Embedded system for upper-limb exoskeleton based on electromyography control

A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle

Arquitectura de un sistema de medición de bioparámetros integrando señales inerciales-magnéticas y electromiográficas

Este trabajo presenta una arquitectura para la medición e integración de bioparámetros basado en unidades de procesamiento de movimiento inercial-magnético (MPUs) y electromiografía (EMG). Derivado de la arquitectura propuesta, se logró desarrollar un dispositivo llamado Imocap, el cual reúne y utiliza las mejores características de la tecnología MPU + EMG para realizar una medición completa en el segmento de brazo y antebrazo en el cuerpo humano. Se presenta en primer lugar la revisión bibliográfica de los métodos y herramientas para la captura del movimiento biomecánico, seguido de las técnicas y aplicaciones de la recolección de bioparámetros. Finalmente, se muestra la arquitectura y la descripción del sistema Imocap, algunas aplicaciones y discusión. Como trabajo futuro, Imocap tiene como objetivo proporcionar la información necesaria en un sistema de control electrónico para una plataforma de rehabilitación basada en exoesqueletos robóticos

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection

IntroductionThe time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles.MethodsIn this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient.ResultsThe detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints.DiscussionThis study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction

Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well

A Bamboo-inspired Exoskeleton (BiEXO) Based on Carbon Fiber for Shoulder and Elbow Joints

This paper presents a novel cable-driven exoskeleton (BiEXO) for the upper limb including shoulder and elbow joints. BiEXO is made of carbon fiber that is inspired by the Bamboo structure. The key components of BiEXO are carbon fiber tubes that mimic bamboo tubes. A combined driver is developed for BiEXO with two cable-driven mechanisms (CDMs) and a power transmission belt (PTB). The CDMs are used for shoulder and elbow flexion/extension movement utilizing cables to mimic the skeletal muscles function, while the PTB system drives a shoulder link to mimic the scapula joint for shoulder abduction/adduction movement. Simulation studies and evaluation experiments were performed to demonstrate the efficacy of the overall system. To determine the strength-to-weight of the bamboo-inspired links and guarantee high buckling strength in the face of loads imposed from the user side to the structure, finite element analysis (FEA) was performed. The results show that the carbon fiber link inspired by bamboo has more strength in comparison to the common long carbon fiber tube. The kinematic configuration was modeled by the modified Denavit-Hartenberg (D-H) notation. The mean absolute error (MAE) was 5.9 mm, and the root-mean-square error (RMSE) was 6 mm. In addition, verification experiments by tracking the trajectory in Cartesian space and the wear trials on a subject were carried out on the BiEXO prototype. The satisfactory results indicate BiEXO to be a promising system for rehabilitation or assistance in the future.</p

Real-Time Control of a Multi-Degree-of-Freedom Mirror Myoelectric Interface During Functional Task Training

Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.This study was funded by the Eurostars Project E! 113928 Subliminal Home Rehab (SHR), BMBF (Bundesministerium für Bildung und Forschung) (FKZ: SHR 01QE2023; and REHOME 16SV8606), Fortüne-Program of the University of Tübingen (2452-0-0/1), Ministry of Science of the Basque Country (Elkartek: MODULA KK-2019/00018) and H2020- FETPROACT-EIC-2018-2020 (MAIA 951910)

Eyes-free tongue gesture and tongue joystick control of a five DOF upper-limb exoskeleton for severely disabled individuals

Spinal cord injury can leave the affected individual severely disabled with a low level of independence and quality of life. Assistive upper-limb exoskeletons are one of the solutions that can enable an individual with tetraplegia (paralysis in both arms and legs) to perform simple activities of daily living by mobilizing the arm. Providing an efficient user interface that can provide full continuous control of such a device—safely and intuitively—with multiple degrees of freedom (DOFs) still remains a challenge. In this study, a control interface for an assistive upper-limb exoskeleton with five DOFs based on an intraoral tongue-computer interface (ITCI) for individuals with tetraplegia was proposed. Furthermore, we evaluated eyes-free use of the ITCI for the first time and compared two tongue-operated control methods, one based on tongue gestures and the other based on dynamic virtual buttons and a joystick-like control. Ten able-bodied participants tongue controlled the exoskeleton for a drinking task with and without visual feedback on a screen in three experimental sessions. As a baseline, the participants performed the drinking task with a standard gamepad. The results showed that it was possible to control the exoskeleton with the tongue even without visual feedback and to perform the drinking task at 65.1% of the speed of the gamepad. In a clinical case study, an individual with tetraplegia further succeeded to fully control the exoskeleton and perform the drinking task only 5.6% slower than the able-bodied group. This study demonstrated the first single-modal control interface that can enable individuals with complete tetraplegia to fully and continuously control a five-DOF upper limb exoskeleton and perform a drinking task after only 2 h of training. The interface was used both with and without visual feedback

A Bamboo-inspired Exoskeleton (BiEXO) Based on Carbon Fiber for Shoulder and Elbow Joints

This paper presents a novel cable-driven exoskeleton (BiEXO) for the upper limb including shoulder and elbow joints. BiEXO is made of carbon fiber that is inspired by the Bamboo structure. The key components of BiEXO are carbon fiber tubes that mimic bamboo tubes. A combined driver is developed for BiEXO with two cable-driven mechanisms (CDMs) and a power transmission belt (PTB). The CDMs are used for shoulder and elbow flexion/extension movement utilizing cables to mimic the skeletal muscles function, while the PTB system drives a shoulder link to mimic the scapula joint for shoulder abduction/adduction movement. Simulation studies and evaluation experiments were performed to demonstrate the efficacy of the overall system. To determine the strength-to-weight of the bamboo-inspired links and guarantee high buckling strength in the face of loads imposed from the user side to the structure, finite element analysis (FEA) was performed. The results show that the carbon fiber link inspired by bamboo has more strength in comparison to the common long carbon fiber tube. The kinematic configuration was modeled by the modified Denavit-Hartenberg (D-H) notation. The mean absolute error (MAE) was 5.9 mm, and the root-mean-square error (RMSE) was 6 mm. In addition, verification experiments by tracking the trajectory in Cartesian space and the wear trials on a subject were carried out on the BiEXO prototype. The satisfactory results indicate BiEXO to be a promising system for rehabilitation or assistance in the future.</p

IISE Trans Occup Ergon Hum Factors

Background:In the literature, efficacy of passive upper limb exoskeletons has been demonstrated in reduced activity of involved muscles during overhead occupational tasks. However, there are fewer studies that have investigated the efficacy of active upper limb exoskeletons or compared them with their passive counterparts.Purpose:We aimed to use an approach simulating human-exoskeleton interactions to compare several passive and active assistance methods in an upper limb exoskeleton and to evaluate how different assistance types affect musculoskeletal loadings during overhead lifting.Methods:An upper-extremity musculoskeletal model was integrated with a five degree-of-freedom exoskeleton for virtual human-in-the-loop evaluation of exoskeleton design and control. Different assistance methods were evaluated, including spring-based activation zones and active control based on EMG, to examine their biomechanical effects on musculoskeletal loadings including interaction forces and moments, muscle activations, and joint moments and reaction forces.Results:Our modeling and simulation results suggest the effectiveness of the proposed passive and active assistance methods in reducing biomechanical loadings\u2014the upper-limb exoskeletons could reduce maximum loading on the shoulder joint by up to 46% compared to the no-exoskeleton situation. Active assistance was found to outperform the passive assistance approach. Specifically, EMG-based active assistance could assist over the whole lifting range and had a larger capability to reduce deltoid muscle activation and shoulder joint reaction force.Conclusions:We used a modeling and simulation approach to virtually evaluate various exoskeleton assistance methods without testing multiple physical prototypes and to investigate the effects of these methods on musculoskeletal loadings that cannot be measured directly or noninvasively. Our findings offer new approaches for testing methods and improving exoskeleton designs with \u201csmart\u201d controls. More research is planned to further optimize the exoskeleton control strategies and validate the simulated results in a real-life implementation.CC999999/ImCDC/Intramural CDC HHSUnited States/2022-01-27T00:00:00Z34254566PMC878993412107vault:4075