Myoelectric (ME) prosthetic devices assist amputees in daily activities by interpreting electrical signals from muscle movements. These signals, known as Electromyography (EMG), are generated by muscle contraction or expansion and are used to control the prosthetic limb. EMG analysis is applied in various fields such as muscle diagnosis, rehabilitation, human-machine interaction, and speech analysis. EMG signals can be acquired through invasive needle electrodes or non-invasive surface electrodes (sEMG). While sEMG is preferred in ME prosthetics due to its non-invasive nature, it is susceptible to noise from Power Line Interference (PLI), motion artifacts, and instrument noise. The challenge lies in minimizing these noise effects while preserving the integrity of the EMG signal. Classical digital filters are often used to remove noise, but they can also reduce the amplitude of the EMG signal, creating a trade-off between noise attenuation and signal fidelity. 

The Silent Speech Interface (SSI) is an emerging field in Human-Computer Interaction (HCI) research. SEMG-based SSI leverages the electrical activity of facial muscles to detect speech. However, current SSI techniques rely on computationally intensive methods and complex machine learning algorithms, making real-time implementation challenging, particularly for cost-sensitive applications like communicative aids for laryngectomy patients. This research aims to develop a simpler, more computationally efficient SEMG-based SSI model without compromising on accuracy. 

IoT-based medical devices are transforming health monitoring by enabling real-time, remote care through sensors, microcontrollers, and wireless communication. These devices facilitate non-invasive fetal ECG extraction, allowing expectant parents and healthcare providers to remotely monitor fetal heartbeats with enhanced accuracy using techniques like Empirical Mode Decomposition (EMD), Kalman filtering, Principal Component Analysis (PCA), and Blind Source Separation (BSS). Additionally, IoT solutions continuously monitor ECG signals for arrhythmia and cardiac disorder detection, employing algorithms like Dynamic Time Warping (DTW) for real-time analysis of heart rhythms. This integration allows for early detection of irregularities, ensuring timely medical intervention and improving overall patient care.