Upper-limb Rehabilitation and Assistance
VR-Enabled Human-Robot Interaction Control for Upper-limb Rehabilitation Using Robotic Manipulators
In this project, our main goal is to develop a robotic platform for upper limb rehabilitation of post-stroke patients by incorporating a robotic manipulator and an intelligent control system. This robotic rehabilitation system will be a multipurpose and cost-effective platform while being adaptive to various therapeutic practices.
HAPTIC-3D: A Reproducible 3-DOF Haptic Robot for Upper-limb Rehabilitation
The objective of the project is the development of the Haptic3D, an interactive robot with 3 degrees of freedom (DOF), for the rehabilitation of movements of the upper limb. The haptic capabilities are added to this device through adaptive impedance/admittance-based control. The system is expandable to incorporate more sensing and actuation modalities, and its integration with VR technology.
Robot-Assisted Study of Human Motor Adaptation in Upper Limbs
In this project, we want to take a closer look at the adaptation of motor learning when an environmental disturbance occurs, particularly when the individual is subjected to a force field applied by a 6-DOF robot. We are interested to learn how the cognitive-motor processes evolve in an environment where the force field evolves.
EMG-Based Control of Assistive Manipulator Robots via Reinforcement Learning
This work uses a continuous actor-critic Reinforcement Learning method for optimizing the control of an assistive robot using myoelectric interface. The RL agent uses a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees.
Muscle-Activity Sensing and Signal Acquisition
Development of a Wearable EMG Acquisition System
In this project, a customizable EMG acquisition sleeve has been developed using four EMG sensors to detect upper-limb muscle activation, filter them and record them in real-time to be used for robotic systems.