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Adaptive variable impedance applied to robotic rehabilitation of walking

Grant number: 15/50376-2
Support type:Regular Research Grants
Duration: March 01, 2016 - October 31, 2017
Field of knowledge:Engineering - Mechanical Engineering
Cooperation agreement: MIT
Principal researcher:Adriano Almeida Gonçalves Siqueira
Grantee:Adriano Almeida Gonçalves Siqueira
Principal researcher abroad: Hermano Igo Krebs
Institution abroad: Massachusetts Institute of Technology (MIT), United States
Home Institution: Escola de Engenharia de São Carlos (EESC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The main objective is to develop an adaptive strategy based on variable impedance for walking rehabilitation of post-stroke patients. The proposed strategy will be evaluated in two different robotic devices. The first device is the lower limb exoskeleton Exo-Kanguera, designed and built by the proposer in previous projects. The device has three degrees of freedom, acting at hip, knee, and ankle joints using series elastic actuators. The second device, named Anklebot, has active actuation in two ankle joints (dorsi/plantar flexion and inversion/version) by controlling the mechanical impedance of the interface between the device and the user. The proposed adaptive strategy considers the variation of the mechanical impedance of the robots joints based on the performance and participation of the patient during the therapy session, according to the four sub-systems: Robust Position Estimation, Impedance Control, Patient/Exoskeleton Interaction Mode, and Adaptive Impedance Strategy. The proposal is mainly related to the sub-systems Patient/Exoskeleton Interaction Model and Adaptive Impedance Strategy. (AU)

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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)
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.
PEREZ-IBARRA, JUAN C.; SIQUEIRA, ADRIANO A. G.; KREBS, HERMANO I. Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v. 28, n. 12, p. 2933-2943, DEC 2020. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.