Wireless Research Articles

Mouse Exploratory Behaviour in the Open Field with and without NAT-1 EEG Device: Effects of MK801 and Scopolamine.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with onboard memory for direct high-resolution recording of brain activity for >48 h. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). The mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between the drug treatments. Hypoactivity was globally observed as a consistent outcome in the MK801-treated mice and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological, and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in, e.g., drug discovery and models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

ESFL: Efficient Split Federated Learning Over Resource-Constrained Heterogeneous Wireless Devices

ESFL: Efficient Split Federated Learning Over Resource-Constrained Heterogeneous Wireless Devices

Intelligent and Optimal System for Monitoring Environmental Variables and Solar Luminosity, with solar path tracker and telemetry

In this proposed research are analyzed general solutions and techniques to optimize the solar luminosity measurement by a designed smart system, as well as its usability by the solar energy conversion and its storing. However, many times, this kind of systems have not an optimal solar path tracker and as a consequence it can not be measured the full solar luminosity during all the day, therefore, in this work are designed optimal mathematical models and algorithms to enhance the solar path tracker according to measure solar luminosity and getting optimal solar energy conversion/storing, which is useful to study geographical conditions before to build solar energy conversion centers. As well as, in this research also is studied and suggested some techniques to get wireless data communication regarding the measured environmental variables, measured solar luminosity and stored electrical energy as a consequence of the solar energy conversion, which can be transmitted, so far away, to a central monitoring center by Radio Frequency (RF) waves.

Characterising Skin Electrical Impedance Using Tape Stripping Methods: A Bioelectrical Study of a Porcine Model.

Background Recent advancements in ultra-low power electronics and wireless devices have facilitated the widespread adoption of wearable technology for fitness and health monitoring, paving the way for personalized medicine. Microneedle-based devices, comprising small epidermal patches that penetrate the skin's stratum corneum to potentially access biomarkers in the extracellular fluid of the viable epidermis, represent a promising innovation in this field. Objectives This project aimed to develop and validate a novel method to evaluate microneedle engagement in the skin in real-time. To our knowledge, there are no studies published to date that have characterized the electrical impedance of stratum corneum and epidermis using the tape stripping method to selectively remove cell layers. Additionally, no studies have been published comparing the electrical impedance of fresh to frozen-thawed porcine skin. The objective of this study was to develop and validate a novel method to evaluate microneedle engagement in skin, in real-time, that does not require processing of the tissue. Methods A tape stripping technique was employed to selectively remove the stratum corneum from fresh and frozen-thawed porcine skin samples which were then electrically characterized using an excitation frequency of 5 kHz with a peak Voltage of 1 V. Results This study demonstrated a mean impedance reduction of 97.08 ± 1.3 % for fresh porcine skin, and 98.04 ± 0.3 % for frozen-thawed porcine skin when transitioning from the surface stratum corneum to the viable epidermis. The correlation between the reduction of impedance and the number of tape strips across all 18 test sites was significant (r = 0.98, p < 0.00001). However, comparing the skin impedance of the fresh and frozen-thawed specimens showed poor equivalence, with the frozen-thawed sites approximately 5.5 times the impedance of the fresh sites before any tape stripping, and 4.19 times greater after 30 tape strips. Conclusions These findings suggest that monitoring for an interelectrode impedance decrease of greater than 95% between two projections of a microneedle device could provide a rapid and effective evaluation of skin engagement, crucial for advancing the development and clinical application of microneedle-based technologies in personalized medicine. The study also underscores the impact of the freeze-thaw process on the mechanical and electrical properties of skin, which is crucial for standardizing testing protocols.

(Invited) Skin-Interfaced Wearable Sweat Biosensors

The rising research interest in personalized medicine promises to revolutionize traditional medical practices. This presents a tremendous opportunity for developing wearable devices toward predictive analytics and treatment. In this talk, I will introduce our efforts in developing wearable biosensors for non-invasive molecular analysis. Such wearables can autonomously access body fluids (e.g., human sweat) across the activities and continuously measure a broad spectrum of analytes including metabolites, nutrients, hormones, proteins, and drugs. I will highlight our recent works in the design of wearable and wireless devices for the real-time electrochemical detection of picomolar-level proteins (i.e., inflammatory biomarker C-reactive) and hormones (i.e., estradiol) in sweat. Laser engraving, inkjet printing, and direct-ink-writing 3D printing are used to manufacture high-performance nanomaterials-based biosensors at large scale and low cost. The clinical value of our wearable systems is evaluated through various human trials toward precision nutrition, stress/mental health assessment, chronic disease management, and drug personalization. I will also discuss our research progress on energy harvesting from the human body and the environment to realize battery-free wireless wearable sensing. These wearable technologies could open the door to a wide range of personalized monitoring, diagnostic, and therapeutic applications.

Longitudinal Tracking of Calprotectin and Interleukin-6 in Sweat and Saliva Using a Sweat Wearable Device

Introduction: This work demonstrates an electrochemical sensor for the application of continuous, noninvasive measurement of inflammation in sweat and saliva through the biomarkers Calprotectin and Interleukin-6 (IL-6), respectively. Calprotectin and IL-6 play a part in the systemic inflammation inherent to chronic inflammatory conditions, such as Inflammatory Bowel Disease (IBD), rheumatoid arthritis, cystic fibrosis, etc. Sweat and saliva represent comparable external mediums through which inflammation can be tracked continuously and noninvasively. Materials and Methods: Calprotectin and IL-6 were measured in sweat and saliva for 2 chronically inflamed subjects and 10 healthy subjects over a two-day period. Sweat measurements were performed using a wearable, skin-based electrochemical sensor utilizing electrochemical impedance spectroscopy (EIS) to quantify Calprotectin and IL-6 concentrations continuously in passively expressed sweat. Calprotectin and IL-6 levels were measured in real time and transmitted to a wireless device application. Figure 1 illustrates the sensor and wearable device design and composition alongside an application interface displaying the wearer's Calprotectin levels measured in real time. Synchronously collected saliva was measured for Calprotectin and IL-6 to characterize the relationship between sweat and saliva expression, establish the relationship between Calprotectin and IL-6 expression as markers of chronic inflammation, and investigate the sweat and salivary diurnal patterns of marker expression for inflamed and healthy subjects. In addition, we tracked sweat Calprotectin over a 40-hour period for chronically inflamed and healthy subjects with varying levels of inflammation and treatments. Results and Discussion: Between sweat and saliva, Calprotectin and IL-6 demonstrated a strong linear relationship (R2>0.7), and strong to moderate linear relationships between Calprotectin and IL-6 in sweat and saliva, respectively. Bland-Altman analysis and Deming regression analysis determined a low incidence of outliers and similar ranges of Calprotectin and IL-6 expression between sweat and saliva. Diurnal analysis exhibited significantly higher sweat Calprotectin and higher median Calprotectin measured in the morning-afternoon than measured in the evening among chronically inflamed and healthy subjects, respectively. Among sweat IL-6 measured in the morning-afternoon and evening, chronically inflamed individuals expressed higher median sweat IL-6 in the morning-afternoon, while healthy individuals expressed higher median IL-6 in the evening. Temporal results of 40-hour tracking of sweat Calprotectin demonstrated higher basal Calprotectin in a medicated over unmedicated inflamed subject, and higher basal Calprotectin of inflamed subjects over a healthy subject. Conclusion: Calprotectin and IL-6 demonstrated strong positive linear relationships between saliva and sweat expression of each marker and strong to moderate linear relationships between the markers in sweat and saliva, along with a low incidence of outliers and a similar range of expression between sweat and saliva. Diurnal patterns were characterized for chronically inflamed and healthy individuals for Calprotectin and IL-6 in sweat, and continuous tracking of inflammation in sweat was demonstrated with the wearable sensor device. Figure 1

An ingestible, battery-free, tissue-adhering robotic interface for non-invasive and chronic electrostimulation of the gut

Ingestible electronics have the capacity to transform our ability to effectively diagnose and potentially treat a broad set of conditions. Current applications could be significantly enhanced by addressing poor electrode-tissue contact, lack of navigation, short dwell time, and limited battery life. Here we report the development of an ingestible, battery-free, and tissue-adhering robotic interface (IngRI) for non-invasive and chronic electrostimulation of the gut, which addresses challenges associated with contact, navigation, retention, and powering (C-N-R-P) faced by existing ingestibles. We show that near-field inductive coupling operating near 13.56 MHz was sufficient to power and modulate the IngRI to deliver therapeutically relevant electrostimulation, which can be further enhanced by a bio-inspired, hydrogel-enabled adhesive interface. In swine models, we demonstrated the electrical interaction of IngRI with the gastric mucosa by recording conductive signaling from the subcutaneous space. We further observed changes in plasma ghrelin levels, the “hunger hormone,” while IngRI was activated in vivo, demonstrating its clinical potential in regulating appetite and treating other endocrine conditions. The results of this study suggest that concepts inspired by soft and wireless skin-interfacing electronic devices can be applied to ingestible electronics with potential clinical applications for evaluating and treating gastrointestinal conditions.

Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios.

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC-α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

Exposure to Radiofrequency Induces Synaptic Dysfunction in Cortical Neurons Causing Learning and Memory Alteration in Early Postnatal Mice.

The widespread use of wireless communication devices has necessitated unavoidable exposure to radiofrequency electromagnetic fields (RF-EMF). In particular, increasing RF-EMF exposure among children is primarily driven by mobile phone use. Therefore, this study investigated the effects of 1850 MHz RF-EMF exposure at a specific absorption rate of 4.0 W/kg on cortical neurons in mice at postnatal day 28. The results indicated a significant reduction in the number of mushroom-shaped dendritic spines in the prefrontal cortex after daily exposure for 4 weeks. Additionally, prolonged RF-EMF exposure over 9 days led to a gradual decrease in postsynaptic density 95 puncta and inhibited neurite outgrowth in developing cortical neurons. Moreover, the expression levels of genes associated with synapse formation, such as synaptic cell adhesion molecules and cyclin-dependent kinase 5, were reduced in the cerebral cortexes of RF-EMF-exposed mice. Behavioral assessments using the Morris water maze revealed altered spatial learning and memory after the 4-week exposure period. These findings underscore the potential of RF-EMF exposure during childhood to disrupt synaptic function in the cerebral cortex, thereby affecting the developmental stages of the nervous system and potentially influencing later cognitive function.

Principles of Interoperability of Diabetes Devices.

Better interoperability is essential to derive maximum benefit from connected wireless diabetes devices. The need for this feature of diabetes devices is supported by trends in designing better performance for three types of devices: nonmedical devices, medical devices, and diabetes devices. First, interoperability is a standard attribute for the performance of nonmedical devices contained in smart systems that can sense and actuate. Second, interoperability is now mandated by the US Department of Health and Human Services as carried out by the Office of the National Coordinator for Health Information technology (ONC) and the US Food and Drug Administration (FDA) to improve the performance of all medical devices. Third, new guidance from the FDA and nongovernmental professional organizations are intended to promote interoperability because this feature will improve the performance of all diabetes devices. Wireless devices perform best when they are interoperable, and this is particularly true for diabetes devices.

Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring

Swallowing is a complex neuromuscular activity regulated by the autonomic central nervous system, and impairment can lead to dysphagia, which is difficulty in swallowing. This research presents a novel approach that utilizes wireless, wearable technology for the continuous mechano-acoustic tracking of respiratory activities and swallowing. To address the challenge of accurately tracking swallowing amidst potential confounding activities or significant body movements, we employ two accelerometers. These accelerometers help distinguish between genuine swallowing events and other activities. By monitoring movements and vibrations through the skin surface, the developed device enables non-intrusive monitoring of swallowing dynamics and respiratory patterns. Our focus is on the development of both the wireless skin-interfaced device and an advanced algorithm capable of detecting swallowing dynamics in conjunction with respiratory phases. The device and algorithm demonstrate robustness in detecting respiratory patterns and swallowing instances, even in scenarios where users exhibit periodic movements due to disease or daily activities. Furthermore, peak detection using an adaptive threshold automatically adjusts to an individual’s signal strength, facilitating the detection of swallowing signals without the need for individual adjustments. This innovation has significant potential for enhancing patient training and rehabilitation programs aimed at addressing dysphagia and related respiratory issues.

Supercapacitor-Assisted Energy Harvesting Systems

Energy harvesting from energy sources is a rapidly developing cost-effective and sustainable technique for powering low-energy consumption devices such as wireless sensor networks, RFID, IoT devices, and wearable electronics. Although these devices consume very low average power, they require peak power bursts during the collection and transmission of data. These requirements are satisfied by the use of energy-storage devices such as batteries or supercapacitors (SCs). Batteries offer significantly higher energy density but are subject to regular replacement, thermal runaway risk, and environmental concerns. On the other hand, SCs provide over a million-fold increase in capacitance compared to a traditional capacitor of the same volume. They are considered as the energy-storing devices that bridge the gap between conventional capacitors and batteries. They also offer fast charging times, a long lifecycle, and low equivalent series resistance (ESR). Most importantly, they are capable of handling the high transient currents produced by energy harvesters and provide a stable power source for external loads. This study encompasses a brief exploration of the three fundamental SC types. Then, the discussion delves into the integration of SCs into energy harvesting applications. The collective knowledge presented aims to guide future research endeavors fostering the development of novel energy harvesting systems using SCs.

Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators.

Prognostic value of electrical bioimpedance measured with a portable and wireless device in acute heart failure

Introduction and objectivesThe current evaluation of acute heart failure (HF) does not allow an adequate prediction of its evolution. The electrical bioimpedance (BI) allows knowing the state of blood volume, until now only with fixed equipment. We have developed and validated a portable and wireless device to measure BI at the ankle (IVOL). The objective of the study is to know the long-term prognostic value of the point measurement of BI with IVOL in patients with acute HF. MethodsA prospective cohort study of unselected patients admitted for acute HF in a tertiary hospital. The association between BI and different clinical, analytical and echocardiographic variables on admission and clinical evolution were analyzed. Results76 patients were included (mean age 66.1 years, 71.1% men, 68.4% hypertensive, 34.2% diabetic, mean NT-ProBNP: 7,103 pg/ml). Of these, 52.6% with non-preserved left ventricular ejection fraction (LVEF) (<50%) and 56.6% with right ventricular (RV) dysfunction. 26.3% died during a mean follow-up of 35.8 months. Survival in patients with BI ≤ 21,8 Ω was lower, globally and in the subgroups of patients without preserved LVEF and with RV dysfunction, p < 0.008). In the multivariate analysis, a BI ≥ 21.8 Ω was an independent survival factor (HR 0.242, 95% CI:0.86−0.681, p = 0.007). ConclusionsBI values ​​measured with IVOL may be an independent predictor of long-term mortality in patients hospitalized for acute HF. This prognostic value is maintained in patients without preserved LVEF function and with RV dysfunction.

Analyzing Human-Centric Wireless Energy Harvesting for Sustainable and Resilient Energy Systems

Discussions around wireless communications and energy harvesting in the literature are elitist and techno-centric. There is a need for more studies on a human-centric approach that simplifies the concept and links energy harvesting with human empowerment and well-being. Also, studies so far on the implementation of wireless energy harvesting in rural settlements have been scanty despite the important role wireless communication plays in social interactions and financial transactions within rural communities. The challenge of little awareness of the human sustainability impact of WEH is impeding stakeholders’ advocacy and advancement of wireless energy harvesting (WEH) despite its real and perceived benefits. So far, research efforts as reported in the literature are less than proportionate to the potential of WEH. Apart from providing sustainable energy for wireless devices and networks, its generation of nonelectric renewable energy is a huge plus for ensuring access to affordable, reliable, sustainable, and modern energy for all as contained in the United Nations Sustainable Development Goal 7 (SDG 7). Given the astronomical growth in the use of wireless devices globally, more research studies are needed to create more awareness for massive WEH advocacy. This study uses a model-driven approach to simplify and highlight critical aspects of WEH such as wireless body area network, sources of non-electric renewable energy sources, green energy (environmental protection), intermittent nature of renewable wireless energy sources, and research directions. Also, the application of WEH for the sustenance of lives and livelihood in rural economies is examined using the rural community of Adum-Aiona in Nigeria as a case study. It is expected that the information provided will create more awareness and foster further discussions around the human-centric angle of WEH even as advances in technology continue to be made.

A Practical Guide to Wireless Breast Localization Devices

A Practical Guide to Wireless Breast Localization Devices

A Practical Guide to Wireless Breast Localization Devices

A Practical Guide to Wireless Breast Localization Devices

Self-Powered Wireless Devices and Machine Learning Enable Local Humidity Monitoring

Self-Powered Wireless Devices and Machine Learning Enable Local Humidity Monitoring

Utilizing Dry Electrode Electroencephalography and AI Robotics for Cognitive Stress Monitoring in Video Gaming

This research explores the integration of the Dry Sensor Interface-24 (DSI-24) EEG headset with a ChatGPT-enabled Furhat robot to monitor cognitive stress in video gaming environments. The DSI-24, a cutting-edge, wireless EEG device, is adept at rapidly capturing brainwave activity, making it particularly suitable for dynamic settings such as gaming. Our study leverages this technology to detect cognitive stress indicators in players by analyzing EEG data. The collected data are then interfaced with a ChatGPT-powered Furhat robot, which performs dual roles: guiding players through the data collection process and prompting breaks when elevated stress levels are detected. The core of our methodology is the real-time processing of EEG signals to determine players’ focus levels, using a mental focusing feature extracted from the EEG data. The work presented here discusses how technology, data analysis methods and their combined effects can improve player satisfaction and enhance gaming experiences. It also explores the obstacles and future possibilities of using EEG for monitoring video gaming environments.

Metaheuristic optimization‐based clustering with routing protocol in wireless sensor networks

SummaryIn recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches.