El objetivo de la tesis fue optimizar el algoritmo automated Dynamic AOI Mapping (aDAM). aDAM es un algoritmo de visión por ordenador para la detección de seguimiento de objetos mediante identificación de líneas e intersección de esquinas. El objetivo de la optimización fue rastrear objetos de dimensiones reducidas y realizar pruebas con ellos.Usability Testing is indispensable in the development of medical devices. It validates intended functionality, guaranties patient safety, and provides a technological advantage. In this thesis, the performance of the automated Dynamic AOI Mapping algorithm (aDAM) is evaluated on two small medical devices. Its functionality is tested to see if it is suitable for its use in the growing field of eHealth applications. The performance showed very promising results. In the area of tracking, with the correspondent adjustments, a correct gaze point mapping can be achieved up to 98% of the cases. For Screen Matching purposes, a highly accurate matching can be acquired if the device plays a main role in the analyzed video. In case the images are from very low-resolution, the algorithm does not function correctly. Nevertheless, this algorithm performs well, and it can be used for the analysis of small medical devices.