Abstract
All the gestures and movements we make are influenced by our psychomotor abilities. This mobility deteriorates over the years. It is logical to think that an older individual has worse mobility than a younger one if they do not suffer from other pathologies. On this premise, the main aim of this research work is based on the detection of semantic biomechanical features, using a hand-tracking controller (Leap Motion) through three calibration tests. The data collected by this hand-tracker has allowed to measure hand-wrist movements. Likewise, this paper aims to highlight different tasks based on those used by neurologists customarily based on the Hoehn Yahr [11] and UPDRS scales [9]. Indeed, this study intends to visualize the differences between healthy participants. The manuscript provides some promising findings that will help tailoring biometric indicators for non-normative participants in future research using this technology.
This research work was partly funded by one intramural project of Rey Juan Carlos University and a contract with the Spanish Defense Ministry (2022/00004/004 and 2021/00168/001, respectively).
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Rodrigo-Rivero, C., del Olmo, C.G., Álvarez-Marquina, A., Gómez-Vilda, P., Domínguez-Mateos, F., Palacios-Alonso, D. (2022). Acquisition of Relevant Hand-Wrist Features Using Leap Motion Controller: A Case of Study. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_23
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