The Pohua system was developed using python 3.7. The main purpose of the system is to collect, process, save, display data. The Pohua system has the following modules: • Data collection: collect data from a data acquisition card with up to 5 channels of emg data with 12 bits resolution and 500 Hz and the angular position of the fingers with a sampling rate of 120 Hz. • Data processing: perform time-domain feature extraction of the emg signals in real-time. • Data saving: store raw and processed data for further analysis. • Classification: Perform a classification with 3 modalities: emg signals, leap motion controller and sensor fusion in real-time • Communication module: establish communication between the classification module and the serious game.

Employing computer vision to provide access to employees within a normal body temperature range

• A manuscript entitled: “interaction modalities used on serious games for upper limp rehabilitation: a systematic review, submitted and published. • Design of a serious game with different interaction modalities (emg, leap motion and sensor fusion) • Preliminary experiments performed with the leap motion controller in two lightning environments. • Statistical analysis of the experiments. • Investigate the muscles responsible for flexion and extension movements of the fingers to find the best position for the emg sensors. • EMG data acquisition system • EMG real-time segmentation using time-domain features.

The objective of the project was to carry out the planning of trajectories of a turtlebot type robot using robot operating system (ROS).

Consulting on the use of computer vision for analysis of satellite images for the diagnosis of pests in sugarcane crops

This project was developed using unity 3D engine and c# programing language. The interaction with the game could be done using 3 different methods, which are: emg signals, leap motion controller and sensor fusion.