Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model∗

Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.Approach. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.Main results. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.Significance. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.

Refined Feature-based Multi-frame and Multi-scale Fusing Gate network for accurate segmentation of plaques in ultrasound videos

The accurate segmentation of carotid plaques in ultrasound videos will provide evidence for clinicians to evaluate the properties of plaques and treat patients effectively. However, the confusing background, blurry boundaries and plaque movement in ultrasound videos make accurate plaque segmentation challenging. To address the above challenges, we propose the Refined Feature-based Multi-frame and Multi-scale Fusing Gate Network (RMFG_Net), which captures spatial and temporal features in consecutive video frames for high-quality segmentation results and no manual annotation of the first frame. A spatial-temporal feature filter is proposed to suppress the noise of low-level CNN features and promote the detailed target area. To obtain a more accurate plaque position, we propose a transformer-based cross-scale spatial location algorithm, which models the relationship between adjacent layers of consecutive video frames to achieve stable positioning. To make full use of more detailed and semantic information, multi-layer gated computing is applied to fuse features of different layers, ensuring sufficient useful feature map aggregation for segmentation. Experiments on two clinical datasets demonstrate that the proposed method outperforms other state-of-the-art methods under different evaluation metrics, and it processes images with a speed of 68 frames per second which is suitable for real-time segmentation. A large number of ablation experiments were conducted to demonstrate the effectiveness of each component and experimental setting, as well as the potential of the proposed method in ultrasound video plaque segmentation tasks. The codes can be publicly available from https://github.com/xifengHuu/RMFG_Net.git.

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

Slippery Epidural ECoG Electrode for High-Performance Neural Recording and Interface

Chronic implantation of an epidural Electrocorticography (ECoG) electrode produces thickening of the dura mater and proliferation of the fibrosis around the interface sites, which is a significant concern for chronic neural ECoG recording applications used to monitor various neurodegenerative diseases. This study describes a new approach to developing a slippery liquid-infused porous surface (SLIPS) on the flexible ECoG electrode for a chronic neural interface with the advantage of increased cell adhesion. In the demonstration, the electrode was fabricated on the polyimide (PI) substrate, and platinum (Pt)-gray was used for creating the porous nanocone structure for infusing the silicone oil. The combination of nanocone and the infused slippery oil layer created the SLIPS coating, which has a low impedance (4.68 kΩ) level favourable for neural recording applications. The electrochemical impedance spectroscopy and equivalent circuit modelling also showed the effect of the coating on the recording site. The cytotoxicity study demonstrated that the coating does not have any cytotoxic potentiality; hence, it is biocompatible for human implantation. The in vivo (acute recording) neural recording on the rat model also confirmed that the noise level could be reduced significantly (nearly 50%) and is helpful for chronic ECoG recording for more extended neural signal recording applications.

Health, Security and Fire Safety Process Optimisation Using Intelligence at the Edge

The proliferation of sensors to capture parametric measures or event data over a myriad of networking topologies is growing exponentially to improve our daily lives. Large amounts of data must be shared on constrained network infrastructure, increasing delays and loss of valuable real-time information. Our research presents a solution for the health, security, safety, and fire domains to obtain temporally synchronous, credible and high-resolution data from sensors to maintain the temporal hierarchy of reported events. We developed a multisensor fusion framework with energy conservation via domain-specific "wake up" triggers that turn on low-power model-driven microcontrollers using machine learning (TinyML) models. We investigated optimisation techniques using anomaly detection modes to deliver real-time insights in demanding life-saving situations. Using energy-efficient methods to analyse sensor data at the point of creation, we facilitated a pathway to provide sensor customisation at the "edge", where and when it is most needed. We present the application and generalised results in a real-life health care scenario and explain its application and benefits in other named researched domains.

Recent Advancements in Electrochemical Biosensors for Monitoring the Water Quality

The release of chemicals and microorganisms from various sources, such as industry, agriculture, animal farming, wastewater treatment plants, and flooding, into water systems have caused water pollution in several parts of our world, endangering aquatic ecosystems and individual health. World Health Organization (WHO) has introduced strict standards for the maximum concentration limits for nutrients and chemicals in drinking water, surface water, and groundwater. It is crucial to have rapid, sensitive, and reliable analytical detection systems to monitor the pollution level regularly and meet the standard limit. Electrochemical biosensors are advantageous analytical devices or tools that convert a bio-signal by biorecognition elements into a significant electrical response. Thanks to the micro/nano fabrication techniques, electrochemical biosensors for sensitive, continuous, and real-time detection have attracted increasing attention among researchers and users worldwide. These devices take advantage of easy operation, portability, and rapid response. They can also be miniaturized, have a long-life span and a quick response time, and possess high sensitivity and selectivity and can be considered as portable biosensing assays. They are of special importance due to their great advantages such as affordability, simplicity, portability, and ability to detect at on-site. This review paper is concerned with the basic concepts of electrochemical biosensors and their applications in various water quality monitoring, such as inorganic chemicals, nutrients, microorganisms' pollution, and organic pollutants, especially for developing real-time/online detection systems. The basic concepts of electrochemical biosensors, different surface modification techniques, bio-recognition elements (BRE), detection methods, and specific real-time water quality monitoring applications are reviewed thoroughly in this article.

SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB

Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review

The coronavirus disease 19 (COVID-19) pandemic that has been raging in 2020 does affect not only the physical state but also the mental health of the general population, particularly, that of the healthcare workers. Given the unprecedented large-scale impacts of the COVID-19 pandemic, digital technology has gained momentum as invaluable social interaction and health tracking tools in this time of great turmoil, in part due to the imposed state-wide mobilization limitations to mitigate the risk of infection that might arise from in-person socialization or hospitalization. Over the last five years, there has been a notable increase in the demand and usage of mobile and wearable devices as well as their adoption in studies of mental fitness. The purposes of this scoping review are to summarize evidence on the sweeping impact of COVID-19 on mental health as well as to evaluate the merits of the devices for remote psychological support. We conclude that the COVID-19 pandemic has inflicted a significant toll on the mental health of the population, leading to an upsurge in reports of pathological stress, depression, anxiety, and insomnia. It is also clear that mobile and wearable devices (e.g., smartwatches and fitness trackers) are well placed for identifying and targeting individuals with these psychological burdens in need of intervention. However, we found that most of the previous studies used research-grade wearable devices that are difficult to afford for the normal consumer due to their high cost. Thus, the possibility of replacing the research-grade wearable devices with the current smartwatch is also discussed.

Enhancing osteoregenerative potential of biphasic calcium phosphates by using bioinspired ZIF8 coating

Biphasic calcium phosphate ceramics (BCPs) have been extensively used as a bone graft in dental clinics to reconstruct lost bone in the jaw and peri-implant hard tissue due to their good bone conduction and similar chemical structure to the teeth and bone. However, BCPs are not inherently osteoinductive and need additional modification and treatment to enhance their osteoinductivity. The present study aims to develop an innovative strategy to improve the osteoinductivity of BCPs using unique features of zeolitic imidazolate framework-8 (ZIF8). In this method, commercial BCPs (Osteon II) were pre-coated with a zeolitic imidazolate framework-8/polydopamine/polyethyleneimine (ZIF8/PDA/PEI) layer to form a uniform and compact thin film of ZIF8 on the surface of BCPs. The surface morphology and chemical structure of ZIF8 modified Osteon II (ZIF8-Osteon) were confirmed using various analytical techniques such as XRD, FTIR, SEM, and EDX. We evaluated the effect of ZIF8 coating on cell attachment, growth, and osteogenic differentiation of human adipose-derived mesenchymal stem cells (hADSCs). The results revealed that altering the surface chemistry and topography of Osteon II using ZIF8 can effectively promote cell attachment, proliferation, and bone regeneration in both in vitro and in vivo conditions. In conclusion, the method applied in this study is simple, low-cost, and time-efficient and can be used as a versatile approach for improving osteoinductivity and osteoconductivity of other types of alloplastic bone grafts.

Multi-Walled Carbon Nanotubes-Based Sensors for Strain Sensing Applications

The use of multi-walled carbon nanotube (MWCNT)-based sensors for strain-strain applications is showcased in this paper. Extensive use of MWCNTs has been done for the fabrication and implementation of flexible sensors due to their enhanced electrical, mechanical, and thermal properties. These nanotubes have been deployed both in pure and composite forms for obtaining highly efficient sensors in terms of sensitivity, robustness, and longevity. Among the wide range of applications that MWCNTs have been exploited for, strain-sensing has been one of the most popular ones due to the high mechanical flexibility of these carbon allotropes. The MWCNT-based sensors have been able to deduce a broad spectrum of macro- and micro-scaled tensions through structural changes. This paper highlights some of the well-approved conjugations of MWCNTs with different kinds of polymers and other conductive nanomaterials to form the electrodes of the strain sensors. It also underlines some of the measures that can be taken in the future to improve the quality of these MWCNT-based sensors for strain-related applications.

A Review on the Use of Impedimetric Sensors for the Inspection of Food Quality

This paper exhibits a thorough review of the use of impedimetric sensors for the analysis of food quality. It helps to understand the contribution of some of the major types of impedimetric sensors that are used for this application. The deployment of impedimetric sensing prototypes has been advantageous due to their wide linear range of responses, detection of the target analyte at low concentrations, good stability, high accuracy and high reproducibility in the results. The choice of these sensors was classified on the basis of structure and the conductive material used to develop them. The first category included the use of nanomaterials such as graphene and metallic nanowires used to form the sensing devices. Different forms of graphene nanoparticles, such as nano-hybrids, nanosheets, and nano-powders, have been largely used to sense biomolecules in the micro-molar range. The use of conductive materials such as gold, copper, tungsten and tin to develop nanowire-based prototypes for the inspection of food quality has also been shown. The second category was based on conventional electromechanical circuits such as electronic noses and other smart systems. Within this sector, the standardized systems, such as electronic noses, and LC circuit -based systems have been explained. Finally, some of the challenges posed by the existing sensors have been listed out, along with an estimate of the increase in the number of sensors employed to assess food quality.

The Effects of Random Stimulation Rate on Measurements of Auditory Brainstem Response

Electroencephalography (EEG) signal is an electrophysiological recording from electrodes placed on the scalp to reflect the electrical activities of the brain. Auditory brainstem response (ABR) is one type of EEG signals in response to an auditory stimulus, and it has been widely used to evaluate the potential disorders of the auditory function within the brain. Currently, the ABR measurements in the clinic usually adopt a fixed stimulation rate (FSR) technique in which the late evoked response could contaminate the ABR signals and deteriorate the waveform differentiation after averaging, thus compromising the overall auditory function assessment task. To resolve this issue, this study proposed a random stimulation rate (RSR) method by integrating a random interval between two adjacent stimuli. The results showed that the proposed RSR method was consistently repeatable and reliable in multiple trials of repeated measurements, and there was a large amplitude of successive late evoked response that would contaminate the ABR signals for conventional FSR methods. The ABR waveforms of the RSR method showed better wave I–V morphology across different stimulation rates and stimulus levels, and the improved ABR morphology played an important role in early diagnoses of auditory pathway abnormities. The correlation coefficients as functions of averaging time showed that the ABR waveform of the RSR method stabilizes significantly faster, and therefore, it could be used to speed up current ABR measurements with more reliable testing results. The study suggests that the proposed method would potentially aid the adequate reconstruction of ABR signals towards a more effective means of hearing loss screening, brain function diagnoses, and potential brain–computer interface.

Mussel inspired ZIF8 microcarriers: a new approach for large-scale production of stem cells

Metal–organic frameworks (MOFs) have high porosity, large surface area, and tunable functionality and have been widely used for drug loading. The aim of this study was to exploit unique features of zeolitic imidazolate framework-8 (ZIF8) to develop an innovative composite microcarrier (MC) for human mesenchymal stem cells (hMSCs) adhesion and proliferation. ZIF8 MCs were prepared by immobilization of polydopamine/polyethyleneimine (PDA/PEI) and ZIF8 on the surface of polystyrene beads. The chemical properties of MCs such as coating stability and homogeneity were characterized by different techniques such as ATR-FTIR, XRD, EDX, SEM, and contact angle. The prepared MCs were tested using human adipose-derived mesenchymal stem cells (hADSCs) in both static and dynamic conditions, and results were compared to a commercially available MC (Star-Plus), polydopamine coated MCs and amine-functionalized MCs as a control. Results show that PDA/PEI/ZIF8 coated MCs (in short: ZIF8 MCs) provides an excellent biocompatible environment for hADSCs adhesion and growth. In conclusion, ZIF8 MCs represent suitable and low-cost support for hADSCs culture and expansion, which can maximize cell yield and viability while preserving hADSCs multipotency. The present findings have revealed this strategy has the potential for chemical and topographical modification of MCs in tissue engineering applications.

Mussel inspired ZIF8 microcarriers with high surface area, biocompatibility, and nanoscale surface roughness are applied to enhance mesenchymal stem cell attachment and proliferation in 3D cell culture.

Silicon-Based Sensors for Biomedical Applications: A Review

The paper highlights some of the significant works done in the field of medical and biomedical sensing using silicon-based technology. The use of silicon sensors is one of the pivotal and prolonged techniques employed in a range of healthcare, industrial and environmental applications by virtue of its distinct advantages over other counterparts in Microelectromechanical systems (MEMS) technology. Among them, the sensors for biomedical applications are one of the most significant ones, which not only assist in improving the quality of human life but also help in the field of microfabrication by imparting knowledge about how to develop enhanced multifunctional sensing prototypes. The paper emphasises the use of silicon, in different forms, to fabricate electrodes and substrates for the sensors that are to be used for biomedical sensing. The electrical conductivity and the mechanical flexibility of silicon vary to a large extent depending on its use in developing prototypes. The article also explains some of the bottlenecks that need to be dealt with in the current scenario, along with some possible remedies. Finally, a brief market survey is given to estimate a probable increase in the usage of silicon in developing a variety of biomedical prototypes in the upcoming years.

Laser-Assisted Printed Flexible Sensors: A Review

This paper provides a substantial review of some of the significant research done on the fabrication and implementation of laser-assisted printed flexible sensors. In recent times, using laser cutting to develop printed flexible sensors has become a popular technique due to advantages such as the low cost of production, easy sample preparation, the ability to process a range of raw materials, and its usability for different functionalities. Different kinds of laser cutters are now available that work on samples very precisely via the available laser parameters. Thus, laser-cutting techniques provide huge scope for the development of prototypes with a varied range of sizes and dimensions. Meanwhile, researchers have been constantly working on the types of materials that can be processed, individually or in conjugation with one another, to form samples for laser-ablation. Some of the laser-printed techniques that are commonly considered for fabricating flexible sensors, which are discussed in this paper, include nanocomposite-based, laser-ablated, and 3D-printing. The developed sensors have been used for a range of applications, such as electrochemical and strain-sensing purposes. The challenges faced by the current printed flexible sensors, along with a market survey, are also outlined in this paper.

Finger-to-Heart (F2H): Authentication for Wireless Implantable Medical Devices

Any proposal to provide security for implantable medical devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a finger-to-heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD. Doctors can gain access to the IMD and perform emergency treatment by scanning the patient's finger tip instead of asking the patient for passwords/security tokens, thereby, achieving the necessary trade-off. In the scheme, an improved minutia-cylinder-code-based fingerprint authentication algorithm is proposed for the IMD by reducing the length of each feature vector and the number of query feature vectors. Experimental results show that the improved fingerprint authentication algorithm significantly reduces both the size of messages in transmission and computational overheads in the device, and thus, can be utilized to secure the IMD. Compared to existing electrocardiogram signal-based security schemes, the F2H scheme does not require the IMD to capture or process biometric traits in every access attempt since a fingerprint template is generated and stored in the IMD beforehand. As a result, the scarce resources in the IMD are conserved, making the scheme sustainable as well as energy efficient.

Molecularly Imprinted Polymer-Based Electrochemical Biosensor for Bone Loss Detection

Serum C-terminal telopeptide of type I collagen (CTx-I) assays quantify the fragment of CTx-I released throughout the procedure of bone remodeling. CTx-I is a key bone turnover biomarker where any variation in the level of CTx-I can be an indication of increased bone resorption. This study focuses on a new strategy for the prognosis of bone loss by monitoring the concentration of CTx-I in serum. An interdigital capacitive sensor together with electrochemical impedance spectroscopy was employed to assess the dielectric properties of the test solution. Artificial antibodies have been prepared for CTx-I molecules using the molecular imprinting technique. The sensor was functionalized using the synthesized molecular imprinted polymer in order to introduce the selectivity of CTx-I biomarker to the sensor. Calibration experiments were performed using different known concentration of sample solutions. The proposed biosensor showed a good linear response between 0.1 and 2.5 ng/mL. The detection limit of 0.09 ng/mL was found, encompassing the normal reference ranges required for recognition of bone turnover. Unknown real serum samples obtained from sheep blood were analysed using the proposed biosensor. The validation of the suggested technique was done using enzyme-linked immunosorbent assay (ELISA). The developed biosensor exhibited a good correlation with ELISA.

Detection Methodologies for Pathogen and Toxins: A Review

Pathogen and toxin-contaminated foods and beverages are a major source of illnesses, even death, and have a significant economic impact worldwide. Human health is always under a potential threat, including from biological warfare, due to these dangerous pathogens. The agricultural and food production chain consists of many steps such as harvesting, handling, processing, packaging, storage, distribution, preparation, and consumption. Each step is susceptible to threats of environmental contamination or failure to safeguard the processes. The production process can be controlled in the food and agricultural sector, where smart sensors can play a major role, ensuring greater food quality and safety by low cost, fast, reliable, and profitable methods of detection. Techniques for the detection of pathogens and toxins may vary in cost, size, and specificity, speed of response, sensitivity, and precision. Smart sensors can detect, analyse and quantify at molecular levels contents of different biological origin and ensure quality of foods against spiking with pesticides, fertilizers, dioxin, modified organisms, anti-nutrients, allergens, drugs and so on. This paper reviews different methodologies to detect pathogens and toxins in foods and beverages.

Smart Sensing System for the Prognostic Monitoring of Bone Health

The objective of this paper is to report a novel non-invasive, real-time, and label-free smart assay technique for the prognostic detection of bone loss by electrochemical impedance spectroscopy (EIS). The proposed system incorporated an antibody-antigen-based sensor functionalization to induce selectivity for the C-terminal telopeptide type one collagen (CTx-I) molecules—a bone loss biomarker. Streptavidin agarose was immobilized on the sensing area of a silicon substrate-based planar sensor, patterned with gold interdigital electrodes, to capture the antibody-antigen complex. Calibration experiments were conducted with various known CTx-I concentrations in a buffer solution to obtain a reference curve that was used to quantify the concentration of an analyte in the unknown serum samples. Multivariate chemometric analyses were done to determine the performance viability of the developed system. The analyses suggested that a frequency of 710 Hz is the most discriminating regarding the system sensitivity. A detection limit of 0.147 ng/mL was achieved for the proposed sensor and the corresponding reference curve was linear in the range of 0.147 ng/mL to 2.669 ng/mL. Two sheep blood samples were tested by the developed technique and the results were validated using enzyme-linked immunosorbent assay (ELISA). The results from the proposed technique match those from the ELISA.

A Hybrid Memetic Framework for Coverage Optimization in Wireless Sensor Networks

One of the critical concerns in wireless sensor networks (WSNs) is the continuous maintenance of sensing coverage. Many particular applications, such as battlefield intrusion detection and object tracking, require a full-coverage at any time, which is typically resolved by adding redundant sensor nodes. With abundant energy, previous studies suggested that the network lifetime can be maximized while maintaining full coverage through organizing sensor nodes into a maximum number of disjoint sets and alternately turning them on. Since the power of sensor nodes is unevenly consumed over time, and early failure of sensor nodes leads to coverage loss, WSNs require dynamic coverage maintenance. Thus, the task of permanently sustaining full coverage is particularly formulated as a hybrid of disjoint set covers and dynamic-coverage-maintenance problems, and both have been proven to be nondeterministic polynomial-complete. In this paper, a hybrid memetic framework for coverage optimization (Hy-MFCO) is presented to cope with the hybrid problem using two major components: 1) a memetic algorithm (MA)-based scheduling strategy and 2) a heuristic recursive algorithm (HRA). First, the MA-based scheduling strategy adopts a dynamic chromosome structure to create disjoint sets, and then the HRA is utilized to compensate the loss of coverage by awaking some of the hibernated nodes in local regions when a disjoint set fails to maintain full coverage. The results obtained from real-world experiments using a WSN test-bed and computer simulations indicate that the proposed Hy-MFCO is able to maximize sensing coverage while achieving energy efficiency at the same time. Moreover, the results also show that the Hy-MFCO significantly outperforms the existing methods with respect to coverage preservation and energy efficiency.

An Efficient Biometric-Based Algorithm Using Heart Rate Variability for Securing Body Sensor Networks

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

Rapid and molecular selective electrochemical sensing of phthalates in aqueous solution

Reported research work presents real time non-invasive detection of phthalates in spiked aqueous samples by employing electrochemical impedance spectroscopy (EIS) technique incorporating a novel interdigital capacitive sensor with multiple sensing thin film gold micro-electrodes fabricated on native silicon dioxide layer grown on semiconducting single crystal silicon wafer. The sensing surface was functionalized by a self-assembled monolayer of 3-aminopropyltrietoxysilane (APTES) with embedded molecular imprinted polymer (MIP) to introduce selectivity for the di(2-ethylhexyl) phthalate (DEHP) molecule. Various concentrations (1-100 ppm) of DEHP in deionized MilliQ water were tested using the functionalized sensing surface to capture the analyte. Frequency response analyzer (FRA) algorithm was used to obtain impedance spectra so as to determine sample conductance and capacitance for evaluation of phthalate concentration in the sample solution. Spectrum analysis algorithm interpreted the experimentally obtained impedance spectra by applying complex nonlinear least square (CNLS) curve fitting in order to obtain electrochemical equivalent circuit and corresponding circuit parameters describing the kinetics of the electrochemical cell. Principal component analysis was applied to deduce the effects of surface immobilized molecular imprinted polymer layer on the evaluated circuit parameters and its electrical response. The results obtained by the testing system were validated using commercially available high performance liquid chromatography diode array detector system.

Assessment of pelt quality in leather making using a novel non-invasive sensing approach

Excessive removal of structural material from skin during leather processing results in unattractive crease formation in leather. It is difficult to detect this in pelts at an early processing stage as it only becomes really apparent once the skin is made into leather. There would be great advantages in detecting the problem at the pickled pelt stage (skins treated with sodium sulphide and lime, bated with enzymes, and then preserved in NaCl and sulphuric acid) so that adjustments to the processing could be made to mitigate the effect. A novel bio-sensor for inspection of pickled lamb pelts has been fabricated and developed. The sensor has the planar Interdigital structure. The experimental results show that the sensor has a great potential to predict the quality of leather in a non-invasive and non-destructive way.

Studies on central nervous system function in diabetes mellitus

Fifty-seven insulin dependent (IDDM) and non-insulin dependent (NIDDM) diabetic patients and 25 controls were studied. Patients with history of strokes, hypoglycaemia, hearing impairment, diabetic retinopathy, etc, were excluded. Clinical examination of central nervous system (CNS) and computerised tomography scan of brain were absolutely normal in all cases. Neuroelectrophysiological tests done were the visual evoked potential (VEP), brainstem auditory evoked response (BAER) and somatosensory evoked potential (SEP). The mean VEP latency was significantly raised in both NIDDM and IDDM compared with controls. The mean BAER and SEP latencies were both significantly raised in NIDDM but not in IDDM. The percentage of cases with abnormally raised CNS latencies were as follows: In NIDDM, VEP-16.7%, BAER-50% and SEP-26.7%; in IDDM, VEP-11.1%, BAER-14.8% and SEP-18.5%. Thus, subclinical CNS dysfunction is common in diabetes mellitus particularly in NIDDM and this can be reliably detected by measuring the CNS latencies, specially VEP.