Pressure ulcers (PU) are deep scars on the skin that cause pain, infections and severe health complications. Most movement-impaired subjects are vulnerable to PU, leading to permanent and irreversible skin damage. The system proposed in this paper aims to prevent PU formation with the design and implementation of a wirelessly controlled device that predicts PUs before their occurrence and attempts to prevent it using therapeutic feedback. A flexible pad that consists of multiple types of sensors is used, theses sensors continuously and non-invasively monitor ulcer-related vital signs in vulnerable areas, and uses these data to predict PU with a decision-making process. When PU is detected an electrical stimulation (ES) unit is automatically activated. Stimulation prevents PU formation by increasing local blood flow to the simulated area and eliminating the main factor that leads to PU formation. The system successfully monitored and predicted PU; tests were performed on three healthy volunteers and one volunteer with sacral ulcers. Results including readings of blood oxygenation, force, humidity and temperature were recorded as graphs to monitor decay/increase in values more efficiently.

The Pressure ulcers (PU) are irreversible skin damage targeting vulnerable areas of the human body, it is an unfortunate consequence of aging, diabetes, paralysis and post surgery hospitalization. Managing PU is challenging as treatment usually fails to reverse the damage. This study proposes a new method to prevent PU at very early stages using a monitoring/predictive system. The PU prediction system contains a wireless sensing device that continuously monitors the vital signs of the living tissue which is prone to develop PU. Data collected from the sensing device are used to drive a mathematical equation that describes the behavior of the living system and uses this model to predict future values indicating PU's onset, predicted data are then used in a decision-making structure to evaluate PU risk. The results were recorded for (4 hours) as three volunteers took the supine position, (14400) discrete data were acquired and used to derive a fourth-degree polynomial equation able to predict future data with an error of $(\pm 0.264$ %), when PU is predicted an alarm is sent to the user interface to notify healthcare givers.

The cirrhosis and cirrhosis-related problems are connected to the degree of fibrosis in the liver. The purpose of this paper is to propose an automated method for identifying liver fibrosis using ultrasound shear wave elastography (700) images that is based on a hybrid machine learning approach using a convolutional neural network (CNN) with two types of classifier (SoftMax and support vector machine (SVM)). The dataset gathered from hospitals is used in the training and testing phases of the model. The objective is to develop a hybrid machine learning model that can classify images based on their stage of fibrosis. The suggested system comprises three stages. The first is the preprocessing step, which starts with countor detection and continues with the "contrast limited adaptive histogram equalization (CLAHE)" technique to show the properties of liver tissue. In the second step, the CNN algorithm was utilized, which was based on several images to extract deep features and identify shear wave elastography (SWE) samples. In the third step, SVM and SoftMax functions are used to classify liver fibrosis. A five-class model (normal, F1, F2, F3, and F4) was developed. The result illustrates how successfully the CNN-SoftMax and CNN-SVM classifiers classified liver fibrosis in the test dataset, with 97.18% and 98.59% accuracy, respectively.

Biomedical signal processing is essential for diagnosing and monitoring various health disorders. Conventional signal processing methods frequently struggle to Biomedical signal processing is essential for diagnosing and monitoring various health disorders. Conventional signal processing methods frequently struggle to

Background: Flatfoot deformity is a common condition in children and teenagers that may increase the risk of knee, hip, and back pain. Most of the insoles suggested to treat flatfoot symptoms are not designed to adapt to foot temperature during walking, and they are either too soft to provide support or hard enough to be uncomfortable. Purpose: This study aims to develop an advanced solution to diagnose and treat flexible flatfoot (FFT) using infrared thermography measurements and a hybrid insole reinforced by nitinol (NiTiCu) smart-memory-alloy wires (SMAWs), this super-elastic alloy can return back to its pre-deformed shape when heated, which helps to reduce the local high-temperature points caused by the uneven pressure of FFT. This approach achieves a more uniform thermal distribution across the foot, which makes the hybrid insole more comfortable. Methods: The study involved 16 subjects, divided into two groups of eight flat-footed and eight normal. The procedure includes two parts, namely, designing a prototype insole with SMAW properties based on thermography measurement by using SolidWorks, and evaluating this design using Ansys. Second, a hybrid insole reinforced with SMAWs is customized for flatfoot subjects. The thermography measurement differences between the medial and lateral sides of the metatarsophalangeal line are compared for the normal and flatfoot groups before and after wearing the suggested design. Results: The results show that our approach safely diagnosed FFT and significantly improved the thermal distribution in FFT subjects by more than 80% after wearing the suggested design. A paired t-test reported significant (p-value > 0.001) thermal decreases in the high-temperature points after using the SMAW insole, which was closely approximated to the normal subjects. Conclusions: the SMAW-reinforced insole is comfortable and suitable for treating FFT deformity, and infrared thermography is an effective tool to evaluate FFT deformity.