Search Results (50)

Background/Objectives: Migraine and diabetes mellitus are highly prevalent chronic diseases, and their comorbidity presents management challenges, particularly when wearable medical technologies are used concurrently. Remote electrical neuromodulation (REN; Nerivio^®) is an FDA-cleared non-pharmacological migraine therapy, and continuous glucose monitoring (CGM) systems are widely used in diabetes care. However, the safety and compatibility of simultaneous co-use have not yet been evaluated. This technical compatibility study aimed to assess whether REN operation affects CGM performance or interferes with glucose measurement integrity in diabetic adults. Methods: Twenty-one adults with diabetes using Dexcom G6/G7 or FreeStyle Libre 2/3 participated in a single-arm interventional study. During a 45 min session, participants operated the REN and CGM devices simultaneously on their smartphones, and the REN device was paused three times to compare CGM readings between REN ON and RED OFF conditions. The primary outcome was the mean absolute relative difference (MARD_{REN ON/OFF}), evaluated against a prespecified 5% threshold. Statistical analysis included the Wilcoxon test, with subgroup analysis by the CGM device family. Results: The median MARD_{REN ON/OFF} across all participants was 1.61% (IQR 0.84–2.44%), significantly below the 5% threshold (p < 0.001). All participants achieved MARD_{REN ON/OFF} < 5%. Subgroup analyses were consistent: the median MARD_{REN ON/OFF} was 1.70% (IQR 0.90–2.45%) for Dexcom and 1.05% (IQR 0.83–1.50%) for Abbott. No technical interference, Bluetooth disruptions, missed data transmission, or adverse events were observed. Conclusions: Simultaneous use of Nerivio^® REN and CGM systems in adults with diabetes is compatible and safe, with no evidence of interference or significant deviations in glucose readings. These findings support the integrated and reliable use of REN and CGM wearables in adults with diabetes managing comorbid conditions. Full article

In the last few decades, several wearable devices have been designed to monitor respiration rate to capture pulmonary signals with a higher accuracy and reduce patients’ discomfort during use. In this article, we present the design and implementation of a device for the real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force-Sensing Resistor (FSR) attached to a Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to its adequate throughput, high accessibility, and the possibility of power consumption reduction. In addition to the sensing mechanism, the device includes a compact, energy-efficient micro-controller and a three-axis accelerometer that captures body movement. Power is supplied by a rechargeable Lithium-ion Polymer (LiPo) battery, and energy usage is optimized using a buck converter. For comfort and usability, the enclosure was 3D printed using Stereolithography (SLA) technology to ensure a smooth, ergonomic shape. This setup allows the device to operate reliably over long periods without disturbing the user. Altogether, the design supports continuous respiratory tracking in both clinical and home settings, offering a practical, low-power, and portable solution. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

With the exponential growth of Internet of Things (IoT) and wearable devices for home automation and industrial applications, vast volumes of data are continuously generated, requiring efficient data collection methods. IoT devices, being resource-constrained and typically battery-dependent, require lightweight protocols that optimize resource usage and energy consumption. Among such IoT devices, this article focuses on Bluetooth-based beacons due to their low latency and the advantage of not requiring pairing for communications. Specifically, to tackle the limitations of beacons in terms of bandwidth and transmission frequency, this article proposes a protocol that modifies beacon frames to include up to three parameters per frame and that allows for making use of configurable beaconing intervals based on the specific requirements of the communications scenario. Moreover, the use of the proposed protocol leads to increased data rates for beaconing transmissions, providing a low latency and a flexible configuration that permits adjusting different parameters. The proposed solution enables end-to-end interoperability in Opportunistic Edge Computing (OEC) networks by integrating a lightweight bridge module to transparently manage BLE advertisement segments. To demonstrate the performance of the devised opportunistic protocol, it is evaluated across multiple scenarios (i.e., in a short-distance reference scenario, inside a home with diverse obstacles, inside a building, outdoors and in an industrial scenario), showing its flexibility and ability to collect substantial data volumes from heterogeneous IoT devices. Full article

Breast cancer is a leading cause of cancer-related mortality worldwide, requiring efficient diagnostic tools for early detection and monitoring. Human epidermal growth factor receptor 2 (HER2) is a key biomarker for breast cancer classification, typically assessed using immunohistochemistry (IHC). However, IHC requires invasive biopsies and time-intensive laboratory procedures. In this study, we present a biosensor integrated with a reusable printed circuit board (PCB) and functionalized glucose test strips designed for rapid and non-invasive HER2 detection in saliva. The biosensor achieved a limit of detection of 10⁻¹⁵ g/mL, 4 to 5 orders of magnitude more sensitive than the enzyme-linked immunosorbent assay (ELISA), with a sensitivity of 95/dec and a response time of 1 s. In addition to HER2, the biosensor also detects cancer antigen 15-3 (CA15-3), another clinically relevant breast cancer biomarker. The CA15-3 test demonstrated an equally low limit of detection, 10⁻¹⁵ g/mL, and a higher sensitivity, 190/dec, further validated using human saliva samples. Clinical validation using 29 saliva samples confirmed our biosensor’s ability to distinguish between healthy, in situ breast cancer, and invasive breast cancer patients. The system, which integrates a Bluetooth Low-Energy (BLE) module, enables remote monitoring, reduces hospital visits, and enhances accessibility for point-of-care and mobile screening applications. This ultra-sensitive, rapid, and portable biosensor can serve as a promising alternative for breast cancer detection and monitoring, particularly in rural and underserved communities. Full article

With the rapid development of Wi-Fi 6/6E and dual-band wireless systems, there is an increasing demand for compact antennas with balanced high-gain performance across both 2.4 GHz and 5 GHz bands. However, most existing dual-band metasurface antennas face challenges in uneven gain distribution between lower/higher-frequency bands and structural miniaturization. This paper proposes a high-gain dual-band metasurface antenna based on an artificial magnetic conductor (AMC) array, which has a significant advantage in miniaturization and improving antenna performance. Two types of dual-band AMC structures are applied to design the metasurface antenna. The optimal antenna with dual-slot AMC array operates in the 2.42–2.48 GHz and 5.16–5.53 GHz frequency bands, with a 25% size reduction compared to the reference dual-band U-slot antenna. Meanwhile, high gains of 7.65 dBi and 8 dBi are achieved at 2.4 GHz and 5 GHz frequency bands, respectively. Experimental results verify stable radiation gains across the operation bands, where the total efficiency remains above 90%, agreeing well with the simulation results. This research provides an effective strategy for high-gain and dual-band metasurface antennas, offering a promising solution for integrated modern wireless systems such as Wi-Fi 6, Bluetooth, and MIMO technology. Full article

Hearing difficulty in noise can occur in 10–15% of listeners with typical hearing in the general population of the United States. Using one’s smartphone as a remote microphone (RM) system with the AirPods Pro (AP) may be considered an assistive device given its wide availability and potentially lower price. To evaluate this possibility, the accuracy of voice-to-text transcription for sentences presented in noise was compared, when KEMAR wore an AP receiver connected to an iPhone set to function as an RM system, to the accuracy obtained when it wore a sophisticated Phonak Roger RM system. A ten-sentence list was presented for six technology arrangements at three signal-to-noise ratios (SNRs; +5, 0, and −5 dB) in two types of noise (speech-shaped and babble noise). Each sentence was transcribed by Otter AI to obtain an overall percent accuracy for each condition. At the most challenging SNR (−5 dB SNR) across both noise types, the Roger system and smartphone/AP set to noise cancelation mode showed significantly higher accuracy relative to the condition when the smartphone/AP was in transparency mode. However, the major limitation of Bluetooth signal delay when using the AP/smartphone system would require further investigation in real-world settings with human users. Full article

Work has rarely been reported on a highly portable smartphone-based capillary electrophoresis (CE) with capacitively coupled contactless conductivity detection (C⁴D). Herein, a highly portable phone-based CE (130 mm × 190 × 70 mm, 1.4 kg) with C⁴D and Bluetooth communication, as well as user-interface software, was developed for portable analysis. To demonstrate the device, six metal ions were selected as model analytes for verification and successfully applied to the detection of ions in tap water. The analytical performance highlighted that the runs and data analysis of the CE-C⁴D device could be controlled via the user interface based on smartphones. Furthermore, the experiments showed that (i) the linear ranges of six metal ions were between 6 and 1500 μmol/L with a correlation coefficient of more than 0.9934; (ii) the limit of detection (LOD) values were within 1.84–4.33 μmol/L; (iii) the intra-day deviations of migration time and peak area were 2.40–5.24% and 0.75–2.82% (n = 5), respectively. Although the LOD is not the most optimal among current portable devices, the results still indicated the satisfactory analytical performance and potential of the developed device, software, and method for portable separation and quantitation of analytes from various fields. Full article

The increasing proliferation of Internet of Things (IoT) devices has created a growing need for more efficient communication networks, especially in areas where continuous connectivity is unstable or unavailable. Opportunistic networks have emerged as a possible solution in such scenarios, allowing for intermittent and decentralized data sharing. This article presents a novel communication protocol that uses Bluetooth 5 and the libp2p framework to enable decentralized and opportunistic communications among IoT devices. The protocol provides dynamic peer discovery and decentralized management, resulting in a more flexible and robust IoT network infrastructure. The performance of the proposed architecture was evaluated through experiments in both controlled and industrial scenarios, with a particular emphasis on latency and on the impact of the presence of obstacles. The obtained results show that the protocol has the ability to improve data transfer in environments with limited connectivity, making it adequate for both urban and rural areas, as well as for challenging environments such as shipyards. Moreover, the presented findings conclude that the protocol works well in situations with minimal signal obstruction and short distances, like homes, where average latency values of about 8 s have been achieved with no losses. Furthermore, the protocol can also be used in industrial scenarios, even when metal obstacles increase signal attenuation, and over long distances, where average latency values of about 8.5 s have been obtained together with packet losses of less than 5%. Full article

Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, and lead to significant economic losses. We recently developed and published an embedded system prototype for detecting gunshots in an indoor environment. The proposed device can be attached to the walls or ceilings of schools, offices, clubs, places of worship, etc., similar to smoke detectors or night lights, and they can notify the first responders as soon as a gunshot is fired. The proposed system will help to stop the shooter early and the injured people can be taken to the hospital quickly, thus more lives can be saved. In this project, a new custom dataset of blank gunshot sounds is recorded, and a deep learning model using both time and frequency domain features is trained to classify gunshot and non-gunshot sounds with 99% accuracy. The previously developed system suffered from several security and privacy vulnerabilities. In this research, those vulnerabilities are addressed by implementing secure Message Queuing Telemetry Transport (MQTT) communication protocols for IoT systems, better authentication methods, Wi-Fi provisioning without Bluetooth, and over-the-air (OTA) firmware update features. The prototype is implemented in a Raspberry Pi Zero 2W embedded system platform and successfully tested with blank gunshots and possible false alarms. Full article

This study introduces a fall detection system utilizing an affordable consumer smartwatch and smartphone with edge computing capabilities for implementing AI algorithms. Due to the widespread use of these devices, the system as a whole is extremely accepted, easy to use, requires no tuning of any kind, and guarantees extended functioning for a long period. From a technical standpoint, falls are identified using AI techniques to analyze 3D raw data acquired by the smartwatch’s built-in accelerometer. However, existing AI models for fall detection are often trained on simulated falls involving young people, which may not accurately represent the falls of elderly in unhealthy conditions, such as arthritis or Parkinson’s disease, leading to limitations in detecting falls in this population. Additionally, variations in hardware features among different smartwatches can result in inconsistencies in accelerometer data measurements across X, Y, and Z orientations, further complicating accurate fall detection. To address the challenge of limited and device-specific datasets and to enhance model generalization across various devices, a Deep Transfer Learning approach is proposed. This method proves effective when data are poor. Specifically, the Continuous Wavelet Transform (CWT) is applied to raw accelerometer signals to convert them into 2D images, enabling the use of deep architectures for Transfer Learning. By employing CWT on 5 s time windowed raw accelerometer signals, heat maps (scalograms) are generated. Real-time accelerations sampled at 50 Hz are collected using a smartwatch application, transmitted via Bluetooth to a smartphone app, and converted into scalograms. These serve as input for pre-trained Deep Learning models to estimate fall probabilities. Preliminary tests on the Wrist Early Daily Activity and Fall Dataset (WEDA-FALL) show promising results with an accuracy of approximately 98%, underscoring the efficacy of utilizing wrist-worn wearable devices for processing raw accelerometer data. Full article

This paper presents a novel waste-heat-powered, wireless, and battery-less Industrial Internet of Things (IIoT) device designed for predictive maintenance in Industry 4.0 environments. With a focus on real-time quality data, this device addresses the limitations of current battery-operated IIoT devices, such as energy consumption, transmission range, data rate, and constant quality of service. It is specifically developed for heat-intensive industries (e.g., iron and steel, cement, petrochemical, etc.), where self-heating nodes, low-power processing platforms, and industrial sensors align with the stringent requirements of industrial monitoring. The presented IIoT device uses thermoelectric generators based on the Seebeck effect to harness waste heat from any hot surface, such as pipes or chimneys, ensuring continuous power without the need for batteries. The energy that is recovered can be used to power devices using mid-range wireless protocols like Bluetooth 5.0, minimizing the need for extensive in-house wireless infrastructure and incorporating light-edge computing. Consequently, up to 98% of cloud computation efforts and associated greenhouse gas emissions are reduced as data is processed within the IoT device. From the environmental perspective, the deployment of such self-powered IIoT devices contributes to reducing the carbon footprint in energy-demanding industries, aiding their digitalization transition towards the industry 5.0 paradigm. This paper presents the results of the most challenging energy harvesting technologies based on an all-silicon micro thermoelectric generator with planar architecture. The effectiveness and self-powering ability of the selected model, coupled with an ultra-low-power processing platform and Bluetooth 5 connectivity, are validated in an equivalent industrial environment to monitor vibrations in an electric machine. This approach aligns with the EU’s strategic objective of achieving net zero manufacturing capacity for renewable energy technologies, enhancing its position as a global leader in renewable energy technology (RET). Full article

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes. Full article

This paper presents a novel frequency reconfigurable antenna design for sub-6 GHz applications, featuring a unique combination of antenna elements and control mechanisms. The antenna is composed of an outer split-ring resonator loaded with an inner spiral resonator, which can be adjusted through the remote control of PIN diode or Single Pole Double Throw (SPDT) switches. The compact antenna, measuring 22 × 16 × 1.6 mm³, operates in broadband, or tri-band mode depending on the ON/OFF states of switches. The frequency reconfigurability is achieved using two BAR64−02V PIN diodes or two CG2415M6 SPDT switches acting as RF switches. SPDT switches are controlled remotely via Arduino unit. Additionally, the antenna demonstrates an omni-directional radiation pattern, making it suitable for wireless communication systems. Experimental results on an FR-4 substrate validate the numerical calculations, confirming the antenna’s performance and superiority over existing alternatives in terms of compactness, wide operating frequency range, and cost-effectiveness. The proposed design holds significant potential for applications in Wi-Fi (IEEE 802.11 a/n/ac), Bluetooth (5 GHz), ISM (5 GHz), 3G (UMTS), 4G (LTE), wireless backhaul (4G and 5G networks), WLAN (IEEE 802.11 a/n/ac/ax), 5G NR n1 band, and Wi-Fi access points due to its small size and easy control mechanism. The antenna can be integrated into various devices, including access points, gateways, smartphones, and IoT kits. This novel frequency reconfigurable antenna design presents a valuable contribution to the field, paving the way for further advancements in wireless communication systems. Full article

Currently, people spend many more hours on smartphones, and the potential impact of phone radiation is receiving more attention. Reducing the impact of a phone’s magnetic field on human health is vital. Although many studies advise changing phone use habits, such as reducing call times to impede phone radiation, there are no specific products found in the literature to prevent or reduce phone radiation. Therefore, this study used TRIZ theory as a research method to design prototypes in order to reduce the potential impacts of magnetic fields generated by smartphones on the human body. The results show that the distance between the human body and the phone is negatively related to phone radiation; the longer the distance is, the less phone radiation there is. Three prototypes have been designed through this research in order to reduce a phone’s radiation. The first testing condition simulates a phone conversation in which the prototypes are installed on the phone while having a WhatsApp conversation. The second testing condition simulates phone standby mode in which the prototypes are installed on a phone while the phone’s Wi-Fi, Bluetooth, data, GPS, and hotpot are on. A magnetic field tester was used to measure the magnetic fields every 5 s, and each measurement set lasted for 3 min. Five sets of measurements were completed at the end, and the average result shows that the use of prototypes under these two conditions can reduce 100% and 90% of the magnetic field generated by a smartphone during a phone conversation and the phone’s standby mode, respectively. Full article