Thesis Project Form
Title (tentative): Development and evaluation of non-invasive sensory feedback strategies for the DexterHand prosthetic platform in people with upper-limb amputation| Thesis advisor(s): Chiappalone Michela, Nicolò Boccardo (nicolo.boccardo@iit.it, Rehab Technologies - Istituto Italiano di Tecnologia, IIT) | E-mail: |
| Address: Via Opera Pia 13, 16145 Genova | Phone: |
Description
Motivation and application domain
This thesis originates in Rehab Technologies - INAIL - IIT lab (https://www.iit.it/web/rehab-technologies-inail-iit-lab), in the framework of the Dexterhand and Biointernect project. Dexterhand is a multi-joint prosthetic platform developed in collaboration with Istituto Italiano di Tecnologia (Genova) and INAIL Prosthetic Research Center in Vigorso di Budrio (Bologna). At the state of the art, Dexterhand Prosthetic Platform comprises a poli-articulated hand with a 2 degrees-of-freedom wrist, sensorized fingertips and feedback restitution. The prosthesis is mainly controlled by the user via electromyographic signals (EMG), extracted from the residual limb.
General objectives and main activities
While DexterHand provides advanced multi-joint actuation, sensorized fingertips and EMG-based control, the restitution of somatosensory feedback remains a major challenge for improving embodiment and natural interaction. The thesis aims to explore non-invasive sensory feedback strategies to restore tactile perception in individuals with upper-limb amputation. The overall goal of the thesis is to design, implement, and evaluate non-invasive feedback mechanisms that convey tactile information from DexterHand to the user through skin stimulation.
The student will: investigate state-of-the-art feedback technologies and identify optimal encoding strategies for force, contact, or grip events; implement embedded control algorithms and hardware interfaces to drive the selected actuators in real time; integrate the feedback system with DexterHand fingertip sensors and EMG-driven control; take advantage a software interface for data acquisition, stimulus modulation, and experiment management (e.g., Python, MATLAB, Unity); perform experimental sessions on able-bodied volunteers and on amputee participants to assess discrimination accuracy, usability, and impact on prosthetic control; analyze results and contribute to defining guidelines for future implementations of sensory feedback on the DexterHand platform.
The student will: investigate state-of-the-art feedback technologies and identify optimal encoding strategies for force, contact, or grip events; implement embedded control algorithms and hardware interfaces to drive the selected actuators in real time; integrate the feedback system with DexterHand fingertip sensors and EMG-driven control; take advantage a software interface for data acquisition, stimulus modulation, and experiment management (e.g., Python, MATLAB, Unity); perform experimental sessions on able-bodied volunteers and on amputee participants to assess discrimination accuracy, usability, and impact on prosthetic control; analyze results and contribute to defining guidelines for future implementations of sensory feedback on the DexterHand platform.
Training Objectives (technical/analytical tools, experimental methodologies)
1. Programming skills: C, C#, Unity, Matlab/Python;
2. Analysis of biosignal data and feature extraction;
3. Design, implementation, and execution of experiments.
2. Analysis of biosignal data and feature extraction;
3. Design, implementation, and execution of experiments.
Place(s) where the thesis work will be carried out: Rehab Technologies Lab, Istituto Italiano di Tecnologia - IIT (via Morego 30, 16163, Genova))
Additional information
Pre-requisite abilities/skills: previous lab experience, data analysis
Maximum number of students: 1