About

The Project

FesRobex is a research project focused on developing a Hybrid Functional Electrical Stimulation (FES) and Robotic Exoskeleton system for the rehabilitation of lower limb motor functions. The system combines functional electrical stimulation with a robotic exoskeleton to help treat gait disorders, balance and coordination of movements in patients with stroke, multiple sclerosis, Parkinson’s disease, paraplegia, and other neurological and orthopaedic conditions.

Research Objectives

  • Study existing FES systems and recovery robots and compare them
  • Develop a hybrid control system that adequately manages the balance between FES and robotic controllers
  • Design and implement the mathematical model for calculating the torque at each joint for the appropriate selection of motors
  • Create a real-time sensor monitoring system for lower limb biomechanical data acquisition during gait analysis
  • Develop the central control system (Master-Slave controller) integrating all subsystems

System Components

  1. Electromyography system (EMG) — Monitoring muscle electrical activity
  2. Electrical Muscle Stimulation System (FES) — Functional electrical stimulation for muscle activation
  3. EMG & FES integrated system — Combined electromyography and stimulation control
  4. Robotic Exoskeleton — Mechanical structure with actuated joints for lower limb support
  5. Central Control System — Master-Slave controller coordinating all subsystems
  6. Communication Network — Data link between all system modules

Key Publication

Kavalieros, D.; Kapothanasis, E.; Kakarountas, A.; Loukopoulos, T. “Methodology for Selecting the Appropriate Electric Motor for Robotic Modular Systems for Lower Extremities.” Healthcare 2022, 10(10), 2054. doi.org/10.3390/healthcare10102054

Affiliation

Department of Computer Science and Biomedical Informatics
University of Thessaly, 35131 Lamia, Greece
Intelligent Systems Laboratory (iSL)

Dimitrios Kavalieros — Ph.D. Candidate & Lead Researcher
Academic Profile · GitHub · LinkedIn