Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller

Introduction

This repository contains the code to reproduce our paper M. Zanghieri et al., “Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller” [1].

It is an extension of our previous work M. Zanghieri et al., “Event-based low-power and low-latency regression method for hand kinematics from surface EMG” [2].

Usage

1. Run experiment_regressions.ipynb (or equivalently experiment_regressions.py).
2. Run read_results.ipynb to get the results statistics.

Authors

• Marcello Zanghieri - University of Bologna
• Pierangelo Maria Rapa - University of Bologna
• Mattia Orlandi - University of Bologna
• Dr. Elisa Donati - Institute of Neuroinformatics (INI) of University of Zürich and ETH Zürich
• Prof. Luca Benini - University of Bologna, ETH Zürich
• Prof. Simone Benatti - University of Bologna, University of Modena and Reggio Emilia

Citation

When using or referencing the project, please cite our paper:

@ARTICLE{zanghieri2024eventbased,
  author={Zanghieri, Marcello and Rapa, Pierangelo Maria and Orlandi, Mattia and Donati, Elisa and Benini, Luca and Benatti, Simone},
  journal={IEEE Sensors Journal}, 
  title={Event-based Estimation of Hand Forces from High-Density Surface {EMG} on a Parallel Ultra-Low-Power Microcontroller}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/JSEN.2024.3359917},
}

References

[1] M. Zanghieri, P.M. Rapa, M. Orlandi, E. Donati, L. Benini, S. Benatti, “Event-based Estimation of Hand Forces from High-Density Surface EMG on a Parallel Ultra-Low-Power Microcontroller,” IEEE Sensors Journal, pp. 1–1, 2024. DOI: 10.1109/JSEN.2024.3359917.

[2] M. Zanghieri, S. Benatti, L. Benini, and E. Donati, “Event-based low-power and low-latency regression method for hand kinematics from surface EMG,” in 2023 9th International Workshop on Advances in Sensors and Interfaces (IWASI), 2023, pp. 293–298. DOI: 10.1109/IWASI58316.2023.10164372

License

All files are released under the LGPL-2.1 license (LGPL-2.1) (see LICENSE).