Sensor-based Devices Research Articles

Concept and Architecture for Applying Continuous Machine Learning in Multi-Access Routing at Underground Mining Vehicles

Autonomous moving vehicles facilitate mining of ore in underground mines. The vehicles are usually equipped with many sensor-based devices (e.g., Lidar, video camera, proximity sensor, etc.), which enable environmental monitoring, and remote control of the vehicles at the control center. Transfer of sensor-based data from the vehicles towards the control center is challenging due to limited connectivity enabled by the multi-access technologies of the communication infrastructure (e.g., 5G, Wi-Fi) within the underground mine, and the mobility of the vehicles. This paper presents design, development, and evaluation of a concept and architecture enabling continuous machine learning (ML) for optimizing route selection of real-time streaming data in a real and emulated underground mining environment. Continuous ML refers to training and inference based on the most recently available data. Experiments in the emulator indicated that utilization of a ML-based model (based on the RandomForestRegressor) in decision making achieved ~5–13% lower one-way delay in streaming data transfers, when compared to a simpler heuristic model.

A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization.

Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing plays a vital role in managing ever increasing processing demands from diverse IoT-based applications. Smart healthcare systems work with the assistance of sensor-based devices and automatic data collection and processing can speed up overall system functionality. In the proposed work, a novel framework for smart health care is presented where a series of activities are performed with prime objective of reducing latency and execution time. Principal component analysis is used for feature reduction and support vector machines with radial basis function kernel is used for classification purpose. Workload optimization on the fog nodes is implemented using genetic algorithm. Data collection process also involves preprocessing as a leading step for generating cleaner data. Amalgamation of intelligent and optimization techniques in the presented framework certainly improves the efficiency of the overall system. Experimental results reveal that proposed work outperforms the existing fog-assisted smart healthcare systems in terms of latency, execution time, overall system accuracy, and system stability.

Enabling Blockchain with IoMT Devices for Healthcare

Significant modifications have been seen in healthcare facilities over the past two decades. With the use of IoT-enabled devices, the monitoring and analysis of patient diagnostic parameters is made considerably easy. The new technology shift for medical field is IoMT. However, the problem of privacy for patient data and the security of information still a point to ponder. This research proposed a prototype model to integrate the blockchain and IoMT for providing better analysis of patient health factors. The authors suggested IoMT data to be collected over Edge Computing gateway devices and forward to Cloud Gateway. The three-layered decision making structure ensures the integrity of the data. The further analysis of information collected over sensor-based devices is done in the Cloud IoT Central Hub service. To ensure the secrecy and compliance of the patient data, Smart Contracts are integrated. After the exchange of smart contracts, a block of information is broadcast over the health blockchain. The P2P network makes it viable to retain all health statistics and further processing of information. The paper describes the scenario and experimental setup for a COVID-19 data-set analyzed in the proposed prototype mode. After the collection of information and decision making, the block of data is sent across all peer nodes. Thus, the power of IoMT and blockchain makes it easy for the healthcare worker to diagnose and handle patient data with privacy. The IoMT is integrated with artificial intelligence to enable decision making based on the classification of data. The results are saved as transactions in the blockchain hyperledger. This study demonstrates the prototype model with test data in a testing network with two peer nodes.

Intelligent Fault-Tolerance Data Routing Scheme for IoT-Enabled WSNs

Wireless sensor networks (WSNs) have become one of the essential components of the Internet of Things (IoT). In any IoT application, different sensor-based devices gather data from physical objects and transmit the sensed information to the base station (BS). The BS analyzes this information depending on the sensor location. A high-performance intelligent WSNs is essential for any IoT-based application. In this article, high-performance intelligent WSNs are referred to as IoT-enabled WSNs. In IoT-enabled WSNs, fault occurrence probability is much more than traditional networks. Faulty nodes and broken links affect the reliability of the IoT-enabled WSNs. Various fault-tolerance algorithms enhance the network’s reliability using multipath transmission, relay node placement, and backup node selection. However, these algorithms suffer from huge data transmission delays, packet overhead, and less detection accuracy. In this work, a multiobjective-deep reinforcement-learning (DRL)-based algorithm is proposed for fault tolerance in IoT-enabled WSNs. The main objective of this work is to detect the faulty nodes with high accuracy and less overhead. Furthermore, this work focuses on reliable data transmission after fault detection. Finally, a mobile sink (MS) is used for energy-efficient data gathering that significantly improves the network lifetime. Extensive simulations and theoretical analysis prove that the proposed algorithm outperformed as compared to the state-of-the-art algorithms in terms of fault detection accuracy (FDA), false alarm rate (FAR), false-positive rate (FPR), network lifetime, and throughput.

Molecularly Imprinted Surface Plasmon Resonance Sensor-Based Devices for Clinical Applications

In clinical applications, surface plasmon resonance sensors are widely used with many advantages including rapid and simultaneous response, high selective, sensitivity and specificity, portable and outstanding detection performance. Recently, the innovations and efforts in the field of sensor have expanded rapidly and have a wide range of applications, especially in the fields of environmental, medical, and pharmaceutical applications, and food safety. In particular, sensor systems were prepared with molecularly imprinted polymers create selective and specific recognition spaces for template molecules in a polymeric matrix, resulting in an important formation with surface modification techniques. In addition, this method provides a wide scope for imprinting various biomolecules with different three-dimensional structures and physical and chemical properties. Unlike time-consuming and complex surface modification methods, molecular imprinting technology offers sensitive, selective, fast, easy preparation, and use approaches especially for diagnostic, and theranostic applications. In addition, molecularly imprinted polymer-based sensors, with their low cost, reusability, high stability, physical and chemical robustness, have become very attractive methods for clinical applications with their sensitivity to very small structural changes in the structure of biomolecules. This review aims to summarize the molecular imprinting technology, the working principle of surface plasmon resonance (SPR) sensors, SPR sensor studies prepared with molecularly imprinted polymers in the literature and the future outlook of these concepts.

Enhancing Nutrition Care Through Real-Time, Sensor-Based Capture of Eating Occasions: A Scoping Review.

As food intake patterns become less structured, different methods of dietary assessment may be required to capture frequently omitted snacks, smaller meals, and the time of day when they are consumed. Incorporating sensors that passively and objectively detect eating behavior may assist in capturing these eating occasions into dietary assessment methods. The aim of this study was to identify and collate sensor-based technologies that are feasible for dietitians to use to assist with performing dietary assessments in real-world practice settings. A scoping review was conducted using the PRISMA extension for scoping reviews (PRISMA-ScR) framework. Studies were included if they were published between January 2016 and December 2021 and evaluated the performance of sensor-based devices for identifying and recording the time of food intake. Devices from included studies were further evaluated against a set of feasibility criteria to determine whether they could potentially be used to assist dietitians in conducting dietary assessments. The feasibility criteria were, in brief, consisting of an accuracy ≥80%; tested in settings where subjects were free to choose their own foods and activities; social acceptability and comfort; a long battery life; and a relatively rapid detection of an eating episode. Fifty-four studies describing 53 unique devices and 4 device combinations worn on the wrist (n = 18), head (n = 16), neck (n = 9), and other locations (n = 14) were included. Whilst none of the devices strictly met all feasibility criteria currently, continuous refinement and testing of device software and hardware are likely given the rapidly changing nature of this emerging field. The main reasons devices failed to meet the feasibility criteria were: an insufficient or lack of reporting on battery life (91%), the use of a limited number of foods and behaviors to evaluate device performance (63%), and the device being socially unacceptable or uncomfortable to wear for long durations (46%). Until sensor-based dietary assessment tools have been designed into more inconspicuous prototypes and are able to detect most food and beverage consumption throughout the day, their use will not be feasible for dietitians in practice settings.

Prediction of Freezing of Gait in Parkinson's Disease Using Unilateral and Bilateral Plantar-Pressure Data.

BackgroundFreezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson's disease (PD). FOG has been linked to falling, injury, and overall reduced mobility. Wearable sensor-based devices can detect freezes already in progress and provide a cue to help the person resume walking. While this is helpful, predicting FOG episodes before onset and providing a timely cue may prevent the freeze from occurring. Wearable sensors mounted on various body parts have been used to develop FOG prediction systems. Despite the known asymmetry of PD motor symptom manifestation, the difference between the most affected side (MAS) and least affected side (LAS) is rarely considered in FOG detection and prediction studies.MethodsTo examine the effect of using data from the MAS, LAS, or both limbs for FOG prediction, plantar pressure data were collected during a series of walking trials and used to extract time and frequency-based features. Three datasets were created using plantar pressure data from the MAS, LAS, and both sides together. ReliefF feature selection was performed. FOG prediction models were trained using the top 5, 10, 15, 20, 25, or 30 features for each dataset.ResultsThe best models were the MAS model with 15 features and the LAS and bilateral models with 5 features. The LAS model had the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MAS model achieved the highest specificity (84.9%) and lowest false positive rate (1.9 false positives/walking trial). Overall, the bilateral model was best with 77.3% sensitivity and 82.9% specificity. In addition, the bilateral model identified 94.2% of FOG episodes an average of 0.8 s before FOG onset. Compared to the bilateral model, the LAS model had a higher false positive rate; however, the bilateral and LAS models were similar in all the other evaluation metrics.ConclusionThe LAS model would have similar FOG prediction performance to the bilateral model at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased false positive rate may be acceptable to people with PD. Therefore, a single plantar pressure sensor placed on the LAS could be used to develop a FOG prediction system and produce performance similar to a bilateral system.

Performance monitoring of different types of photovoltaic cells

Scientific and technological developments make major changes in the energy sector for both generating energies from sustainable energy sources and monitoring of energy systems in ways that are more efficient. The main purpose of this study is to measure experimental data such as temperature, current and voltage by way of a sensor-based microcontroller device. Ergo, Arduino Uno is used for an ATmega328 microcontroller, voltage and current sensors are used to receive electric data that solar cells convert from solar radiation and a temperature and humidity sensor is used to collect weather data. The air temperature and solar radiation values have been taken from the Copernicus Atmosphere Monitoring Service. To store data, a secure digital memory card module (SD) is used. The performance evaluation of different types of photovoltaic cells, which are monocrystalline and polycrystalline cells, is aimed at in this study. To harvest energy, four similar monocrystalline and four similar polycrystalline cells are used. Monitoring the performance of each solar cell is the other objective of this study. Results of the performance of different types of solar cells are as expected, the starting point for further studies both for monitoring and for evaluating the performance of solar cells and sensor-based devices.

A Study on the Dexterity of Surgical Robotic Tools in a Highly Immersive Virtual Environment: Assessing Usability and Efficacy

Robot-assisted minimally invasive surgery (RAMIS) has produced noticeable benefits for patients in the recent years <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , making it a favorable approach for a wide range of surgeries. The benefits of improving the dexterity of patient side manipulators to enable surgeons to perform more complex tasks are offset by the increased complexity of teleoperation and cognitive and physical effort on the operator side typically. A right balance between higher dexterity and intuitive control in teleoperation is yet to be defined. In this study, a dexterous, anthropomorphic primary master controller was deployed to assess and compare the efficiency of simulated anthropomorphic surgical instruments in an immersive surgical concept. Virtual surgical training tasks were built using a gaming software engine (Unity) and performed using simulated surgical tools with extended degrees of freedom (DoF) in the surgical shaft and gripper and compared with the standard da Vinci (DV) grasper. The motion of the tools were controlled using commercial inertial measurement unit (IMU) sensor-based devices attached to the user’s arms and hands. This article summarizes results obtained from three studies with similar features but different levels of complexity, taken with both lay users with no experience in surgery or teleoperation and surgeons experienced in RAMIS. The results showed that more than 70% of users achieved better results using articulated tools but required more physical and mental effort for teleoperation.

Gait Assessment Using Wearable Sensor-Based Devices in People Living with Dementia: A Systematic Review.

The ability of people living with dementia to walk independently is a key contributor to their overall well-being and autonomy. For this reason, understanding the relationship between dementia and gait is significant. With rapidly emerging developments in technology, wearable devices offer a portable and affordable alternative for healthcare experts to objectively estimate kinematic parameters with great accuracy. This systematic review aims to provide an updated overview and explore the opportunities in the current research on wearable sensors for gait analysis in adults over 60 living with dementia. A systematic search was conducted in the following scientific databases: PubMed, Cochrane Library, and IEEE Xplore. The targeted search identified 1992 articles that were potentially eligible for inclusion, but, following title, abstract, and full-text review, only 6 articles were deemed to meet the inclusion criteria. Most studies performed adequately on measures of reporting, in and out of a laboratory environment, and found that sensor-derived data are successful in their respective objectives and goals. Nevertheless, we believe that additional studies utilizing standardized protocols should be conducted in the future to explore the impact and usefulness of wearable devices in gait-related characteristics such as fall prognosis and early diagnosis in people living with dementia.

LIAA: A listen interval adaptive adjustment scheme for green communication in event-sparse IoT systems

Due to the development of microprocessor technology, there are more than 20 billion sensor-based devices connected to the Internet of Things (IoT) to monitor physical phenomena and events. To reduce the energy used by idle listening, a low-duty cycle is often used in an event-sparse wireless sensor network. However, low-duty cycles bring large end-to-end delays. In this paper, a listen interval adaptive adjustment (LIAA) scheme is proposed to adjust the listen interval (LI) of a node to reduce end-to-end delays while maintaining the long lifetime of a network. The key idea is to make full use of the energy consumption imbalance in a network, to allow nodes away from the sink to use the residual energy to add listen intervals. The LIAA scheme has 3 sub-strategies. One is the basic add listen interval (BALI) strategy, in which the parent node adds listen intervals in the fixed active slots of its child nodes. Another strategy is the consecutive listen (CL) scheme, which is based on BALI, and listen intervals are added consecutively. The third strategy is the random add listen interval (RALI) scheme, which uses the idea of randomness to add listen intervals. The extensive theoretical analysis and experimental results show that the LIAA scheme proposed in this paper has better performance. Compared with the traditional scheme, the delays in the BALI scheme, CL scheme and RALI scheme were reduced by 24.03%, 23.45% and 39.41%, respectively, while the lifetime of the network was maintained.

Telemonitoring of Real-World Health Data in Cardiology: A Systematic Review.

Background: New sensor technologies in wearables and other consumer health devices open up promising opportunities to collect real-world data. As cardiovascular diseases remain the number one reason for disease and mortality worldwide, cardiology offers potent monitoring use cases with patients in their out-of-hospital daily routines. Therefore, the aim of this systematic review is to investigate the status quo of studies monitoring patients with cardiovascular risks and patients suffering from cardiovascular diseases in a telemedical setting using not only a smartphone-based app, but also consumer health devices such as wearables and other sensor-based devices. Methods: A literature search was conducted across five databases, and the results were examined according to the study protocols, technical approaches, and qualitative and quantitative parameters measured. Results: Out of 166 articles, 8 studies were included in this systematic review; these cover interventional and observational monitoring approaches in the area of cardiovascular diseases, heart failure, and atrial fibrillation using various app, wearable, and health device combinations. Conclusions: Depending on the researcher’s motivation, a fusion of apps, patient-reported outcome measures, and non-invasive sensors can be orchestrated in a meaningful way, adding major contributions to monitoring concepts for both individual patients and larger cohorts.

Influence of Cognitive Impairment on the Recovery of Subjects with Subacute Stroke Undergoing Upper Limb Robotic Rehabilitation.

Cognitive decline is often present in stroke survivors, with a significant impact on motor recovery. However, how specific cognitive domains could impact motor recovery after robotic rehabilitation in patients with stroke is still not well understood. In this study, we analyzed the relationship between cognitive impairment and the outcome of a robot-mediated upper limb rehabilitation intervention in a sample of 51 subacute stroke patients. Participants were enrolled and treated with a set of robotic and sensor-based devices. Before the intervention, patients underwent a cognitive assessment by means of the Oxford Cognitive Screen. To assess the effect of the 30-session rehabilitation intervention, patients were assessed twice with the following outcome measures: the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), to evaluate motor function; the Upper limb Motricity Index (MI), to evaluate upper limb muscle strength; the Modified Barthel Index (mBI), to evaluate activities of daily living and mobility. We found that deficits in spatial attention and executive functions impacted the mBI improvement, while language, number processing, and spatial attention deficits reduced the gains in the FMA-UE. These results suggest the importance to evaluate the cognitive functions using an adequate tool in patients with stroke undergoing a robotic rehabilitation intervention.

Security Framework for IoT Based Real-Time Health Applications

The amazing fusion of the internet of things (IoT) into traditional health monitoring systems has produced remarkable advances in the field of e-health. Different wireless body area network devices and sensors are providing real-time health monitoring services. As the number of IoT devices is rapidly booming, technological and security challenges are also rising day by day. The data generated from sensor-based devices need confidentiality, integrity, authenticity, and end-to-end security for safe communication over the public network. IoT-based health monitoring systems work in a layered manner, comprising a perception layer, a network layer, and an application layer. Each layer has some security, and privacy concerns that need to be addressed accordingly. A lot of research has been conducted to resolve these security issues in different domains of IoT. Several frameworks for the security of IoT-based e-health systems have also been developed. This paper introduces a security framework for real-time health monitoring systems to ensure data confidentiality, integrity, and authenticity by using two common IoT protocols, namely constrained application protocol (CoAP) and message query telemetry transports (MQTT). This security framework aims to defend sensor data against the security loopholes while it is continuously transmitting over the layers and uses hypertext transfer protocols (HTTPs) for this purpose. As a result, it shields from the breach with a very low ratio of risk. The methodology of this paper focuses on how the security framework of IoT-based real-time health systems is protected under the tiers of CoAP and HTTPs. CoAP works alongside HTTPs and is responsible for providing end-to-end security solutions.

Big Data Analytic Framework for Organizational Leverage

Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.

Virtualized Care Systems, Wearable Sensor-based Devices, and Real-Time Medical Data Analytics in COVID-19 Patient Health Prediction

(Poppas et al , 2020) 2 Conceptual Framework and Literature Review Patients who have progressed most from the increased convenience of telehealth services encounter obstacles leaving the house as a result of chronic illness, proceed along to see a specialist, or reside in an inadequately serviced location with unsatisfactory access to care Descriptive statistics of compiled data from the completed surveys were calculated when appropriate 4 Survey Methods and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States (Kaplan, 2021) For patients in the process of mental health treatment who are worried about COVID-19 exposure risk, telehealth has enabled uninterruptedness of mental health care (Hirko et al , 2020) Health systems have advanced automated logic flows that transfer moderate-to-high-risk COVID-19 confirmed individuals to nurse triage lines while allowing them to arrange video visits with healthcare providers so as to prevent transit to in-person care settings

Eversion, ecology, touch, and rain: A post-PC rhetoric

The post-PC era of computing offers digital rhetors an opportunity to innovate their inventional approaches. The new era is one in which an ecology of networked, distributed, sensor-based devices amplify our perceptions of self and world by changing the ecological relations that define our connections to our techno-social environments. By extending Casey Boyle’s posthuman practice of rhetorical invention to the new computational era, rhetoricians can develop digital interactive projects that move participants by amplifying the choric bases of their perceptions of self and world.

Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper.

Low Power Communication Technologies in Water Quality Monitoring System: A Review

The core part of the internet of things (IoT) is the wireless sensor network (WSN). WSN makes sensor-based devices communicate without any human action. In recent years, WSN and IoT technologies have transformed the environmental monitoring field. Water quality monitoring is one of the main environmental monitoring application. The communication unit is the main energy-consuming part of WSN. Since most of the WSN is powered by a battery, low power communication is very essential. So the main challenges in water quality monitoring applications are low power and wide communication coverage. This paper provides a critical review of various communication technologies used in water quality monitoring system. The communication technologies are ZigBee, Wi-Fi and newly emerging low power wireless technologies like LoRa, Sigfox and NB-IoT. The comparison of these technologies by considering different factors is also provided.

Bare Conductive® role as filler in mechanical and electrical reinforcement of polyvinyl alcohol-based strain sensor

Polyvinyl alcohol (PVA) is a water-soluble polymer typically suited for food and biomedical applications. However, its non-conductive properties limit PVA potential as material in electronic or sensor-based devices. In order to enhance the electrical as well as mechanical properties of PVA, Bare Conductive ® (BC) paste was incorporated into PVA solution using solution intercalation method. It is discovered that the addition of BC could enhance the mechanical and electrical properties of PVA. The tensile test data showed that the presence of carbon black in BC had resulted in the tensile stress improvement at about 50-170 % as compared to that of the original value. On the other hand, the electrical conductivity of PVA/BC composite film at a higher wt% had demonstrated a significant increment in resistance/length ratio (1.86 kΩ/cm at 0.2 wt%BC).