Healopad

We fabricate a smart wound dressing of two segments: A) The disposable bandage (with electrodes) and B) a reusable sensor (receiver). The disposable bandage: We propose Polyurethane (PU) foams as wound dressing fabricated by solvent casting/salt leaching method. A bilayer of nanofiber is added to the scaffold and subsequently coated with Ag-AgCl for anti-bacterial effects and as electrodes. Polyurethane gained much awareness due to its mechanical and physical improvements over other polymers. It can be created in forms such as film, foam, hydrogel, and hydrocolloid, and shapes. Additionally, the nanofiber enhances the repair/regeneration of skin tissue. Therefore, this product can be used generally for chronic wounds. Our proposed Wound dressing with biosensors connects to the externally positioned reusable biosensors to monitor and predict wound healing procedure. The reusable sensor: Our proposed software will use machine learning methods to monitor and predict wound healing procedures. The sensor is attached to the bandage as a tool to measure and monitor the status of chronic wounds. It acts as an effective assessor of healing and predicting outcomes of treatments. Initially, The Machine Learning system will be trained using supervised learning using historical data. The real data collected through sensors, such as the uric acid levels and the rate of moisture of the wound, is fed through the Machine Learning Database. The Trained Database uses Artificial Neural Network Algorithms (ANN), to classify and compare the real-time data with the trained historical data, facilitating real-time monitoring. The Machine Learning Database is self-learning and updates the information (data) as new data becomes available. Prediction of wound status is facilitated through the output of ANN on a mobile device and remotely, so the wound can be monitored 24/7. Forecasting the likelihood of a particular outcome is facilitated as the real data is compared with the l