Alejandro Garcia-Sosa

Alejandro Garcia-Sosa, Jose J. Quintana-Hernandez, Miguel A. Ferrer Ballester et al.

Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited.

S. Fortea-Sevilla, A. Garcia-Sosa., P. Morales-Almeida et al.

The increasing prevalence of Autism Spectrum Disorder and Attention-Deficit/ Hyperactivity Disorder among students highlights the need to improve evaluation and diagnostic techniques, as well as effective tools to mitigate the negative consequences associated with these disorders. With the widespread use of touchscreen mobile devices, there is an opportunity to gather comprehensive data beyond visual cues. These devices enable the collection and visualization of information on velocity profiles and the time taken to complete drawing and handwriting tasks. These data can be leveraged to develop new neuropsychological tests based on the velocity profile that assists in distinguishing between challenging cases of ASD and ADHD that are difficult to differentiate in clinical practice. In this paper, we present a proof of concept that compares and combines the results obtained from standardized tasks in the NEPSY-II assessment with a proposed observational scale based on the visual analysis of the velocity profile collected using digital tablets.