A Simulation-based Methodology to Test and Assess Designs of Mechatronic Neural Interface Systems

Bustamante, Samuel; Yepes, Juan C.; Pérez, Vera Z.; Correa, Julio C.

doi:10.5220/0005698200780087

Cited by 1 publication

(1 citation statement)

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“…The sEMG signal processing algorithm was developed in real-time as an HIL simulation, i.e., tests were performed using pre-recorded signals. Moreover, tests were carried out as proposed in the simulation-based methodology stated by the authors in [28, 29].…”

Section: Methodsmentioning

confidence: 99%

Myoelectric control algorithm for robot-assisted therapy: a hardware-in-the-loop simulation study

Yepes¹,

Portela²,

Saldarriaga³

et al. 2019

BioMed Eng OnLine

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BackgroundA direct blow to the knee is one way to injure the anterior cruciate ligament (ACL), e.g., during a football or traffic accident. Robot-assisted therapy (RAT) rehabilitation, simulating regular walking, improves walking and balance abilities, and extensor strength after ACL reconstruction. However, there is a need to perform RAT during other phases of ACL injury rehabilitation before attempting an advanced exercise such as walking. This paper aims to propose a myoelectric control (MEC) algorithm for a robot-assisted rehabilitation system, “Nukawa”, to assist knee movement during these types of exercises, i.e., such as in active-assisted extension exercises.MethodsSurface electromyography (sEMG) signal processing algorithm was developed to detect the motion intention of the knee joint. The sEMG signal processing algorithm and the movement control algorithm, reported by the authors in a previous publication, were joined together as a hardware-in-the-loop simulation to create and test the MEC algorithm, instead of using the actual robot.Experiments and resultsAn experimental protocol was conducted with 17 healthy subjects to acquire sEMG signals and their lower limb kinematics during 12 ACL rehabilitation exercises. The proposed motion intention algorithm detected the orientation of the intention 100% of the times for the extension and flexion exercises. Also, it detected in 94% and 59% of the cases the intensity of the movement intention in a comparable way to the maximum voluntary contraction (MVC) during extension exercises and flexion exercises, respectively. The maximum position mean absolute error was , , and for the hip, knee, and ankle joints, respectively.ConclusionsThe MEC algorithm detected the intensity of the movement intention, approximately, in a comparable way to the MVC and the orientation. Moreover, it requires no prior training or additional torque sensors. Also, it controls the speed of the knee joint of Nukawa to assist the knee movement, i.e., such as in active-assisted extension exercises.

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Section: Methodsmentioning

confidence: 99%