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A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System.

Andrea Catherine Alarcón-AldanaMauro Callejas-CuervoTeodiano Freire BastosAntonio Padilha Lanari Bo
Published in: Sensors (Basel, Switzerland) (2022)
This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model.
Keyphrases
  • upper limb
  • molecularly imprinted
  • electronic health record
  • high speed
  • deep learning
  • data analysis