This work presents a comparison between techniques for recognizing inorganic molecular structures from hand-drawn sketches. The approach used combines Convolutional Neural Networks (CNN) and the Histogram of Oriented Gradients (HOG) method to classify and recognize these sketches. For the tests, we used images drawn by high school students. The comparison of the results obtained with the CNN and HOG techniques was carried out in detail. Additionally, we developed an application capable of generating three-dimensional visualizations of the molecular structures, allowing their representation in a virtual environment. This 3D visualization provides a more intuitive understanding of the molecular structures. This work highlights the effectiveness of machine learning technologies in education, by offering an accessible and interactive educational tool that complements traditional chemistry teaching. In this way, it provides students with a deeper understanding of the physical and chemical properties of inorganic molecules, enriching the learning process and making it more engaging.

This work investigated the usability of two interaction techniques in low-cost virtual environments using smart-watches: on-screen continuous gesture recognition and the MDDTW algorithm for touchless gestures. While continuous gesture recognition requires direct interaction on the device's screen, MDDTW allows users to perform gestures in the air without the need for physical touch. Although users initially preferred continuous gesture recognition, the results revealed that MDDTW achieved slightly higher scores. This underscores the crucial importance of considering user experience in the development of new technologies. The comparative analysis between the two approaches contributes to the design and implementation of interactions in accessible virtual environments, especially concerning the integration of physical gestures as part of the interaction. This work contributes to understanding the factors influencing usability in virtual environments and highlights the need for user-centered approaches in designing interactive technologies.

In this work, we propose a method for recognizing percussive gestures using smartwatches with accelerometers and the MDDTW algorithm, incorporating an activation threshold to identify the beginning of gestures. We developed a system that allows simulating drum percussion in a musical duet, providing an interactive and engaging experience for users. Our method utilizes an activation threshold based on the value of gravity to identify the onset of percussive gestures, enabling precise and efficient detection of user movements. We conducted a controlled experiment where participants were instructed to perform predefined percussive gestures, which were captured by the smartwatch sensor and processed by the system. The results demonstrated good accuracy, with a consistent recall rate, indicating the system's ability to correctly identify performed gestures. The Fl-score, as a combined measure of precision and recall, confirmed the overall good performance of the method.