Thesis Project Form

Body-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. In the last decades, body machine interfaces have been developed as tool to investigate neural control of movement and/ or to empower disabled people to reach assistive and rehabilitative goals. BMI based on body movement have been proved effective to support personalized a therapy for survivors of cervical spinal cord injury (cSCI).

Aim1: Developing the technology for a hybrid human-machine interface based on mapping body motion sensors and EMG signals onto a variety of control tasks. We will manipulate the sensory feedback (visual) to induce desired changes in the muscle and motion patterns of healthy and spinal cord injured subjects.
Aim 2: Assessing the activation and/or deactivation of targeted muscles and muscle synergies through a movement and EMG analyses.

The student will learn
- To analyze and correlate body signals from different sources such as movement and EMG
- To develop the control of an external device based on body signal coming from different sources
- to develop data analysis tools for behavioral data
- to improve the knowledge of Matlab/Simulink, C Sharp, autoencoders networks and statistical analysis
- to work (in an international team) with people with different backgrounds and with people with disability