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.