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.

Measuring compressive loads on a 'smart' lumbar interbody fusion cage: Proof of concept.

There are several complications associated with lumbar interbody fusion surgery however, pseudarthrosis (non-union) presents a multifaceted challenge in the postoperative management of the patient. Rates of pseudarthrosis range from 3 to 20 % in patients with healthy bone and 20 to 30 % in patients with osteoporosis. The current methods in post-operative follow-up - radiographs and CT, have high false positive rates and poor agreement between them. The aim of this study was to develop and test a proof-of-concept load-sensing interbody cage that may be used to monitor fusion progression. Piezoresistive pressure sensors were calibrated and embedded within a polyether ether ketone (PEEK) interbody cage. Silicone and poly (methyl methacrylate) (PMMA) were inserted in the graft regions to simulate early and solid fusion. The load-sensing cage was subjected to distributed and eccentric compressive loads up to 900 N between synthetic lumbar vertebral bodies. Under maximum load, the anterior sensors recorded a 56-58 % reduction in pressure in the full fusion state compared to early fusion. Lateral regions measured a 36-37 % stress reduction while the central location reduced by 45 %. The two graft states were distinguishable by sensor-recorded pressure at lower loads. The sensors more effectively detected left and right eccentric loads compared to anterior and posterior. Further, the load-sensing cage was able to detect changes in endplate stiffness. The proof-of-concept 'smart' cage could detect differences in fusion state, endplate stiffness, and loading conditions in this in vitro experimental setup.

A biomechanical comparison of posterior fixation approaches in lumbar fusion using computed tomography based lumbosacral spine modelling.

Extreme lateral interbody fusion (XLIF) may be performed with a standalone interbody cage, or with the addition of unilateral or bilateral pedicle screws; however, decisions regarding supplemental fixation are predominantly based on clinical indicators. This study examines the impact of posterior supplemental fixation on facet micromotions, cage loads and load-patterns at adjacent levels in a L4-L5 XLIF at early and late fusion stages. CT data from an asymptomatic subject were segmented into anatomical regions and digitally stitched into a surface mesh of the lumbosacral spine (L1-S1). The interbody cage and posterior instrumentation (unilateral and bilateral) were inserted at L4-L5. The volumetric mesh was imported into finite element software for pre-processing, running nonlinear static solves and post-processing. Loads and micromotions at the index-level facets reduced commensurately with the extent of posterior fixation accompanying the XLIF, while load-pattern changes observed at adjacent facets may be anatomically dependent. In flexion at partial fusion, compressive stress on the cage reduced by 54% and 72% in unilateral and bilateral models respectively; in extension the reductions were 58% and 75% compared to standalone XLIF. A similar pattern was observed at full fusion. Unilateral fixation provided similar stability compared to bilateral, however there was a reduction in cage stress-risers with the bilateral instrumentation. No changes were found at adjacent discs. Posterior supplemental fixation alters biomechanics at the index and adjacent levels in a manner that warrants consideration alongside clinical information. Unilateral instrumentation is a more efficient option where the stability requirements and subsidence risk are not excessive.

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.

Sleep postures monitoring based on capacitively coupled electrodes and deep recurrent neural networks.

BACKGROUND: Capacitively coupled electrode (CC electrode), as a non-contact and unobtrusive technology for measuring physiological signals, has been widely applied in sleep monitoring scenarios. The most common implementation is capacitive electrocardiogram (cECG) that could provide useful clinical information for assessing cardiac function and detecting cardiovascular diseases. In the current study, we sought to explore another potential application of cECG in sleep monitoring, i.e., sleep postures recognition. METHODS: Two sets of experiments, the short-term experiment, and the overnight experiment, were conducted. The cECG signals were measured by a smart mattress based on flexible CC electrodes and sleep postures were recorded simultaneously. Then, a classifier model based on a deep recurrent neural network (RNN) was proposed to distinguish sleep postures (supine, left lateral and right lateral). To verify the reliability of the proposed model, leave-one-subject-out cross-validation was introduced. RESULTS: In the short-term experiment, the overall accuracy of 96.2% was achieved based on 30-s segment, while the overall accuracy was 88.8% using one heart beat segment. For the unconstrained overnight experiment, the accuracy of 91.0% was achieved based on 30-s segment, while the accuracy was 81.4% using one heart beat segment. CONCLUSIONS: The results suggest that cECG could render valuable information about sleep postures detection and potentially be helpful for sleep disorder diagnosis.

Wearable Sensors for Healthcare: Fabrication to Application.

This paper presents a substantial review of the deployment of wearable sensors for healthcare applications. Wearable sensors hold a pivotal position in the microelectronics industry due to their role in monitoring physiological movements and signals. Sensors designed and developed using a wide range of fabrication techniques have been integrated with communication modules for transceiving signals. This paper highlights the entire chronology of wearable sensors in the biomedical sector, starting from their fabrication in a controlled environment to their integration with signal-conditioning circuits for application purposes. It also highlights sensing products that are currently available on the market for a comparative study of their performances. The conjugation of the sensing prototypes with the Internet of Things (IoT) for forming fully functioning sensorized systems is also shown here. Finally, some of the challenges existing within the current wearable systems are shown, along with possible remedies.

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.

Feasibility of a Planar Coil-Based Inductive-Capacitive Water Level Sensor with a Quality-Detection Feature: An Experimental Study.

This paper presents a new water-level-sensing mechanism based on planar coils fabricated on a printed circuit board (PCB). In addition to level, the sensor detects any relative increase in conductivity compared to that of clean water, which is an indicator of its quality. The sensing mechanism utilizes the eddy current induced in the water column, the corresponding change in the coil inductance, and the change in the turn-to-turn capacitance of the coil in the presence of water. Although several level sensors are available, there is none that gives the level and quality information using a single sensing element. Since both water quantity and quality measurements are fundamental in realizing efficient water and wastewater management, obtaining these two parameters from the same sensor is very beneficial. A scalable, planar coil-based sensor that helps achieve this goal is designed, fabricated, and tested in a laboratory setting. The results illustrate that the reactance of the sensor coil measured at a frequency (1 kHz for the prototype) much lower than the self-resonance of the coil gives reliable information about the level of water, while the measurement made at resonance, using an inductance-to-digital converter, is a clear indicator of its conductivity and, hence, quality.

Compact Four-Port Circularly Polarized MIMO X-Band DRA.

A circularly polarized (CP) multi-input multioutput (MIMO) dielectric resonator (DR) antenna (DRA) with compact size and four ports is implemented. CP radiation was achieved using the deformed DR geometry excited with aperture coupled feeding. A CPDRA with a single and two ports is investigated. The defected ground structure (DGS) was incorporated into the antenna for improving the isolation between the ports. The DGS was incorporated in such a way that the required phase difference between the generated orthogonal degenerate modes is preserved. This concept could be utilized in implementing a compact four-port CP antenna. The MIMO antenna provides a 10 dB impedance bandwidth of 38% (8.5-12.5 GHz) and a 3 dB AR bandwidth of 9.32% (9.2-10.1 GHz). The gain of the implemented antenna was around 6 dBi in the band where CP radiation was achieved. The MIMO performance parameters were calculated, and their values remained within the acceptable limits. The implemented antenna could suitably be used in X-band applications.

TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space.

Nowadays, there is tremendous growth in the Internet of Things (IoT) applications in our everyday lives. The proliferation of smart devices, sensors technology, and the Internet makes it possible to communicate between the digital and physical world seamlessly for distributed data collection, communication, and processing of several applications dynamically. However, it is a challenging task to monitor and track objects in real-time due to the distinct characteristics of the IoT system, e.g., scalability, mobility, and resource-limited nature of the devices. In this paper, we address the significant issue of IoT object tracking in real time. We propose a system called 'TrackInk' to demonstrate our idea. TrackInk will be capable of pointing toward and taking pictures of visible satellites in the night sky, including but not limited to the International Space Station (ISS) or the moon. Data will be collected from sensors to determine the system's geographical location along with its 3D orientation, allowing for the system to be moved. Additionally, TrackInk will communicate with and send data to ThingSpeak for further cloud-based systems and data analysis. Our proposed system is lightweight, highly scalable, and performs efficiently in a resource-limited environment. We discuss a detailed system's architecture and show the performance results using a real-world hardware-based experimental setup.

Finite element modeling of temporal bone graft changes in XLIF: Quantifying biomechanical effects at adjacent levels.

Extreme lateral interbody fusion allows for the insertion of a large-footprint interbody cage while maintaining the presence of natural stabilizing ligaments and the facets. It is unclear how the load-distribution mechanisms through these structures alter with temporal changes in the bone graft. The aim of this study was to examine the effects of temporal bone graft changes on load distribution among the cage, graft, and surrounding spinal structures using finite element analysis. Thoracolumbosacral spine computed tomography data from an asymptomatic male subject were segmented into anatomical regions of interest and digitally stitched to generate a surface mesh of the lumbar spine (L1-S1). The interbody cage was inserted into the L4-L5 region during surface meshing. A volumetric mesh was generated and imported into finite element software for pre-processing, running nonlinear static solves, and post-processing. Temporal stiffening was simulated in the graft region with unbonded (Soft Callus, Temporal Stages 1-3, Solid Graft) and bonded (Partial Fusion, Full Fusion) contact. In flexion and extension, cage stress reduced by 20% from the soft callus to solid graft state. Force on the graft was directly related to its stiffness, and load-share between the cage and graft improved with increasing graft stiffness, regardless of whether contact was fused with the endplates. Fused contact between the cage-graft complex and the adjacent endplates shifted load-distribution pathways from the ligaments and facets to the implant, however, these changes did not extend to adjacent levels. These results suggest that once complete fusion is achieved, the existing load paths are seemingly diminished.

Wearable Sensors and Systems in the IoT.

Wearable smart devices are widely used to determine various physico-mechanical parameters at chosen intervals. The proliferation of such devices has been driven by the acceptance of enhanced technology in society [...].

Development and Progress in Sensors and Technologies for Human Emotion Recognition.

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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.

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.