Sign languages are one of the main rehabilitation methods for dealing with hearing loss. Like any other language, the geographical location will influence on how signs are made. Particularly in Colombia, the hard of hearing population is lacking from education in the Colombian Sign Language, mainly due of the reduce number of interpreters in the educational sector. To help mitigate this problem, Machine Learning binded to data gloves or Computer Vision technologies have emerged to be the accessory of sign translation systems and educational tools, however, in Colombia the presence of this solutions is scarce. On the other hand, humanoid robots such as the NAO have shown significant results when used to support a learning process. This paper proposes a performance evaluation for the design of an activity to support the learning process of all the 11 color-based signs from the Colombian Sign Language. Which consists of an evaluation method with two modes activated through user interaction, the first mode will allow to choose the color sign to be evaluated, and the second will decide randomly the color sign. To achieve this, MediaPipe tool was used to extract torso and hand coordinates, which were the input for a Neural Network. The performance of the Neural Network was evaluated running continuously in two scenarios, first, video capture from the webcam of the computer which showed an overall F1 score of 91.6% and a prediction time of 85.2 m, second, wireless video streaming with NAO H25 V6 camera which had an F1 score of 93.8% and a prediction time of 2.29 s. In addition, we took advantage of the joint redundancy that NAO H25 V6 has, since with its 25 degrees of freedom we were able to use gestures that created nonverbal human-robot interactions, which may be useful in future works where we want to implement this activity with a deaf community.
