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ESR2- Sensory Feedback for Prosthetic Devices

Understand the basic principle of tactile encoding and define effective ways to implement it in a neuroprosthetic device, by defining optimal stimulation protocols (both invasive and non-invasive) to elicit appropriate perception.

TAGS   Biological Touch   Prosthetics   Technologies for Touch

Overview

Objectives

Understand the basic principle of tactile encoding and define effective ways to implement it in a neuroprosthetic device, by defining optimal stimulation protocols (both invasive and non-invasive) to elicit appropriate perception. The fellow will develop algorithms to translate stimulus properties encoded in the spike trains from artificial sensors into appropriate stimulation patterns. The algorithms will benefit from the deeper understanding of neural encoding of tactile perception developed within research themes 5 and 6. The improvements of the proposed algorithms will be validated by the development of systems and protocols for performance evaluation, for example in terms of performance of manipulation of human amputees equipped with upper limb prosthetic devices or of subjects using wearable systems or teleoperating robotic systems.

Expected Results

Prosthetic and orthotic devices equipped with novel algorithms that better communicate a natural touch sensation to the user, improving ease of use and acceptability.

Secondments

  • IIT-CNCS

    to learn about neural encoding

  • SISSA

    to integrate stimulation protocol on prosthesis

  • SensArs

    to learn psychophysics techniques for validation, clinical tests and system integration

Supervisors

  • S. Micera

  • S. Panzeri
  • F. Nieto

  • SO from Ossur TBA

Guilia Dominijanni

The will to understanding how to imporove restoration and enhancement of human abilities arose early during my studies. I've been firstly fascinated by the research on sensory feedback for prosthetic limbs and then I extended my interest in the broader field of bidirectional control of human machine interfaces.

Neutouch for me

NeuTouch is a great chance to interact and collaborate with talented researchers with different backgrounds for a common purpose.

Info

  • Research Topics

    Somatosensoty feedback

  • Institution

    EPFL

  • Background

    B.Sc. in Clinical engineering (Università degli studi di Roma, 2013-2016)
    M.Sc. in Bionic engineering (Università di Pisa and Scuola Superiore Sant'Anna, 2016-2019)

Thesis

Investigating neural resource allocation in the sensorimotor control of extra limbs. EPFL, 2024.

Abstract

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges in achieving effective bidirectional human-machine collaboration. The questions are whether people can adjust and learn to use a new robotic limb and whether this is achievable without limiting their other physical capabilities. In realizing successful robotic body augmentation, it's crucial to make sure that introducing an extra (artificial) limb doesn't compromise the functions of a natural (biological) limb. This thesis presents research on robotic body augmentation via extra robotic limbs, merging the definition of theoretical foundations with empirical investigations on the adaptability of the human body and brain to advanced technological integrations. Central to this work is the concept of the 'Neural Resource Allocation Problem', defined and discussed in the introduction of this thesis. It addresses the challenges of integrating augmentative devices with the human body without compromising natural functionalities. Such conceptualization is crucial to ensure that augmentation technologies effectively expand user's capacities rather than simply rerouting resources and replacing an existing function with a different, new one.

Based on this theoretical groundwork, I then proposed operational guidelines and detailed the development and characterization of an ad-hoc human-machine interface based on gaze and diaphragmatic respiration for extra robotic arms. The validation carried out on a virtual extra arm thanks to the neuro-robotic platform engineered for this work and the subsequent testing with an extra robotic arm proved the proposed human-machine interface to be effective and non-intrusive, substantiating the proposed methodology. The in-depth analysis of how users adapt to a toe-controlled robotic thumb that concludes the empirical work reported in this thesis is once again rooted in the conceptual framework detailed at the beginning of the thesis. It offered a window into necessary trade-offs, long term effects and the neural adaptations involved with significant and generalisable augmented-hand motor learning. This thesis contributes to the improvement of targeted human machine interfaces design for extra robotic limbs. The non-intrusive biosignals identified have the potential to be further explored and be applied for the control of degrees of freedom of more sophisticated robotic arms to enable more advanced augmentation. This thesis also contributes to a deeper understanding of the consequences of semi-intensive use of robotic body augmentation at behavioural and neural level.

Publications

Leal Pinheiro, D., Dominijanni, G., Maenza, F. P., Dirat, H., Shokur, S., & Micera, S. (2025). Exploring Skill Generalization with an Extra Robotic Arm for Motor Augmentation. Advanced Intelligent Systems, 2500086. https://doi.org/10.1002/aisy.202500086

Clode, D., Dowdall, L., da Silva, E., Selén, K., Cowie, D., Dominijanni, G., & Makin, T. R. (2024). Evaluating initial usability of a hand augmentation device across a large and diverse sample. Science robotics, 9(90), eadk5183. DOI: 10.1126/scirobotics.adk5183

Dominijanni, G., Pinheiro, D. L., Pollina, L., Orset, B., Gini, M., Anselmino, E., ... & Micera, S. (2023). Human motor augmentation with an extra robotic arm without functional interference. Science Robotics, 8(85), eadh1438. DOI: 10.1126/scirobotics.adh1438

Ozgur, A. G., Wessel, M. J., Olsen, J. K., Cadic-Melchior, A. G., Zufferey, V., Johal, W., ... & Hummel, F. C. (2022). The effect of gamified robot-enhanced training on motor performance in chronic stroke survivors. Heliyon, 8(11). DOI: 10.1016/j.heliyon.2022.e11764 

Dominijanni, G., Shokur, S., Salvietti, G., Buehler, S., Palmerini, E., Rossi, S., ... & Micera, S. (2021). The neural resource allocation problem when enhancing human bodies with extra robotic limbs. Nature Machine Intelligence, 3(10), 850-860. https://doi.org/10.1038/s42256-021-00398-9

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