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People with reduced upper-limb mobility depend mainly on facial gestures to communicate with the world; nonetheless, current facial gesture-based interfaces do not take into account the reduction in mobility that most people with motor limitations experience during recovery periods. This study presents an alternative to overcome this limitation, a human-computer interface based on computer vision techniques over two types of images: images of the user’s face captured by a webcam and screenshots of a desktop application running on the foreground. The first type is used to detect, track, and estimate gestures, facial patterns in order to move and execute commands with the cursor, while the second one is used to ensure that the cursor moves to specific interaction areas of the desktop application. The interface was fully programmed in Python 3.6 using open source libraries and runs in the background in Windows operating systems. The performance of the interface was evaluated with videos of people using four interaction commands in WhatsApp Desktop. We conclude that the interface can operate with various types of lighting, backgrounds, camera distances, body postures, and movement speeds; and the location and size of the WhatsApp window does not affect its effectiveness. The interface operates at a speed of 1 Hz and uses 35 % of the capacity a desktop computer with an Intel Core i5 processor and 1.5 GB of RAM for its execution; therefore, this solution can be implemented in ordinary, low-end personal computers.

Interfaz humano-computador basada en gestos faciales y orientada a la aplicación WhatsApp para personas con limitación motriz de miembros superiores