A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.0130.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%11.58%±1.44%

Human-robot cooperation for robust surface treatment using non-conventional sliding mode control

[EN] This work presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, deburring, etc. The method considers two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The proposed scheme is based on task priority and adaptive non-conventional sliding mode control. The applicability of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer cobot.This work was supported in part by the Spanish Government under the project DPI2017-87656-C2-1-R and the Generalitat Valenciana under Grants VALi + d APOSTD/2016/044 and APOSTD/2017/055.Solanes Galbis, JE.; Gracia Calandin, LI.; Muñoz-Benavent, P.; Valls Miro, J.; Girbés, V.; Tornero Montserrat, J. (2018). Human-robot cooperation for robust surface treatment using non-conventional sliding mode control. ISA Transactions. 80(1):528-541. https://doi.org/10.1016/j.isatra.2018.05.013S52854180

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017–2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed

Estimation of Torque Based on EMG using ANFIS

© 2017 The Authors. There are wide verities of possible human movements that involve a range from the gait for the lifting of a load by a factory worker to the performance of a superior athlete. Output of the movement can be described by a large number of kinematic variables like knee joint angle, torque. This paper proposes a system that contains a non-parametric model with EMG signal of two muscles is used as input to estimate torque. The mapping of EMG to any joint dynamics is very subject dependent. It also depends on walking, running, jumping or climbing. Each type of posture consists of combination of isometric, eccentric and concentric type of muscle contraction with different intensity level depending on velocity, angle and lifted weight (muscle activation level). To capture the EMG signal pattern which is complex and so dynamic in time and space, an adaptive feature in computational intelligence is desired which will not only learn but also make decision based on EMG channel signal pattern to estimate torque. The EMG signal has been collected from volunteer who has completed the knee joint extension with maximum voluntary contraction (MVC) at different degree/sec ranging from 5deg/Sec to 360deg/Sec. The volunteer was also asked to perform extension with moderate and low effort against different impedance like 5deg/Sec, 20deg/Sec, and 45deg/Sec. RMS feature along with 2nd order digital filter has been used to smooth the raw EMG signal. The proposed study is intended to explore an ANFIS like Neuro-Fuzzy type knowledge based adaptive network with embedded RBF kernel neuron to estimate torque

Human-robot cooperation for robust surface treatment using non-conventional sliding mode control

© 2018 ISA This work presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, deburring, etc. The method considers two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The proposed scheme is based on task priority and adaptive non-conventional sliding mode control. The applicability of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer cobot

Robotic neurorehabilitation: a computational motor learning perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols