Wearable sensor devices are becoming increasingly important in medical applications due to their high sensitivity and compact size, with flexible elastomer materials playing a crucial role in their functionality. This research focuses on developing a capacitive pressure sensor (CPS) using Multiphysics software to explore its potential for medical use. The CPS is designed with a cylindrical structure, utilizing air as the dielectric medium between a polysilicon base and a polydimethylsiloxane (PDMS) diaphragm. Simulation results indicate that at a pressure of 1 kPa, the CPS achieves a capacitance of 1.28 pF and stores 0.644 pJ of electrical energy. Moreover, the sensitivity of the sensor improves as the pressure increases, with analytical results showing strong agreement with numerical analyses. These findings highlight that the CPS can effectively store electrical energy and respond accurately to pressure variations, which is essential for reliable performance in medical applications. The promising results from the simulations suggest that the CPS could be a viable option for integration into wearable medical devices, potentially improving patient monitoring and diagnostics. Future work will involve fabricating the sensor and conducting experimental tests to validate the simulation results. This step is critical to ensure that the sensor performs as expected in real-world conditions and meets the stringent requirements of medical applications. This research underscores the potential of the CPS in the realm of wearable medical devices, highlighting its promise for contributing significantly to patient care and diagnostics.

Wireless Sensor Network (WSN) is an important component of healthcare. The design of the power management unit for WSN poses significant challenges, as it not only needs to achieve good current efficiency but also requires high power supply rejection (PSR) and good load transient performance. This paper presents a low-dropout regulator (LDO) with low quiescent current and fast transient response to adequately meet the power supply requirements of WSN systems. To ensure system stability and reduce voltage spikes during load transients, an adaptive frequency compensation network is integrated into the circuit. Additionally, the LDO incorporates a level shifter that facilitates bi-directional transmission of voltage signals across different power systems. The proposed LDO is designed and simulated in a 180 nm BCD process. It operates under a wide input voltage range from 0.8V to 5.5V, supports maximum load currents of up to 500mA, and allows output voltages to vary from 0.8V to 3.6V by adjusting the feedback resistance. As a result of implementing the adaptive frequency compensation circuit, the overshoot and undershoot voltages at an output voltage of 1V are measured to be only 23mV and 5mV, respectively. Moreover, the LDO achieves a PSR of 83dB for bias voltage and 98dB for input voltage at 1kHz. The level shifter's highest working frequency can reach 26MHz under supply voltages (VCCA = 1.65V to 5.5V; VCCB = 5V), thereby enabling high-speed data transmission. Finally, the LDO consumes a quiescent current of 42μA while incorporating a bandgap reference circuit and other auxiliary circuits.

This study delves into the design and optimization of mobile microrobots tailored for tasks requiring sub-micrometer precision, addressing key challenges in the miniaturization and efficiency of microrobotic systems. Each microrobot is composed of a mobile platform, a manipulation unit, and a specialized end effector, collectively enabling them to perform a diverse array of operations on various surfaces. The mobile platforms provide three degrees of freedom (DOF) and can support loads ranging from 10 g to 500 g, with actuation based on the slip-stick principle. A novel configuration of the components offers promising characteristics, notably the low voltage required to drive the actuators, facilitating battery integration. The manipulation unit incorporates actuators that utilize a combination of electric motors and piezoelectric materials. The research explores two distinct mobile platforms that vary in dimensional scale and pulling force, both actuated using piezoelectric materials, providing insights into how different design parameters affect performance. The study focuses on the effects of platform design and piezoelectric material variations on the external voltage required for actuation. The findings contribute to the development of more efficient manipulation units, with a key challenge being the further miniaturization of these units through the optimization of piezoelectric material shapes and properties. This research underscores the potential for enhancing the design of compact and efficient manipulation units, which is critical for the advancement of mobile microrobots in precision applications.

Electromyography (EMG) is a technique that measures the electrical activity of the muscles and it has been used extensively in the field of physiotherapy to assess the muscle function and activity. Grading muscle power is an important aspect of assessing muscle function, as it provides information about the strength and endurance of muscles. Presently, the physiotherapist uses Manual Muscle Testing (MMT) for grading muscle power however it requires the therapist with good expertise. In this work, an Internet of Medical Things (IoMT) based Smart EMG device is designed and developed for monitoring the patients suffering from abnormal musculoskeletal health conditions. Further, the EMG signals are acquired from normal individuals and the patients with abnormal health conditions. Also, the muscle power grading is used to grade the EMG signals and the Convolutional Neural Network (CNN) based deep learning algorithm is utilised to visualize the progress of course of treatment provided to the patients with musculoskeletal problems such as stroke, spinal cord injuries etc. The entire analysis is carried out Google Co-Laboratory based IoT cloud platform and the algorithms are coded using Python programming language. Results demonstrate that the proposed smart IoMT based smart device can predict the different muscle power with an average accuracy of 97.5 % which proves the effectiveness of the device. This work appears to be of high clinical relevance since the proposed device is capable of providing valuable information about muscle function and enable the physiotherapists to design personalised treatment plans for patients with musculoskeletal disorders.

This study proposes a health monitoring system for snoring detection utilizing Tiny Machine Learning (TinyML) models, specifically designed for resource-constrained wearable Internet of Things (IoT) devices. This research addresses significant constraints associated with running Machine Learning models on IoT devices, such as latency, limited memory, and low computational resources. These parameters are essential for real-time monitoring in healthcare applications, where prompt response is critical. The research focuses on developing a TinyML model capable of identifying specific audio patterns related to snoring during sleep. Experimental evaluations conducted in real-world sleep environments with the TinyML model deployed on resource-constrained wearable IoT devices. The evaluation results show that the proposed model achieves high accuracy while utilizing minimal computational resources and without introducing latency issues. The integration of Audio (Syntiant) and advanced audio preprocessing techniques, the proposed system improves the efficiency of the TinyML model on wearable devices. The quantized TinyML model achieved accuracy of 95.85% with a low latency of 48 ms, utilizing only 17.0K RAM and 34.07K flash memory for real-time snoring classification. This study highlights the benefits of practical deployment of TinyML model for snoring detection on resource-constrained wearable IoT devices, demonstrating that such models can operate effectively within the constraints of current wearable technology.

Honey is a natural sweet food product with multiple nutritional and medicinal properties making it a healthy alternative to processed sugars. Nonetheless, its composition differs based on the climate, soil, altitude, production method, and pollen source, consequently affecting its health benefits and market value. With the consumers’ recent interest and purchase of dietary products the global honey market has greatly increased. To keep up with production, or simply for financial gain, some producers/companies are now blending pure honey with cheaper substances that possess similar physical characteristics. As there are no notable visible differences between pure and adulterated honey, it is extremely difficult to determine the purity of the available honeys. In this study, an electrochemical genosensor based on the sandwich format DNA hybridization reaction between two complementary probes was developed for the detection and quantification of Erica arborea pollen DNA in real samples. Analyzing public database platforms, a 98 base-pair DNA-target probe capable of unequivocally detecting the pollen from E. arborea was selected and designed. The complementary probe to the DNA-target oligonucleotide sequence was then cut into a 28 base-pair thiolated DNA-capture probe and a 70 base-pair fluorescein isothiocyanate-labelled DNA-signaling probe. To increase the hybridization reaction, a self-assembled monolayer formed from mixing the DNA-capture probe with mercaptohexanol was employed. Using chronoamperometry, the enzymatic amplification of the electrochemical signal was achieved with a concentration range of 0.07 to 2.00 nM. The DNA from certified E. arborea leaves was extracted using liquid nitrogen and mechanical grinding and the targeted region amplified by PCR. The developed genosensor was successfully applied for the detection and quantification of the DNA concentration of the extracted E. arborea plant leaves. So, the developed genosensor is a promising cost-effective, and innovative analytical method to detect and quantify the DNA concentration of plant DNA in real honey samples.

The evolution of smart grids (SG) is rapid and ubiquitous with the advent of information and communication technology. SGs enable utilities and prosumers to monitor energy consumption in real-time, thereby possessing effective supply and demand management. The subsets of SGs namely smart homes/smart buildings are tailored to take the benefits of SGs. These smart homes continuously record energy consumption data through smart meters, sensors, and smart appliances and facilitate consumers to track/manage their energy usage in real-time. Usually, the energy consumption of renewable energy-integrated smart homes depends on consumer behavior and weather conditions. These aspects lead to variance in the recorded energy consumption data from the desired levels. This variance in energy consumption impacts pattern finding, forecasting, financial risk, decision-making, and several other grid functionalities. Hence, comprehension of variance in energy consumption is essential to properly manage the energy. With this aim, this paper proposes the variance analysis on the smart home energy consumption readings using a statistical method named “Analysis of Variance (ANOVA)”. It is implemented on the Tracebase dataset, which is a smart city database and contains data for ten months. The data were collected in the city of Darmstadt, Germany, in 2012. The proposed ANOVA is applied to all these months’ data. As an initial step, the energy consumption readings recorded for every month at each day and at each hour are enumerated and this information is further used to perform day-wise variance analysis using ANOVA. The results show that there is a significant variance in several days in each month. Further, it is revealed that out of ten months, two months have high variability. Thus, this proposed variance analysis helps the stakeholders of SGs to take the necessary precautions for smooth grid functionalities as well as properly estimate future energy requirements.

Many wearable devices are commonly used to measure vital signs, heart rate is the most frequently monitored physiological information. Due to the weak signal strength of heartbeats, capturing these signals presents a significant technical challenge. Therefore, this paper adopts a non-invasive wearable device for detecting heartbeats. A wearable device is constructed using three polyvinylidene fluoride (PVDF) piezoelectric film sensors, placed at the three endpoints of an equilateral triangle with a side length of 3 cm, and positioned near the heart to detect heartbeat signals. The multiple sensors in this wearable device utilize the vibration signals to cause deformation in the PVDF piezoelectric film, generating voltage amplitude to represent the magnitude of the vibrations. Since the sensors are very sensitive to detect vibration signals, both physiological signals and surrounding noise are detected when the heart beats, resulting in a low signal-to-noise ratio (SNR) for heart rate signals and significantly increasing the chances of incorrect heart rate interpretation. In this paper, to improve the SNR, not only is hardware circuit design employed to amplify the signals and eliminate high-frequency noise using a low-pass filter, but a novel clustering algorithm is also used to group and classify the datasets by the three sensors. Irregular signals that deviate from the clusters are treated as noise, thereby eliminating noise from the signals and improving the quality of the physiological heart rate signals. According to the experimental results, the SNR of the heart rate signal after noise cancellation can be increased by 7dB, and the accuracy of heart rate signal recognition can reach 98.46%.

The search for more efficient ways to create both light sensors and VIS-IR sources has led to an ever-growing interest in coatings that convert UV radiation into white light. GaN LEDs with an emission wavelength of about 460 nm are most often used as UV emitters.

Step-down UV-converters often made from suspensions of rare-earth phosphor powders (e.g. cerium-activated yttrium-aluminum garnets YAG:Ce³⁺) for transformation of narrowband blue radiation into broadband VIS luminescent emission (540-590 nm). Depending on the technological process, the concentrations of trivalent Ce³⁺ ions and tetravalent Ce⁴⁺ ions in the yttrium aluminum garnet matrix change significantly. It is obvious that improving production technology is impossible without promptly measuring the concentration of Ce³⁺ ions in the transparent matrix, depending on the technological conditions of the synthesis.

The difficulty of experimentally determining the optical characteristics of films of optoelectronic converters is due to the fact that the phosphor composite is a classical turbid medium. Until now, in turbid media it is impossible to simply measure their most important optical parameters: when a light wave propagates in a turbid medium, its intensity gradually decreases due to scattering and absorption, the contribution of which to the extinction of light cannot be separated. Previously, we considered an original model approach and optimized composite production protocol to overcome ambiguity in experimental measurements, which makes it possible to determine the absorption of microcrystals in a phosphor-resin composition from the measured values of the transmittance of a set of films in a certain range of their thicknesses.

In this report, we examine an optical approach as well as design and contractions of physical sensing unit that allows the engineer to directly optimize the emission performance of optoelectronic UV downconverters for yttrium aluminum garnet powder doped with trivalent cerium (Ce³⁺) taking into account the turbidity of composite films.

Diaphragm material selection is the critical issue in addition to the shape or geometry optimization to improve the mechanical performance of micro-fabricated pressure sensors. Although single crystal silicon (Si), polysilicon (PolySi), graphene, Si₃N₄ and SiO₂ are the common diaphgram materials, LTCC is a good candidate for the high temperature applications. Thus, performance analysis of diaphragm is required to find optimum diaphragm size.In this study, low temperature cofired ceramic (LTCC) based circular diaphragm design was considered for the Fabry–Pérot Interferometer (FPI) pressure sensor application. Characteristics of low temperature cofired ceramic (LTCC) based circular diaphragm was analyzed by finite element method. Mechanical sensitivity, resonance frequency and static deflection of the diaphragm are the fundamental characteristic to be analysed and evaluated for MEMS sensor performance of micro electro-mechanical system (MEMS) based ltcc diaphragm.Thickness of LTCC diaphragms were selected 50 μm, 75 μm and 100 μm with the diameter of 3 mm, 4 mm and 5 mm, respectively. The performances of diaphragms are analytically studied and simulations were done using ANYS. Our results showed that sensitivity and frequency response of this structure can be designed flexibly by adjusting the parameters of the ceramic diaphragm size including radius and thickness. The key contribution of this work is to study the LTCC diaphragm with different size for future works.