Smart As Apps is proud to have developed an innovative app for monitoring epilepsy patients in collaboration with Monikit. In this app, which is designed to document seizures, we have incorporated a special sensor and a machine learning model from Monikit.

At the beginning of the project, we focused on requirements engineering, allowing us to precisely define the features of the app. An important step in this process was recruiting UI/UX designers, who developed an intuitive and user-friendly interface.

In close collaboration with Monikit, we succeeded in designing the Bluetooth Low Energy (BLE) interface. This ensures efficient communication between the sensor and the app, which is crucial for continuous monitoring.

During the development of the Android app, we relied on the Model-View-ViewModel (MVVM) architecture and the Android Architecture Components. This allowed us to ensure performance and scalability. At the same time, we placed great importance on data protection and implemented robust encryption techniques for sensitive patient data.

A central aspect of the project was the integration of the Monikit sensor into the app using BLE technology. This allowed for continuous health monitoring and precise documentation of seizures. With the additional integration of Firebase Cloud Messaging, we were able to deliver real-time notifications, enabling immediate response to detected seizures.

A particular highlight of the project was the implementation of a TensorFlow machine learning model. This model aids in detecting seizures in sensor data.