Body Sensor Research Articles

An efficient dynamic sampling method for energy harvesting body sensor node

An efficient dynamic sampling method for energy harvesting body sensor node

Retraction Note: Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

Retraction Note: Design and evaluation of novel hybrid quantum resistant cryptographic system for enhancing security in wireless body sensor networks

A TECHNIQUE TO SECURE DATA TRANSMISSION IN WIRELESS BODY AREA NETWORKS

The wireless body area networks have contributed to the changes in healthcare sector by enabling continuous monitoring of biomedical parameters remotely on the patient body and offering real time data transmission. Upon collection of the vital signs, they are transmitted to the gateway node which then forwards them to the remote hospital medical servers. Here, these physiological data items are analyzed and necessary action is taken. Such physiological data include electrocardiograms (ECGs), blood pressure, body temperature, blood oxygen levels, respiratory rate, Electroencephalogram (EEG), Electromyogram (EMG), glucose levels, physical activity and Electrodermal Activity (EDA). In this paper, we develop a technique for securely transmitting this sensitive data from the body sensor unit (BSU) to the remote hospital medical server (HMS). KEYWORDS: Sensors, BSU, WBAN, security, privacy

Knowledge Transfer for Body Sensor Networks: Characterization and Resistance of Negative Transfer in Overconstrained Environments

Knowledge Transfer for Body Sensor Networks: Characterization and Resistance of Negative Transfer in Overconstrained Environments

MEDCO: an efficient protocol for data compression in wireless body sensor network

MEDCO: an efficient protocol for data compression in wireless body sensor network

Optimizing Energy Harvesting in Wireless Body Area Networks: A Deep Reinforcement Learning Approach to Dynamic Sampling

In energy-harvesting wireless body area networks (EH-WBAN), the self-sustainability of body sensor nodes without compromising the service quality criteria is very important. In remote vital signs monitoring applications, the sampling rate of body nodes in each period should be determined to achieve this goal. There are two fundamental challenges in determining the sampling rate: 1) In EH-WBAN, the rate of the harvestable energy is time-dependent and unpredictable. 2) The vital signs of the patient have different change rates relevant to the time. Therefore, a technique is required that can learn the changes of the harvestable energy and the change rates of vital signs simultaneously and specify the proper sampling rate for BNs. The reinforcement learning algorithms are among the efficient solutions for this problem because they are capable of learning environmental uncertainties. Previous RL-based methods for determining the BN's sampling rate have three fundamental problems: 1) focus on the optimization of the transmission energy and ignore the sensing energy, 2) only affect one of the two aspects of energy or sensed data in determining the sampling rate and 3) discretization of the problem space does not ensure the determination of the optimal sampling rate. Therefore, this paper proposes a method named “deep reinforcement learning-based dynamic sampling” (DRDS) which first formulates the sampling rate determination problem as a Markov decision process (MDP) and proposes a deep deterministic policy gradient algorithm (DDPG) to solve it. The proposed method considers both energy and data variability aspects in determining the sampling rate. Two parameters, the super-capacitor’s voltage level, and ambient light intensity, are considered for the energy aspect; for the data variability aspect, the long short-term memory (LSTM) algorithm is developed to predict the change rate of data in the next round. Simulations indicate that by preserving the data integrity, the proposed method can decrease the sampling rate and unnecessary data transmission by about 49.95% and 89.7%, respectively, compared with state-of-the-art methods, and allow the sensor to achieve self-sustainability.

IoT-Based Health Monitoring System

Wireless body area networks, known as body sensor networks, are wireless networks of wearable computing devices. In this case, the wireless network deployed is the Internet of Things or IoT. This special WBAN will allow nurses and doctors to have a real-time view of the patient's physiological data and thereby detect unusual activities in the patient's health. This is achieved by having a device that allows the patient to collect physiological data using sensors controlled by an Arduino microcontroller board. Doctors can then log into the website and monitor the patient's health status in real-time and over some time.

IoT-FAR: A multi-sensor fusion approach for IoT-based firefighting activity recognition

Inadequate training poses a significant risk of injury among young firefighters. Although Human Activity Recognition (HAR) algorithms have shown potential in monitoring and evaluating performance, most existing studies focus on daily activities and have difficulty distinguishing complex firefighting tasks. This study introduces the Internet of things (IoT)-based wearable firefighting activity recognition (IoT-FAR) system which employs a multi-modal sensor fusion approach to achieve comprehensive activity recognition during firefighting training. The IoT-FAR comprises five wearable body sensor nodes and a coordinator node. This study explores the significance of features extracted from the surface electromyography, heart rate, and inertial measurement units in firefighting training activity recognition. A hybrid machine learning (HML)-based network is proposed, which integrates three models: one trained with all features (MA), another with upper body features (MU), and a third with lower body features (ML). The proposed HML-SVM-RBF1-RF2 network achieves superior performance, with a mean recall of 93.94%, mean precision of 90.94%, and a mean accuracy rate of 98.29%. Additionally, the study introduces the specialized firefighting training associated activities (SFTAA) dataset, which includes endurance training activities involving self-contained breathing apparatus (SCBA) conducted by eighteen firefighters. This dataset represents preliminary work towards building a comprehensive dataset covering various events and scenarios for tracking firefighter activities. The IoT-FAR system also demonstrates the potential use of misclassified activities as evaluation metrics for assessing firefighter training performance.

DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks

SummaryWireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Multi-Objective Technology-Based Approach to Home Healthcare Routing Problem Considering Sustainability Aspects

Background: This research aims to solve a home healthcare vehicle routing problem (HHCVRP) model that considers the social aspect of sustainability and will be implemented in smart cities. In addition to the dynamism and uncertainty caused by variations in the patient’s condition, the proposed model considers parameters and variables that enhance its practicability, such as assuming different levels of patient importance (priority). Methods: The model was solved using a metaheuristic algorithm approach via the Ant Colony Optimization algorithm and the Non-Dominated Sorting technique due to the ability of such a combination to work out with dynamic models with uncertainties and multi-objectives. Results: This study proposes a novel mathematical model by integrating body sensors on patients to keep updating their conditions and prioritizing critical conditions in service. The sensitivity analysis demonstrates that using a heart rate sensor improves service quality and patient satisfaction without affecting the energy consumed. In addition, quality costs are increased if the importance levels of patients increase. Conclusions: The suggested model can assist healthcare practitioners in tracking patients’ health conditions to improve the quality of service and manage workload effectively. A trade-off between patient satisfaction and service provider satisfaction should be maintained.

Navigating the biomechanical landscape: Enhanced methods for drug analysis

In drug analysis and detection, chemiluminescence is a standard technical method based on the idea that the concentration of observed substances in the chemical detection system and the chemical luminescence intensity establish a linear quantitative relationship under certain conditions. Bio-mechanical landscape (BML), which uses bioengineered systems to simulate essential elements of the human tumour microenvironment, is already enhancing cancer biology and aiding clinical translation. The majority of illicit substances have dangerous cardiovascular side effects that might range from an irregular heartbeat to a heart attack. Cardiovascular disease, including blocked veins and bacterial infections of the blood arteries and heart valves, may also result from injecting illicit narcotics. The challenging characteristics of such a cardiovascular disease using drug analysis are heart failure, low blood pressure and headaches. Hence, in this research, drug analysis has improved wearable body sensor network-enabled artificial neural network (WBSN-ANN) technologies for cardiovascular disease. Medicine and drug development because they provide information on patient status in the clinical context and vital information on pharmacodynamics activity, effectiveness, and safety throughout the research process. The process results in a research-intensive organization finding a novel chemical or biological compound and developing it into a medicine that patients can legally utilize for drug discovery and development.

UPDEMIN: an efficient protocol for predicting patient upcoming situation, decision and intervention in wireless body sensor network

UPDEMIN: an efficient protocol for predicting patient upcoming situation, decision and intervention in wireless body sensor network

Advancing Security in Lightweight S-WBSN for E-Healthcare

Wireless body sensor networks (WBSNs) are a critical component of e-healthcare, enabling continuous monitoring of vital signs and other health parameters. However, the limited capacity of WBSNs poses a significant challenge to securing data transmission. A novel architecture for Secure Wireless Body Sensor Networks (S-WBSN) has been introduced, focusing on efficiency and robust security. The S-WBSN architecture employs a combination of the OTP-Q stream block cipher and the Diffie-Hellman key exchange algorithm to ensure secure data transmission and mutual authentication between critical components. The OTP-Q algorithm encrypts data obtained from WBSN sensors, while the Diffie-Hellman algorithm establishes a secure communication channel between the WBSN, Local Processing Center (LPC), and Data Server. This innovative approach effectively meets essential security requirements, including mutual authentication and privacy preservation. It also demonstrates notable efficiency, consuming fewer CPU cycles than existing state-of-the-art security approaches. Comparative analysis showcases reduced processing time for encryption and decryption, making the S-WBSN architecture an attractive solution for e-healthcare data monitoring security. In summary, the S-WBSN architecture significantly enhances the security posture of e-healthcare data transfer, prioritizing efficiency, and robust protection. It sets itself apart from contemporary healthcare data monitoring security methodologies by delivering a comprehensive solution that addresses the unique challenges of WBSNs.

A deep learning-based real-time hypothermia and hyperthermia monitoring system with a simple body sensor.

A real-time hypothermia and hyperthermia monitoring system with a simple body sensor based on a Convolutional Neural Network (CNN) is presented. The sensor is produced with 3D-printed thermochromic material. Due to the color change feature of thermochromic materials with temperature, 3D-printed thermochromic Polylactic Acid (PLA) material was used to monitor temperature changes visually. In this paper, we have used the transfer learning technique and fine-tuned the AlexNet CNN. Thirty images for each temperature class between 28-44°C and 510 image data were used in the algorithm. We used 80% and 20% of the data for training and validation. We achieved 96.1% accuracy of validation with a fine-tuned AlexNet CNN. The material's characteristics suggest that it could be employed in delicate temperature sensing and monitoring applications, particularly for hypothermia and hyperthermia.

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

Efficient and Reliable Emergency Routing for Wireless Body Area Networks

AbstractThe development of Wireless Body Area Networks (WBAN) is currently in progress with the aim of facilitating communication among small body sensors. This data can be employed to offer prompt or timely aid to individuals who are not just in a critical state but also those who are unable to promptly commute to medical facilities due to physical limitations, congested roadways, or their place of residence. Another crucial requirement for WBAN is their capability to deliver quality of service for diverse forms of network traffic. Privacy and system security are crucial to protecting patient records throughout use and storage by healthcare providers. This study introduces a novel emergency data handling method for emergency healthcare communication networks. Furthermore, the proposed system provides Reliable Emergency Routing to ensure secure transfer of emergency and non‐emergency WBAN data rewards are used to establish routes to securely transmit non‐emergency and emergency data in highly exposed situations while minimizing data loss and delay. Simulations showed that the recommended approach outperformed current protocols in energy consumption, latency, packet delivery ratio throughput, and communication overhead.

A Pilot Study Investigating the Use of Serum Glial Fibrillary Acidic Protein to Monitor Changes in Brain White Matter Integrity After Repetitive Head Hits During a Single Collegiate Football Game.

Repetitive head hits (RHHs) in sports and military settings are increasingly recognized as a risk factor for adverse neurological outcomes, but they are not currently tracked. Blood-based biomarkers of concussion have recently been shown to increase after nonconcussive RHHs during a single sporting contest, raising the possibility that they could be used in real time to monitor the brain's early response to repeated asymptomatic head hits. To test this hypothesis, we measured GFAP in serum immediately before (T0), immediately after (T1) and 45 min (T2) after a single collegiate football game in 30 athletes. Glial fibrillary acidic protein (GFAP) changes were correlated with three measures of head impact exposure (number of hits, total linear acceleration, and total rotational acceleration captured by helmet impact sensors) and to changes in brain white matter (WM) integrity, estimated by regional changes in fractional anisotropy (FA) and mean diffusivity (MD) on diffusion tensor imaging from 24 h before (T1) to 48 h after (T3) the game. To account for the potentially confounding effects of physical exertion on GFAP, correlations were adjusted for kilocalories of energy expended during the game measured by wearable body sensors. All 30 participants were male with a mean age of 19.5 ± 1.2 years. No participant had a concussion during the index game. We observed a significant increase in GFAP from T0 to T1 (mean 79.69 vs. 91.95 pg/mL, p = 0.008) and from T0 to T2 (mean 79.69 vs. 99.21 pg/mL, p < 0.001). WM integrity decreased in multiple WM regions but was statistically significant in the right fornix (mean % FA change -1.43, 95% confidence interval [CI]: -2.20, -0.66). T0 to T2 increases in GFAP correlated with reduced FA in the left fornix, right fornix, and right medical meniscus and with increased MD in the right fornix (r-values ranged from 0.59 to 0.61). Adjustment for exertion had minimal effect on these correlations. GFAP changes did not correlate to head hit exposure, but after adjustment for exertion, T0 to T2 increases correlated with all three hit metrics (r-values ranged from 0.69 to 0.74). Thus, acute elevations in GFAP after a single collegiate football game of RHHs correlated with in-game head hit exposure and with reduced WM integrity 2 days later. These results suggest that GFAP may be a biologically relevant indicator of the brain's early response to RHHs during a single sporting event. Developing tools to measure the neurological response to RHHs on an individual level has the potential to provide insight into the heterogeneity in adverse outcomes after RHH exposure and for developing effective and personalized countermeasures. Owing to the small sample size, these findings should be considered preliminary; validation in a larger, independent cohort is necessary.

Research and Development in Biomedical Prosthetics

In this report the sole data is based on innovation and development in biomedical prosthetics field. The current technology used for prosthetic includes 3D printing, device implants, digital design tools and more. There are several types of biomedical engineering, such as tissue, genetic, neural and stem cells, as well as chemical and clinical engineering for health care. Many electronic and magnetic method uses sensors in equipment such as Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI) scans, Electroencephalography (EEG). The notation of the idea is using sensors in body supporting equipment’s or an attachment as prosthetics with the incorporation of AI that could get results in movement or functioning in any nonfunctioning body part as desired.

OXYGEN HOMEOSTASIS FROM SEA LEVEL TO HIGH ALTITUDES

In the beginning there was no oxygen (O2) on planet Earth, today the atmosphere hosts 21%, after considerable oscillations of it through millions of years. Over time, living organisms evolved by mounting mechanisms to detect and respond to fluctuations in O2 concentration in their internal environment. Oxygen sensors in the carotid body, and the HIF system in all cells, play a crucial role in maintaining O2 homeostasis at the organism and cellular level. Therefore, in normoxia, HIFs are rapidly synthesized and destroyed, but in hypoxia, their degradation is inhibited. HIFs, regulate protein expression to compensate for hypoxia, increasing O2 delivery to tissues, and reducing O2 consumption. Millions of people live at high altitudes, such as in the Andean and Tibetan plateaus. Newborns in these regions weigh less than those in the lowlands, but this difference in Tibetans is smaller than in the Andeans. There is a greater increase in utero­placental blood flow in Andean and Tibetan women compared to pregnancies in Europeans, at the same altitude. These increases are due to synthesis in NO and PGI2, generated by HIFs. The Andean populations display Chronic Mountain Sickness (CMS), characterized by increase in hemoglobin concentration, diminution of PO2 and pulmonary hypertension. In contrast, the Tibetans do not have these conditions due to polymorphisms of two genes, the EPAS-1 that encodes for HIF-2a, expressing polymorphisms with loss-of-function and the EGLN-1 gene that encodes for prolyl hydroxylase-2, with gain-of-function polymorphisms, thus reducing HIFs tasks, decreasing hemoglobin concentration and pulmonary arterial hypertension.