appropriate Service Interval of each active station. The proposed algorithm optimizes the utilization of the scarce bandwidth resources by only polling active stations. The algorithm incorporates a selectivity function to assign polling priorities to the active streams only based on their diverse requirements and their link-attainable transmission rates. The performance of the proposed Selectivity Function Scheduler (SFS) scheme is evaluated against the standard scheduler. Simulation results show that the SFS outperforms the standard scheduler in terms of enhancing streams' throughput, reducing packet dropping ratio and maintaining high fairness amongst the admitted TS