Hybrid adaptive strategies for lower limb exoskeletons

• Grant number: 19/05937-7
• Support type: Regular Research Grants
• Duration: May 01, 2020 - April 30, 2022
• Field of knowledge: Engineering - Mechanical Engineering
• Principal researcher: Adriano Almeida Gonçalves Siqueira
• Grantee: Adriano Almeida Gonçalves Siqueira
• Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Assoc. researchers: Anselmo Frizera Neto ; Antonio Padilha Lanari Bo ; Glauco Augusto de Paula Caurin ; Marcela de Abreu Silva Couto ; Thiago Luiz de Russo ; Wilian Miranda dos Santos

Abstract

This research project proposes to develop and evaluate hybrid adaptive strategies for the robotic rehabilitation of walking, using adaptive control algorithms and new interfaces of communication and interaction between the user and the robotic devices, such as, for example, Functional Electrical Stimulation (FES) and Virtual Reality (VR). Specifically, we intend to develop an integrated system of VR and exoskeletons for lower limbs, considering walking on treadmill and over ground. This system will be used in conjunction with the systems for identification of the phases of walking and estimation of muscle fatigue to provide necessary information for the FES adaptive control system. The latter system will work in conjunction with the variable impedance control of the exoskeleton to support walking and promote motor rehabilitation. The estimation algorithms and hybrid adaptive strategies will be evaluated with healthy volunteers and with patients with sequelae due to stroke or incomplete spinal cord injury. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

NUNES, POLYANA F.; OSTEN, ICARO; SIQUEIRA, ADRIANO A. G. Evaluation of Motor Primitive-Based Adaptive Control for Lower Limb Exoskeletons. FRONTIERS IN ROBOTICS AND AI, v. 7, DEC 16 2020. Web of Science Citations: 0.

PEREZ-IBARRA, JUAN C.; SIQUEIRA, ADRIANO A. G.; KREBS, HERMANO I. Identification of Gait Events in Healthy and Parkinson's Disease Subjects Using Inertial Sensors: A Supervised Learning Approach. IEEE SENSORS JOURNAL, v. 20, n. 24, p. 14984-14993, DEC 15 2020. Web of Science Citations: 0.