Pervasive Monitoring Research Articles

Airborne particulate matter measurement and prediction with machine learning techniques

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

Multi-modal detection of fetal movements using a wearable monitor

The importance of Fetal Movement (FM) patterns as a biomarker for fetal health has been extensively argued in obstetrics. However, the inability of current FM monitoring methods, such as ultrasonography, to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. A small body of work has introduced wearable sensor-based FM monitors to address this gap. Despite promises in controlled environments, reliable instrumentation to monitor FM out-of-clinic remains unresolved, particularly due to the challenges of separating FMs from interfering artifacts arising from maternal activities. To date, efforts have been focused almost exclusively on homogenous (single) sensing and information fusion modalities, such as decoupled acoustic or accelerometer sensors. However, FM and related signal artifacts have varying power and frequency bandwidths that homogeneous sensor arrays may not capture or separate efficiently. In this investigation, we introduce a novel wearable FM monitor with an embedded heterogeneous sensor suite combining accelerometers, acoustic sensors, and piezoelectric diaphragms designed to capture a broad range of FM and interfering artifact signal features enabling more efficient isolation of both. We further outline a novel data fusion architecture combining data-dependent thresholding and machine learning to automatically detect FM and separate it from signal artifacts in real-world (home) environments. The performance of the device and the data fusion architecture are validated using 33 h of at-home use through concurrent recording of maternal perception of FM. The FM monitor detected an impressive 82 % of maternally sensed FMs with an overall accuracy of 90 % in recognizing FM and non-FM events. Consistency of detection was strongest from 32 gestational weeks onwards, which overlaps with the critical FM monitoring window for stillbirth prevention. We believe the multi-modal sensor fusion approach presented in this research will be a major milestone in the development of low-cost wearable FM monitors enabling pervasive monitoring of FM in unsupervised environments.

Implementing privacy and data confidentiality within the framework of the Internet of Things

Throughout the current and future worldwide Web network infrastructure, the notion of the Internet of Things (IoT) foresees the pervasive interconnection and cooperation of intelligent things. As such, the IoT is simply the next logical step in the expansion of the Web into the real world, ushering in a plethora of unique services that will enhance people's lives, give rise to entirely new economic sectors and smarten up the physical infrastructure upon which we rely, including buildings, cities and transportation networks. As smart devices permit widespread information collection or tracking, the IoT will not be able to reach its full potential if the vision for the IoT is not implemented appropriately. These helpful characteristics are countered by concerns over confidentiality, which have, to date, hindered the viability of IoT aspirations. In the face of widespread surveillance, the management of private information and the development of tools to limit or evade pervasive monitoring and analysis are two examples of the new difficulties brought about by such dangers. This paper considers the privacy concerns raised by the Internet of Things in depth.

Design of Pyrrole-Based Gate-Controlled Molecular Junctions Optimized for Single-Molecule Aflatoxin B1 Detection.

Food contamination by aflatoxins is an urgent global issue due to its high level of toxicity and the difficulties in limiting the diffusion. Unfortunately, current detection techniques, which mainly use biosensing, prevent the pervasive monitoring of aflatoxins throughout the agri-food chain. In this work, we investigate, through ab initio atomistic calculations, a pyrrole-based Molecular Field Effect Transistor (MolFET) as a single-molecule sensor for the amperometric detection of aflatoxins. In particular, we theoretically explain the gate-tuned current modulation from a chemical-physical perspective, and we support our insights through simulations. In addition, this work demonstrates that, for the case under consideration, the use of a suitable gate voltage permits a considerable enhancement in the sensor performance. The gating effect raises the current modulation due to aflatoxin from 100% to more than 103÷104%. In particular, the current is diminished by two orders of magnitude from the μA range to the nA range due to the presence of aflatoxin B1. Our work motivates future research efforts in miniaturized FET electrical detection for future pervasive electrical measurement of aflatoxins.

Performance Evaluation of an IoT Sensor Node for Health Monitoring of Artwork and Ancient Wooden Structures

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church.

Telehealth Support System Using Wireless Technologies: The Case of Ethiopia

Telehealth is an inter-disciplinary area and is basically the delivery of health and medical information and services over large and small distances using electronic information and communication technologies. Broadband wireless services available today, along with more powerful and convenient handheld devices, will enable a transformational change in health management and healthcare with the introduction of real-time monitoring and timely responses to a wide array of patient needs. Further, a network of low-cost sensors and wireless systems help in creating constantly vigilant and pervasive monitoring capability at home and at work. This paper addresses recent efforts in this growing field, including standards, system architectures, and lower layer protocols for body area networks. The paper also suggests the use of cooperative transmission-based strategies for such wireless topologies. Keywords: Telehealth, Telemedicine, Telecare, Wireless Technology. DOI: 10.7176/JIEA/12-2-01 Publication date: November 30 th 2022

Detection and assessment of Parkinson's disease based on gait analysis: A survey.

Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life.

The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.

Pervasive Monitoring of Public Health and Well-Being in Urban Areas with Blue-Green Solutions.

The urban environment seems to affect the citizens' health. The implementation of Blue-Green Solutions (BGS) in urban areas have been used to promote public health and citizens well-being. The aim of this paper is to present the development of an mHealth app for monitoring patients and citizens health status in areas where BGS will be applied. The "HEART by BioAsssist" application could be used as a health and other data collection tool as well as an "intelligent assistant" to monitor and promote patient's physical activity in areas with Blue-Green Solutions.

Cyber Security in Smart Grid Technology: A systematic Review

A Smart Grid (SG) is an electrical infrastructure that functions similarly to a traditional power grid but includes scalable and ubiquitous two-way communications, timely control capabilities, large-scale integration of dispersed resources, and resource efficiency. Pervasive smart monitoring technologies, autonomous equipment defect detecting, and self-healing are all included in the SG. It is intelligent infrastructure because of features such as 'Wireless Automatic Meter Reading' (WAMR), power system stability monitoring, distributed energy resource optimization, and Demand Response system applications. Given some of these distinguishing characteristics, SGs are without a doubt the world's future power infrastructure. A smart grid may link millions of people and devices in a network, necessitating its robustness, reliability, and security. Because of the long-range communication over open networks, one of the primary issues of today's smart grid systems is security. Cybercriminals, hackers, and terrorists are attempting to assault this national infrastructure in order to obtain control over automated energy monitoring and remote control for personal benefit. This research article provides a full overview of Smart Grids, including their design, methodology, and communication protocols, but the attention is mostly on the cyber-attacks that have been carried out and the remedies that have been advised for smart grids. Finally, we cover the numerous cyber security difficulties, as well as the topics that remain unsolved in the literature and the present solution area, as well as prospective research gaps.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives.

Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

Pervasive Monitoring System for Services and Servers in Healthcare Environment

Information systems are continuously evolving in nature and complexity. In the healthcare environment, information and information exchange are critical for providing health care at all levels. Hence, the healthcare environment is particularly relevant when discussing IT infrastructure monitoring and disaster prevention since availability and communication are vital for the proper functioning of healthcare units, whether acting in isolation or on a network.This work focuses on understanding what comprises a good monitoring solution, analyzing the monitoring solutions currently available in the optics of a multi-location healthcare environment and, finally, proposing a pervasive and comprehensive conceptual architecture for a monitoring system that is capable of handling such environments.

Camera-based Photoplethysmography (cbPPG) using smartphone rear and frontal cameras: an experimental study.

Non-expensive methods for measuring heart rate and oxygen saturation are of great importance in the scope of the COVID-19 outbreak to follow up on the symptoms and help to control the disease.Smartphones are widely available and their cameras can be used to acquire relevant physiological data, such as Photo-plethysmography (PPG) signals. Covering a light source and the camera sensor with a finger, it is possible to acquire the camera-based photoplethysmography (cbPPG) signal. Two methods were analyzed in this work, namely using the rear smartphone camera and the flash LED, and using the front camera and device display as a light source. The latter presents more advantages overall - in particular, greater control over the emitted light and finger detection - and better results were found when compared to a reference device.Clinical relevance- This technology allows the pervasive monitoring of the PPG signal using a standard smartphone, providing a tool to evaluate the subject's heart rate and its variability, respiration, blood oxygenation, etc.

E-PANOPTICON OF FACE-TO-DISPLAY WORKERS: FROM THE OFFICE TO THE HOME

Electronic Monitoring (EM) is becoming prevalent, enabling varied and pervasive monitoring of workplaces. The research design was a set of e-mail surveys. Quantitative data were analyzed using cross-tabulation of data, descriptive and chi-square tests statistics. The study provides an overview of e-worker monitoring in five countries. Twenty percent of respondents believe that their organization uses employee monitoring software to track their activities. Almost half of the e-workers believe that their activities are not being tracked by software. Nearby 1/10 of the face-to-display workers surveyed would trust their employer more using EM. Four-fifths of e-workers state that EM affects their productivity. Presented data emphasizes that companies using face-to-display workers monitoring software can negatively affect morale and productivity instead of producing better work. Further, employees are often unfamiliar with whether or not there is monitoring software tracking their activities. The study recommends that organizations should inform its employees before implementation of EM system to facilitate their positive attitudes

Solar energy harvesting for LoRaWAN-based pervasive environmental monitoring

<p>The aim of this paper is to discuss the characterisation of a solar energy harvesting system to be integrated in a wireless sensor node, to be deployed on means of transport to pervasively collect measurements of Particulate Matter (PM) concentration in urban areas. The sensor node is based on the use of low-cost PM sensors and exploits LoRaWAN connectivity to remotely transfer the collected data. The node also integrates GPS localisation features, that allow to associate the measured values with the geographical coordinates of the sampling site. In particular, the system is provided with an innovative, small-scale, solar-based powering solution that allows its energy self-sufficiency and then its functioning without the need for a connection to the power grid. Tests concerning the energy production of the solar cell were performed in order to optimise the functioning of the sensor node: satisfactory results were achieved in terms of number of samplings per hour. Finally, field tests were carried out with the integrated environmental monitoring device proving its effectiveness.</p>

IoT enabled integrated system for green energy into smart cities

With the substantial social and environmental change at the global level, increasing numbers of cities worldwide have been directed their infrastructure strategies towards sustainable mobility policies, building stock updating energy, increasing renewable energy production, improving waste management, and implementing ICT infrastructures. A significantly greater part of heat and electricity sources and the high level of integration of households and businesses in industry and utilities is needed in smart city energy systems. This article provides a comprehensive methodology to plan and evaluate the creation of smart power systems that lead to complex networks of energy supply technologies using diverse on-site and off-site resources. In this paper, the Internet of Things based Smart Green Energy (IoT-SGE) has been proposed for Smart Cities. With the implementation of IoT, smart cities can exquisitely control energy through pervasive monitoring and secure communications. This article focuses on designing an IoT-based smart energy management system enabled by deep reinforcement learning. The finding outcomes show that IoT sensors to detect energy consumption, predict the energy demand in smart cities and cost-saving.

A smart point-of-care compliance monitoring solution for brace treatment of adolescent idiopathic scoliosis patients

Scoliosis is a medical condition which occurs in adolescents, where an individual's spine develops curvature. A Thoracolumbosacral orthosis (TLSO) is a type of brace used as a long term treat-ment method to control the lateral curvature of the spine in adolescent idiopathic scoliosis (AIS). However, compliance of the daily prescribed duration and tightness of the brace is a big challenge, which plays a significant role in the success of brace treatment. In this paper, we designed and de-veloped a wearable multi-modal sensor solution, which is embedded into the AIS patient's brace. The custom-designed hardware consists of a sensor board, a force sensor & housing, a miniature triaxial accelerometer and gyroscope sensor. The force sensor records the force being exerted by the patient's brace padding, while the accelerometer and gyroscope sensors capture upper trunk movements, providing cues to determine the patient's activities and lifestyle to help with more accurate estimation of the two key parameters, including 1) the in-brace tightness level 2) the duration of the brace wear, in an unsupervised manner. Furthermore, we presented a signal pro-cessing and data analytic methodology to identify the duration and tightness of the brace wear, in addition to identifying various patient activities based on the fusion of continuous force and iner-tial motion sensor recordings. We, specifically, derived a signal processing activity identification method using power spectral density estimation of the sensor data recordings. Then, we evaluate of the effectiveness of brace treatment in a pervasive manner. The proposed method evaluates the duration of brace wear through the process of segmentation and calculates the tightness level of the brace by estimating the baseline force per segment of data. This analysis is done when the patient performs activities including sitting, standing, climbing, walking, running and lying. We investigated an experimental scenario in which the patient performs a series of pre-defined activ-ities at home during day-long segments of brace wear. All analysis is performed using pervasive sensor data recordings. The experimental results demonstrated that we achieved an overall accu-racy of 99.8%, 100% and 99.9% for patients 1, 2 and 3 respectively for semi-supervised activity detection. We continued our compliance study for approximately a month. The level of tight-ness of brace-fit gradually reduced over a period of 2 weeks by 30% as the compliance of brace treatment increased from 7% to 90% on average. Given our experimental results and objective observations, our proposed system is capable of arranging for re-fitting sessions automatically so that the physician can adjust the brace tightness levels for a more effective brace treatment. • Brace treatment for AIS patients is long-term & pervasive demanding self-compliance. • Pervasive monitoring can provide two key factors of duration and tightness of the brace-wear. • We design a multimodal smart brace monitoring system to estimate the two factors. • We design a robust signal processing recipe to undertake unsupervised recordings. • Our experiments demonstrated that our system generates reliable & robust estimates.

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals.

Traditionally, mental health specialists monitor their patients’ social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson’s correlation coefficient >70%) with individuals’ social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches.

Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a convenient and low-cost method to monitor the natural behaviour of hospital patients. However, their accuracy at quantifying limb motion, and clinical acceptability, have not yet been demonstrated. To this end we conducted a two-stage study: First, we compared the inertial accuracy of wrist-worn IMUs, both research-grade (Xsens MTw Awinda, and Axivity AX3) and consumer-grade (Apple Watch Series 3 and 5), and optical motion tracking (OptiTrack). Given the moderate to strong performance of the consumer-grade sensors, we then evaluated this sensor and surveyed the experiences and attitudes of hospital patients (N = 44) and staff (N = 15) following a clinical test in which patients wore smartwatches for 1.5–24 h in the second study. Results indicate that for acceleration, Xsens is more accurate than the Apple Series 5 and 3 smartwatches and Axivity AX3 (RMSE 1.66 ± 0.12 m·s−2; R2 0.78 ± 0.02; RMSE 2.29 ± 0.09 m·s−2; R2 0.56 ± 0.01; RMSE 2.14 ± 0.09 m·s−2; R2 0.49 ± 0.02; RMSE 4.12 ± 0.18 m·s−2; R2 0.34 ± 0.01 respectively). For angular velocity, Series 5 and 3 smartwatches achieved similar performances against Xsens with RMSE 0.22 ± 0.02 rad·s−1; R2 0.99 ± 0.00; and RMSE 0.18 ± 0.01 rad·s−1; R2 1.00± SE 0.00, respectively. Surveys indicated that in-patients and healthcare professionals strongly agreed that wearable motion sensors are easy to use, comfortable, unobtrusive, suitable for long-term use, and do not cause anxiety or limit daily activities. Our results suggest that consumer smartwatches achieved moderate to strong levels of accuracy compared to laboratory gold-standard and are acceptable for pervasive monitoring of motion/behaviour within hospital settings.