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Dynamic Reconfiguration for Multi-Magnet Tracking in Myokinetic Prosthetic Interfaces
  • +3
  • Sergio A. Pertuz,
  • Davi A. Mendes,
  • Marta Gherardini,
  • Daniel Muñoz Arboleda,
  • Helon Vicente Hultmann Ayala,
  • Christian Cipriani
Sergio A. Pertuz
Davi A. Mendes
University of Brasilia
Marta Gherardini
Scuola Superiore Sant’Anna
Daniel Muñoz Arboleda
University of Brasilia

Corresponding Author:[email protected]

Author Profile
Helon Vicente Hultmann Ayala
Pontifical Catholic University of Rio de Janeiro
Christian Cipriani
Scuola Superiore Sant’Anna


Recently myokinetic interfaces have been proposed to exploit magnet tracking for controlling bionic prostheses. This interface derives information about muscle contractions from permanent magnets implanted into the amputee's forearm muscles. Machine learning models have been mapped on Field Programmable Gate Arrays (FPGAs) to track a single magnet, achieving good precision and computational efficiency, but consuming a large area and hardware resources. To track several magnets, here we propose a novel solution based on dynamic partial reconfiguration, switching three prediction models: a linear regressor, a radial basis function neural network, and a multi-layer perceptron neural network. A system with five magnets and 128 magnetic sensor inputs was used and experimental data were collected to train a system with five hardware predictors. To reduce the complexity of the models, we applied principal component analysis, ranking by correlation the number of inputs of each model. This run-time reconfigurable solution allows the circuits to be reconfigured in order to select the most reliable predictor model for each magnet while the rest of the circuit continues to operate extracting the most significant information from the captured signals. Thus, the proposed solution remarkably reduces the hardware occupation and improves the computational efficiency compared to previous solutions.
04 Apr 2024Submitted to TechRxiv
08 Apr 2024Published in TechRxiv