Bluetooth Low Energy Research Articles

A Dual-Branch Convolutional Neural Network-Based Bluetooth Low Energy Indoor Positioning Algorithm by Fusing Received Signal Strength with Angle of Arrival

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology.

Wearable bracelet and machine learning for remote diagnosis and pandemic infection detection

The COVID-19 pandemic has highlighted that effective early infection detection methods are essential, as they play a critical role in controlling the epidemic spread. In this work, we investigate the use of wearable sensors in conjunction with machine learning (ML) techniques for pandemic infection detection. We work on designing a wristband that measures various vital parameters such as temperature, heart rate, and SPO2, and transmits them to a mobile application using Bluetooth Low Energy. The accuracy of the wristband measurements is shown to be within 10% of the readings of existing commercial products. The measured data can be used and analyzed for various purposes. To benefit from the existing online datasets related to COVID-19, we use this pandemic as an example in our work. Hence, we also develop ML-based models that use the measured vital parameters along with cough sounds in order to determine whether a case is COVID-19 positive or not. The proposed models are shown to achieve remarkable results, exceeding 90% accuracy. One of our proposed models exceeds 96% performance in terms of accuracy, precision, recall, and F1-Score. The system lends itself reasonably for amendment to deal with future pandemics by considering their specific features and designing the ML models accordingly. Furthermore, we design and develop a mobile application that shows the data collected from the wristband, records cough sounds, runs the ML model, and provides feedback to the user about their health status in a user-friendly, intuitive manner. A successful deployment of such an approach would decrease the load on hospitals and prevent infection from overcrowded spaces inside the hospital.

An ADPLL-Based GFSK Modulator with Two-Point Modulation for IoT Applications.

To establish ubiquitous and energy-efficient wireless sensor networks (WSNs), short-range Internet of Things (IoT) devices require Bluetooth low energy (BLE) technology, which functions at 2.4 GHz. This study presents a novel approach as follows: a fully integrated all-digital phase-locked loop (ADPLL)-based Gaussian frequency shift keying (GFSK) modulator incorporating two-point modulation (TPM). The modulator aims to enhance the efficiency of BLE communication in these networks. The design includes a time-to-digital converter (TDC) with the following three key features to improve linearity and time resolution: fast settling time, low dropout regulators (LDOs) that adapt to process, voltage, and temperature (PVT) variations, and interpolation assisted by an analog-to-digital converter (ADC). It features a digital controlled oscillator (DCO) with two key enhancements as follows: ΔΣ modulator dithering and hierarchical capacitive banks, which expand the frequency tuning range and improve linearity, and an integrated, fast-converging least-mean-square (LMS) algorithm for DCO gain calibration, which ensures compliance with BLE 5.0 stable modulation index (SMI) requirements. Implemented in a 28 nm CMOS process, occupying an active area of 0.33 mm2, the modulator demonstrates a wide frequency tuning range of from 2.21 to 2.58 GHz, in-band phase noise of -102.1 dBc/Hz, and FSK error of 1.42% while consuming 1.6 mW.

An electrochemical point-of-care testing device for specific diagnosis of the albinism biomarker based on paradigm shift designs

L-tyrosine is a recognized biomarker of albinism, whose endogenous level in human bodies is directly linked to melanin synthesis while no attention has been paid to its specific diagnosis. To this end, we have developed an electrochemical point-of-care testing device based on a molecularly imprinted gel prepared by a universal paradigm shift design to achieve the enhanced specific recognition of the L-tyrosine. Interestingly, this theoretically optimized molecularly imprinted gel validates the recognition pattern of L-tyrosine and optimizes the structure of the polymer itself with the aid of computational chemistry. Besides, modified extended-layer MXene and Au nanoclusters have significantly improved the sensing activity. As a result, the linear diagnostic range of this electrochemical point-of-care testing device for L-tyrosine is 0.1–100 μM in actual human fluids, which fully covers the L-tyrosine levels of healthy individuals and people with albinism. The diagnosis is completed in 90 s and then the results are transmitted by Bluetooth low energy to the smart mobile terminal. Therefore, we are convinced that this electrochemical point-of-care testing device is a promising tool in the future smart medical system.

(Invited) Wearable Flexible Microfluidic Biosensor for Sweat Monitoring

As we enter the post-pandemic era, our lives have certainly become more convenient than before, due to the advancement and elimination of technologies that became practical or widespread during the pandemic. Among these, technologies related to non-invasive bioinstrumentation, which can be used at an individual level to access one's own health information, have become increasingly important. Non-invasive bioinstrumentation can be broadly divided into the measurement of physical information (shape, activity and so on) and the measurement of chemical information, i.e. the components of the body. For example, facial shape is widely used for personal identification, while weight and energy expenditure related to activity are easily managed by smartphones. In contrast, chemical sensing, which is often difficult to sample and requires a wet environment for measurement, still requires breakthroughs to be used by individuals with the same ease as physical sensing.In the research field of non-invasive biomedical measurements, the technology is often developed in the form of wearable systems. Wearable microfluidic devices have been intensively studied for the measurement of body components. We focused on sweat, which contains a variety of biomarkers, and have developed a wearable microfluidic biosensing system that measures sweat components in real-time. Sweat is secreted from sweat glands on the skin of the entire body, making it easily accessible and less mentally demanding to collect. On the other hand, there are problems with sampling methods for reliable measurement, such as large fluctuations in the sweat rate and evaporation from the skin surface during secretion. We overcame this problem by using a wearable microfluidic device to transport the full amount of sweat from the sweat gland to a downstream biosensor by carrier flow, and developed a wristwatch biosensing system to monitor the constituents released as sweat independent of sweat rate.The wristwatch sweat sensor consists of a PDMS sweat sampling device, a biosensor, a fluid controller and a wireless control circuit with Bluetooth Low Energy and external analogue I/O. Communication with the mobile device is via Bluetooth Low Energy. Our method has the advantage that secretions are transported by dissolution in the carrier stream and can therefore be reliably monitored even at very low sweat levels. The pump is controlled by an microcontroller unit, so the flow rate can be controlled in real time according to the sweat rate and target substances, and the sensitivity can be improved at the expense of temporal resolution by running the pump intermittently, i.e. allowing secretions to accumulate in the carrier flow for a certain time before measurement.We monitored changes in sweat lactic acid in response to exercise load using an ergometer. The results showed an increase in sweat lactate with increasing exercise intensity, with a dependence on sweat rate. By measuring sweat at rest, we found that the lactate accumulated in the sweat glands and on the skin's surface were dissolved by the biosensor in the early stages of measurement, while the signals from the sweat glands became dominant after these had been sufficiently decreased. These results suggest that our system appropriately reflects the physiological phenomena associated with sweating. The presentation will also include the behaviour of lactic acid in sweat, which was detected by our system for the first time.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

Integration of LoRa-enabled IoT infrastructure for advanced campus safety systems in Taiwan

Amid rising concerns about campus safety in Taiwan, particularly with the global trend towards smart cities, integrating the Internet of Things (IoT) into institutional security frameworks has become pivotal. The paper discusses the implementation of using iBeacons and Long Range (LoRa) technology to locating the student position and ensure his safety in the school campus. It uses Internet of Things (IoT) approach in real time to monitor and locate the student presence in the school compound. This paper unveils an innovative design for a campus security system that harnesses the LoRa technology. In the system, the students are equipped with devices containing Bluetooth Low Energy (BLE) beacons to capture and transmit real-time location data. The system response time to locating student in abnormal locations such as cornered and concealed areas is about one second. By extending this system to cover all individuals on campus, a closely monitored environment and areas is enabled that significantly bolstering the security measures. This not only furnishes a dynamic protective layer for educational institutions but also serves as a proactive deterrent against potential security breaches. Ultimately, this research underscores the transformative potential of merging IoT with campus security to ushering in a new era of student safety. LoRa technology offers advantages in battery life, cost-effectiveness, deployment flexibility, and network coverage etc. Therefore, this paper ultimately provides a method of how to utilize the LoRa technology to develop a campus security system. Unlike artificial intelligence (AI)-based image recognition, which raises concerns about privacy and human rights; the features of LoRa’s long-range communication and low power consumption make it a more suitable choice.

Development of a novel Bluetooth Low Energy device for proximity and location monitoring in grazing sheep

Monitoring animal location and proximity can provide useful information on behaviour and activity, which can act as a health and welfare indicator. However, tools such as global navigation satellite systems (GNSS) can be costly, power−hungry and often heavy, thus not viable for commercial uptake in small ruminant systems. Developments in Bluetooth Low Energy (BLE) could offer another option for animal monitoring, however, BLE signal strength can be variable, and further information is needed to understand the relationship between signal strength and distance in an outdoor environment and assess factors which might affect its interpretation in on-animal scenarios. A calibration of a purpose-built device containing a BLE reader, alongside commercial BLE beacons, was conducted in a field environment to explore how signal strength changed with distance and investigate whether this was affected by device height, and thus animal behaviour. From this calibration, distance prediction equations were developed whereby beacon distance from a reader could be estimated based on signal strength. BLE as a means of localisation was then trialled, firstly using a multilateration approach to locate 16 static beacons within an ∼5 400 m2 section of paddock using 6 BLE readers, followed by an on-sheep validation where two localisation approaches were trialled in the localisation of a weaned lamb within ∼1.4 ha of adjoining paddocks, surrounded by nine BLE readers. Validation was conducted using 1 days’ worth of data from a lamb fitted with both a BLE beacon and separate GNSS device. The calibration showed a decline in signal strength with increasing beacon distance from a reader, with a reduced range and earlier decline in the proportion of beacons reported at lower reader and beacon heights. The distance prediction equations indicated a mean underestimation of 12.13 m within the static study, and mean underestimation of 1.59 m within the on-sheep validation. In the static beacon localisation study, the multilateration method produced a mean localisation error of 22.02 m, whilst in the on-sheep validation, similar mean localisation errors were produced by both methods – 19.00 m using the midpoint and 23.77 m using the multilateration method. Our studies demonstrate the technical feasibility of localising sheep in an outdoor environment using BLE technology; however, potential commercial application of such a system would require improvements in BLE range and accuracy.

Design of New BLE GAP Roles for Vehicular Communications.

Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m.

Constrained Flooding Based on Time Series Prediction and Lightweight GBN in BLE Mesh.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.

A Proof of Concept Using BLE to Optimize Patients Turnaround Time (PTAT) in Health Care

In 2022, Malaysia's Ministry of Health (MOH) acknowledged that government hospitals continue to face challenges in delivering high-quality patient care. A significant issue is the absence of a Real-Time Patient Location Monitoring and Tracking Solution, which prevents doctors from efficiently tracking patients within medical facilities. This lack of real-time tracking contributes to a decline in service quality, creating an urgent need for optimization in patient monitoring and tracking solutions. Implementing an effective tracking system can significantly enhance patient care quality, improve resource utilization management, and boost staff productivity, ultimately saving costs, time, and resources for hospitals. One critical area for improvement is the Patient Turnaround Time (PTAT), which refers to the duration a patient spends being processed at a hospital before being discharged. PTAT is currently prolonged in many hospitals due to the complex processes involved and patients becoming untraceable while waiting for their turn. To address these challenges, we have developed an Internet of Things (IoT) solution utilizing Bluetooth Low Energy (BLE) technology to optimize PTAT. This solution enables hospital staff to track patient movements and localize them in real-time. By using wearable devices, the system measures the distance from patients to various Access Points (AP) spread throughout the hospital based on the dBm value. This paper presents comprehensive experimentation for the Proof of Concept (POC) and pilot testing of this BLE-based solution. The results demonstrate the accuracy of using the Received Signal Strength Indicator (RSSI) to optimize patient turnaround times in hospitals. By implementing this BLE solution, hospitals can reduce the time patients spend waiting for treatment, thereby improving overall patient satisfaction and hospital efficiency. The ability to monitor patient locations in real-time will ensure that patients receive timely care, reducing the likelihood of them wandering off and becoming untraceable. This technology represents a significant step forward in modernizing patient care and streamlining hospital operations in Malaysia.

A novel programmable gain control low noise amplifier (PGCLNA) design for automatic gain control loop in BLE applications

Bluetooth low energy (BLE) is a widely used short-range communication protocol and BLE receivers frequently encounter transient high-power RF (radio frequency) signals from antennas, leading to receiver saturation. To address this issue, this work proposes a 2.4 GHz programmable gain control low-noise amplifier (PGCLNA), which is a key element in an automatic gain control (AGC) system for BLE receiver applications. The proposed PGCLNA achieves discrete levels of gain using the g m -boosting technique by splitting the negative feedback. The proposed circuit is simulated in UMC 180 nm technology and the maximum gain achieved is 41.4 dB, with a step decrease of 15 dB in discrete levels. The gain variations do not degrade the performance of S11, which is always less than -16 dB. The noise figure (NF) is always minimum of 5.5 dB and in high gain mode, the minimum value of NF is 1.82 dB. The third-order intercept point (IIP3) is −7.621 dBm in the best circumstances and always remains above −10.69 dBm. These results are achieved by utilising 5.4 mW power at 1.8 V of supply voltage. The circuit occupies an area of 732 μm × 771 μm. The proposed PGCLNA design offers a solution to accommodate a wide range of input signals in BLE receivers, excellent gain control, low noise and robust performance characteristics, making it an essential component in an AGC system for BLE receiver applications.

Stretchable Thermoelectric Generators for Self‐Powered Wearable Health Monitoring

AbstractAs continuous wearable physiological monitoring systems become more ubiquitous in healthcare, there is an increasing need for power sources that can sustainably power wireless sensors and electronics for long durations. Wearable energy harvesting with thermoelectric generators (TEGs), in which body heat is converted to electrical energy, presents a promising way to prolong wireless operation and address battery life concerns. In this work, high performance TEGs are introduced that combine 3D printed elastomers with liquid metal epoxy polymer composites and thermoelectric semiconductors to achieve elastic compliance and mechanical compatibility with the body. The thermoelectric properties are characterized in both energy harvesting (Seebeck) and active heating/cooling (Peltier) modes, and examine the performance of wearable energy harvesting under various conditions such as sitting, walking, and running. When worn on a user's forearm while walking outside, the TEG arrays are able to power circuitry to collect photoplethysmography (PPG) waveform data with a photonic sensor and wirelessly transmit the data to an external PC using an on‐board Bluetooth Low Energy (BLE) radio. This represents a significant step forward on the path to sustainable body‐worn smart electronics.

Demonstrating the feasibility of monitoring bone fracture healing using electromechanical impedance spectroscopy – a comparison between three different inputs in Ward’s hierarchical clustering method

Current methods for monitoring the progression of bone fracture healing require large, complex, and expensive imaging technology and, if used repeatedly, can expose patients to harmful ionizing radiation. Recent development of miniaturized, implantable, inexpensive microelectronic sensing technology now makes it possible to continuously monitor fracture healing non-invasively at the patient's bedside. This proof-of-concept study demonstrates a novel, smart, implantable medical device based on electromechanical impedance spectroscopy. The setup consists mainly of a piezoceramic disk (PIC255 from PI Ceramics), an impedance chip (AD5933 from Analog Devices) and an inductive battery recharging system. The system operates autonomously for 24 hours of measurements. Every 20 minutes, the health state of the bone is measured. To test the setup, 20 tibia bones of sheep limbs obtained from a butcher's shop are used. By drilling five holes into the bone one after another, different defects mimicking bone fractures of different sizes are created. This simulates an inverse bone healing process. The collected data is transmitted via Bluetooth LE to a portable device that displays the impedance spectra. After the results have been reviewed, the data is transferred to a computer for statistical evaluation, such as the Root Mean Square Deviation (RMSD). Preliminary results have shown an increasing trend in RMSD with increasing number of holes in the bone. Based on these observations, an in-vivo study is planned.

Wearable Alcohol Monitoring Device for the Data-Driven Transcutaneous Alcohol Diffusion Model.

Wearable alcohol monitoring devices demand noninvasive, real-time measurement of blood alcohol content (BAC) reliably and continuously. A few commercial devices are available to determine BAC noninvasively by detecting transcutaneous diffused alcohol. However, they suffer from a lack of accuracy and reliability in the determination of BAC in real time due to the complex scenario of the human skin for transcutaneous alcohol diffusion and numerous factors (e.g., skin thickness, kinetics of alcohol, body weight, age, sex, metabolism rate, etc.). In this work, a transcutaneous alcohol diffusion model has been developed from real-time captured data from human wrists to better understand the kinetics of diffused alcohol from blood to different skin epidermis layers. Such a model will be a footprint to determine a base computational model in larger studies. Eight anonymous volunteers participated in this pilot study. A laboratory-built wearable blood alcohol content (BAC) monitoring device collected all the data to develop this diffusion model. The proton exchange membrane fuel cell (PEMFC) sensor was fabricated and integrated with an nRF51822 microcontroller, LMP91000 miniaturized potentiostat, 2.4 GHz transceiver supporting Bluetooth low energy (BLE), and all the necessary electronic components to build this wearable BAC monitoring device. The %BAC data in real time were collected using this device from these volunteers' wrists and stored in the end device (e.g., smartphone). From the captured data, we demonstrate how the volatile alcohol concentration on the skin varies over time by comparing the alcohol concentration in the initial stage (= 10 min) and later time (= 100 min). We also compare the experimental results with the outputs of three different input profiles: piecewise linear, exponential linear, and Hoerl, to optimize the developed diffusion model. Our results demonstrate that the exponential linear function best fits the experimental data compared to the piecewise linear and Hoerl functions. Moreover, we have studied the impact of skin epidermis thickness within ±20% and demonstrate that a 20% decrease in this thickness results in faster dynamics compared to thicker skin. The model clearly shows how the diffusion front changes within a skin epidermis layer with time. We further verified that 60 min was roughly the time to reach the maximum concentration, Cmax, in the stratum corneum from the transient analysis. Lastly, we found that a more significant time difference between BACmax and Cmax was due to greater alcohol consumption for a fixed absorption time.

Power Over Fiber and Analog Radio Over Fiber Simultaneous Transmission Over Long Distance in Single Mode, Multicore, and Hollow Core Fibers

AbstractIn this work, a novel study of the feasibility of shared Power over Fiber (PoF) and Fifth‐Generation (5G) New Radio (NR) Analog Radio over Fiber (ARoF) over long distance Hollow‐core Fibers (HCF) is presented. Comparative measurements between similar lengths of HCFs, Multicore (MCF) and Single Mode Fibers (SMF) are performed. A complete analysis on the Stimulated Raman Scattering (SRS)‐induced noise transfer from PoF to ARoF is tackled. Relative Intensity Noise (RIN) of ARoF signals is measured for all the optical fibers tested, observing no degradation in HCFs, neither on MCFs and significant deterioration in SMFs. A combination of PoF and 5G NR ARoF link in a single fiber is implemented, and 5G NR signals with different modulation formats are successfully transmitted through HCFs and MCFs without degradation, while SRS‐induced noise transfer in SMFs massively deteriorate them. PoF transmission efficiencies are also analyzed, achieving up to 9.9 % System Energy Efficiency (SEE) for a 3.1 km HCF, 0.9 % for a 11.1 km HCF and 1.5 %, 13.5 %, and 20.4 % in a 10 km MCF, a 9.8 km SMF and a 3.3 km SMF, respectively. The PoF signal is used to supply a Bluetooth Low‐Energy (BLE) load with a power consumption of 20 mW.

Using proximity loggers in studies of sheep behavior: an estimation of the daily frequency of water consumption

BackgroundProximity loggers are a biotelemetry technology used to quantify animal–animal interactions non-invasively. They are small devices can be affixed to a target animal and detects and records if another device is within a certain distance, which can provide unprecedented insights into individual behavior and interactions with other animals or other aspects of the environment. Proximity loggers also can record the duration and distance between the two or more individuals detected, and have been used to study a diverse array of interactions. Studying drinking behavior in sheep is important for several critical reasons, such as improving animal welfare, enhancing productivity, managing resources efficiently, and ensuring sustainable agricultural practices. The objective of this study was to test the capacity for Bluetooth Low Energy proximity loggers to quantify drinking habits in sheep (n = 7 adult females). At all times, they had access to water from two drinking troughs. Animals wore neck collars that contained a mobile logger, and two stationary loggers were placed under the drinking troughs. To store the records downloaded from the stationary loggers, a gateway was placed close to the troughs. The mobile loggers were programmed as beacons to transmit their ID to the stationary loggers, which were programmed as receivers to scan the proximity of the mobile loggers, record the time, the IDs of the mobile loggers, and the received signal strength of the contact with the mobile loggers.ResultsEwes visited the drinking troughs 2.4 ± 0.5 times per day (rank 1.4–2.9), most frequently (42%) between 0800 and 1000 h, and 84% occurred in the daytime. Mean length of each water intake was 1.72 ± 0.30 min. The cosinor analysis of the distribution of drinking events revealed a significant (P < 0.0001) 24-h circadian rhythm in drinking events, and the acrophase (time of the highest proportion of events) occurred at 1054 h.ConclusionsConsidering that studying water intake in sheep is crucial for optimizing their health, welfare, and management, the proximity loggers were demonstrated to be appropriate for documenting the drinking habits of sheep, and probably, would be suitable for studies of other behaviors that involve approaching other animals or objects.

Wearable device for personalized EMG feedback-based treatments

Curative effects of electromyography (EMG) feedback in treatment of various conditions and/or recovery after injuries have been earlier reported. However, wider application on EMG feedback is somehow limited due to the overall price of such systems and limited availability outside of the specialized treatment centers. Development of a personalized device for EMG feedback would be of great importance for home recovery after stroke or injuries, achieving better success in fitness and improving biofeedback-based treatments of conditions such as urinary incontinence. Despite extensive research on EMG signal collection, there is a lack of focus on in-situ EMG analysis that considers the intensity and duration of muscle activities. This gap presents the motivation for our research. In this paper, we present a methodology for the realization of wearable, rechargeable battery-powered, small-sized (90 mm × 60 mm) electronic device for recording two EMG channels (12-bits resolution, sampling frequency up to 1.6 kHz) with Bluetooth Low Energy connectivity to a smartphone. An average current consumption of 20.5 mA was experimentally determined, suggesting that multiday continuous functionality is possible. Advancing the state of the art, we propose a cross-correlation-based algorithm for in-situ dynamical computing and evaluation of muscle activation levels. This algorithm can determine if the muscle follows a predefined profile of contractions/relaxations (as needed for the treatment) and indicate if two muscles in a specific exercise were or were not engaged in proper time and with the proper intensity. The performed simulation analysis showed that the proposed approach exhibited shorter processing time compared to Morlet Wavelet Transform and Dynamic Time Warping. Finally, experimental work with five human volunteers demonstrated the reliability in EMG acquisition and signal processing. Therefore, the main contribution of this work is a cost-effective, small-sized, customizable EMG recording system with an efficient cross-correlation-based algorithm for in-situ EMG collection and processing.

A Novel Approach for Mobility-Aware Energy-Efficient Clustering-Based Routing Using EANN With GA Algorithm on WSNs

Aim: This research aims to explore and evaluate various strategies for improving energy efficiency within wireless sensor networks (WSNs). Specifically, the study focuses on the critical challenge of extending network lifespan through energy conservation by establishing balanced clusters within the WSN architecture. Background: In wireless sensor networks (WSNs), ensuring prolonged network operation while conserving energy resources is a significant concern. One promising approach to address this challenge is the implementation of equalized clusters, which requires an effective selection of cluster heads (CHs). However, this task presents considerable complexity and demands innovative solutions to overcome. Objective: The primary objective of this study is to develop and assess a novel methodology for selecting precise cluster heads (CHs) within WSNs. This methodology is based on the utilization of Bluetooth low energy (BLE) sensors deployed in a randomly distributed manner across the study area. By employing an enhanced artificial neural network and greedy approach (EANN-GA), the proposed technique seeks to identify CHs with optimal proximity to the cluster center and substantial remaining energy reserves. Methods: The proposed methodology involves the deployment of BLE sensors distributed randomly throughout the study region, which are then organized into clusters. Using the enhanced artificial neural network and greedy approach (EANN-GA), the sensor node nearest to the cluster center with the highest remaining energy is selected as the cluster head (CH). Additionally, a mobile sink (MS) is introduced to harness the power of CHs, and the number of paths utilized by the MS is estimated through a genetic approach. Based on this path information, the MS enters each cluster to initiate the data-gathering process. Results: Performance analysis of the presented methodology demonstrates significant improvements in energy efficiency and the extension of network lifetime. By employing the proposed EANN-GA technique for CH selection and optimizing MS path utilization, the study showcases enhanced operational effectiveness within WSNs. Conclusion: The findings of this research underscore the effectiveness of the proposed methodology in enhancing energy efficiency and prolonging the lifespan of wireless sensor networks. Through the innovative integration of BLE sensors, EANN-GA CH selection, and genetic-based MS path estimation, the study contributes valuable insights toward addressing the critical challenges of energy conservation in WSNs.

A High-Performance, Secure Custom Electronics Circuit for Biopotential Measurement and Processing

Purpose: In this paper, we propose a secure wearable and portable device aiming to (i) monitor physical activity in medical and clinical rehabilitation, (ii) evaluate the subject's movements in sports environments, and (iii) monitor wellness indicators. Materials and Methods: The innovative low-power custom circuit can acquire, pre-process, digitalize, and transmit EMG signals to a Raspberry PI4 device via a low-energy Bluetooth module. The Raspberry PI4 is a hub that sends data to a cloud-based system for remote monitoring and processing. The best cybersecurity practices have been implemented: firewall, anti-DDoS and SELinux. Results: We have assessed the system's vulnerability before and after the system's hardening. SELinux makes the system safer and prevents unauthorized access to patients' data health by tampering with the devices. Conclusion: Adopting IoT systems in telemedicine requires greater attention to cybersecurity. It is necessary to implement security mechanisms to guarantee the privacy of patient's health data.