Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Influence of the robotic exoskeleton Lokomat on the control of human gait : an electromyographic and kinematic analysis

Nowadays there is an increasing percentage of elderly people and it is expected that this percentage will continue increasing, carrying huge organizational costs in rehabilitation services. Recent robotic devices for gait training are more and more regarded as alternatives to solve cost-efficiency issues and provide novel approaches for training. Nevertheless, there is a need to address how to target muscular activation and kinematic patterns for optimal recovery after a neurological damage. The main objective of this work was to understand the underlying principles that the human nervous system employs to synchronize muscular activity during walking assisted by Lokomat. A basic low-dimensional locomotor program can explain the synergistic activation of muscles during assisted gait. As a main contribution, we generated a detailed description of the electro myographic and biomechanical response to variations in robotic assistance in intact humans, which can be used for future control strategies to be implemented in motor rehabilitation

Stiffness and position control of a prosthetic wrist by means of an EMG interface

In this paper, we present a novel approach for decoding electromyographic signals from an amputee and for interfacing them with a prosthetic wrist. The model for the interface makes use of electromyographic signals from electrodes placed in agonistic and antagonistic sides of the forearm. The model decodes these signals in order to control both the position and the stiffness of the wrist

Innovative gait robot for the repetitive practice of floor walking and stair climbing up and down in stroke patients

<p>Abstract</p> <p>Background</p> <p>Stair climbing up and down is an essential part of everyday's mobility. To enable wheelchair-dependent patients the repetitive practice of this task, a novel gait robot, G-EO-Systems (EO, Lat: I walk), based on the end-effector principle, has been designed. The trajectories of the foot plates are freely programmable enabling not only the practice of simulated floor walking but also stair climbing up and down. The article intended to compare lower limb muscle activation patterns of hemiparetic subjects during real floor walking and stairs climbing up, and during the corresponding simulated conditions on the machine, and secondly to demonstrate gait improvement on single case after training on the machine.</p> <p>Methods</p> <p>The muscle activation pattern of seven lower limb muscles of six hemiparetic patients during free and simulated walking on the floor and stair climbing was measured via dynamic electromyography. A non-ambulatory, sub-acute stroke patient additionally trained on the G-EO-Systems every workday for five weeks.</p> <p>Results</p> <p>The muscle activation patterns were comparable during the real and simulated conditions, both on the floor and during stair climbing up. Minor differences, concerning the real and simulated floor walking conditions, were a delayed (prolonged) onset (duration) of the thigh muscle activation on the machine across all subjects. Concerning stair climbing conditions, the shank muscle activation was more phasic and timely correct in selected patients on the device. The severely affected subject regained walking and stair climbing ability.</p> <p>Conclusions</p> <p>The G-EO-Systems is an interesting new option in gait rehabilitation after stroke. The lower limb muscle activation patterns were comparable, a training thus feasible, and the positive case report warrants further clinical studies.</p

Surface Electromyography Applied to Gait Analysis: How to Improve Its Impact in Clinics?

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%

Concept of an exoskeleton for industrial applications with modulated impedance based on Electromyographic signal recorded from the operator

The introduction of an active exoskeleton that enhances the operator power in the manufacturing field was demonstrated in literature to lead to beneficial effects in terms of reducing fatiguing and the occurrence of musculo-skeletal diseases. However, a large number of manufacturing operations would not benefit from power increases because it rather requires the modulation of the operator stiffness. However, in literature, considerably less attention was given to those robotic devices that regulate their stiffness based on the operator stiffness, even if their introduction in the line would aid the operator during different manipulations respect with the exoskeletons with variable power. In this thesis the description of the command logic of an exoskeleton for manufacturing applications, whose stiffness is modulated based on the operator stiffness, is described. Since the operator stiffness cannot be mechanically measured without deflecting the limb, an estimation based on the superficial Electromyographic signal is required. A model composed of 1 joint and 2 antagonist muscles was developed to approximate the elbow and the wrist joints. Each muscle was approximated as the Hill model and the analysis of the joint stiffness, at different joint angle and muscle activations, was performed. The same Hill muscle model was then implemented in a 2 joint and 6 muscles (2J6M) model which approximated the elbow-shoulder system. Since the estimation of the exerted stiffness with a 2J6M model would be quite onerous in terms of processing time, the estimation of the operator end-point stiffness in realtime would therefore be questionable. Then, a linear relation between the end-point stiffness and the component of muscle activation that does not generate any end-point force, is proposed. Once the stiffness the operator exerts was estimated, three command logics that identifies the stiffness the exoskeleton is required to exert are proposed. These proposed command logics are: Proportional, Integral 1 s, and Integral 2 s. The stiffening exerted by a device in which a Proportional logic is implemented is proportional, sample by sample, to the estimated stiffness exerted by the operator. The stiffening exerted by the exoskeleton in which an Integral logic is implemented is proportional to the stiffness exerted by the operator, averaged along the previous 1 second (Integral 1 s) or 2 seconds (Integral 2 s). The most effective command logic, among the proposed ones, was identified with empirical tests conducted on subjects using a wrist haptic device (the Hi5, developed by the Bioengineering group of the Imperial College of London). The experimental protocol consisted in a wrist flexion/extension tracking task with an external perturbation, alternated with isometric force exertion for the estimation of the occurrence of the fatigue. The fatigue perceived by the subject, the tracking error, defined as the RMS of the difference between wrist and target angles, and the energy consumption, defined as the sum of the squared signals recorded from two antagonist muscles, indicated the Integral 1 s logic to be the most effective for controlling the exoskeleton. A logistic relation between the stiffness exerted by the subject and the stiffness exerted by the robotic devices was selected, because it assured a smooth transition between the maximum and the minimum stiffness the device is required to exert. However, the logistic relation parameters are subject-specific, therefore an experimental estimation is required. An example was provided. Finally, the literature about variable stiffness actuators was analyzed to identify the most suitable device for exoskeleton stiffness modulation. This actuator is intended to be integrated on an existing exoskeleton that already enhances the operator power based on the operator Electromyographic signal. The identified variable stiffness actuator is the DLR FSJ, which controls its stiffness modulating the preload of a single spring

EMG-based motion intention recognition for controlling a powered knee orthosis

Powered assistive devices have been playing a major role in gait rehabilitation. This work aims to develop a user-oriented assistive strategy with an EMG-based control using a powered knee orthosis (PKO) to provide assistive commands according to the user's motion intention tracked by electromyography (EMG) signals. To achieve this goal, the work first comprised the development of a wired EMG acquisition system, the study and implementation of a knee joint torque estimation method, and the development of a real-time controller, which uses the estimated torque as the reference actuator's torque to provide user-oriented assistance in walking. We used a proportional gain method to estimate the knee torque, which required a calibration procedure, allowing to determine the relation between the EMG signal and the actuator's torque. The EMG-based control was validated with two subjects walking in a treadmill. The EMG-based control performed as expected since it proved to be functional and time-effective when assisting the user's movements in walking at different walking speeds. Findings show that the developed assistive strategy can effectively follow the user's motion intention and has the potential for gait rehabilitation of patients with residual muscular strength.This work has been supported in part by the Fundacao para a Ciencia e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, the reference project UID/EEA/04436/2019, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference Project POCI-01-0145-FEDER-006941; and the LIACC Project UID/CEC/00027/2019; and with national funds from FCT project SmartOs-under Grant NORTE-01-0145-FEDER-030386