Lower Extremity Exoskeleton Research Articles

User experience of lower extremity exoskeletons and its improvement methodologies: A narrative review.

In this review, user experience (UX) of recent lower limb exoskeletons (LLEs) and its improvement methodologies are investigated. First, statistics based on standardised and custom UX evaluations are presented. It is indicated that, LLE users have positive UX, especially in the aspects of safety, dimension and effectiveness. Further, overall, UX levels of ankle and hip-knee exoskeletons are higher than those of other exoskeleton types; unilateral LLEs have higher mean UX levels than that of the bilateral ones. Then, design practices for improving UX are studied; the focused points are burden reduction and improvement of device fit. The former is achieved through lightweight design and approaches that reduce device's moment of inertia (MOI) at mechanical joints. Works on the latter refer to the endeavours to enhance static and dynamic fit; they mainly rely on the optimisations of human-robot interface (HRS) and endeavours to rectify misalignment of axes of mechanical and anatomic joints, respectively. The following section is control approaches to enhance wearing comfort level; it is mainly focused on adaptive, interaction and compensation-based controls. Finally, existing problems and future directions are stated and prospected respectively.

Repetitive Control of Knee Interaction Torque via a Lower Extremity Exoskeleton for Improved Transparency During Walking

Repetitive Control of Knee Interaction Torque via a Lower Extremity Exoskeleton for Improved Transparency During Walking

Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.

Human gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user's natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG. This study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle. The results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19. This study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

Analysis and Experimental Verification of Human-Robot Coupling Collaboration Characteristics for Lower Extremity Exoskeleton Rehabilitation Robot Based on ADAMS

Analysis and Experimental Verification of Human-Robot Coupling Collaboration Characteristics for Lower Extremity Exoskeleton Rehabilitation Robot Based on ADAMS

Exercising with a robotic exoskeleton can improve memory and gait in people with Parkinson’s disease by facilitating progressive exercise intensity

People with Parkinson’s disease (PwPD) can benefit from progressive high-intensity exercise facilitated with a lower-extremity exoskeleton, but the mechanisms explaining these benefits are unknown. We explored the relationship between exercise intensity progression and memory and gait outcomes in PwPD who performed 8 weeks (2 × per week) of progressive exercise with and without a lower-extremity powered exoskeleton, as the planned exploratory endpoint analysis of an open-label, parallel, pilot randomized controlled trial. Adults 50–85 years old with a confirmed diagnosis of PD participated. Twenty-seven participants randomized to exercise with (Exo = 13) or without (Nxo = 14) the exoskeleton were included in this exploratory endpoint analysis. Detailed exercise logs were kept and actigraphy was used to measure activity count*min−1 (ACPM) during all exercise sessions. Only the Exo group were able to progressively increase their ACPM over the entire 8-week intervention, whereas the Nxo group plateaued after 4 weeks. Exercise intensity progression correlated with change in the memory sub-scale of the SCOPA-COG and change in gait endurance from the 6MWT, consistent with the prevailing hypotheses linking high-intensity interval exercise to improved muscle and brain function via angiogenic and neurotrophic mechanisms. Facilitating high-intensity exercise with advanced rehabilitation technology is warranted for improving memory and gait endurance in PwPD.Registration: ClinicalTrials.gov, NCT 03583879 (7/10/2018).

Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network

The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.

Lower Extremity Exoskeleton for Human Spinal Cord Injury: A Comprehensive Review

Lower Extremity Exoskeleton for Human Spinal Cord Injury: A Comprehensive Review

TRANSITION MOTION PATTERN CLASSIFICATION FOR LOWER LIMB EXOSKELETON IN STAIR SCENES BASED ON CNN AND GRU

Motion pattern classification is one of the important research fields in lower extremity exoskeleton robot, it refers to acquiring motion data from multiple sensors installed on the exoskeleton. We designed a wearable lower limb exoskeleton robot with multiple sensors mainly including force sensitive resistors (FSRs) inside smart shoes and encoders inside joints. The wearable robot was used to help people carry the heavy load in the scenes of ascending stairs and descending stairs. The experiments of stair walking were carried out by the subjects who wore the exoskeleton to ascend stairs and descend stairs for a designated time. Before or after the stair walking, the subject would turn to move on flat ground with the result that there existed four transition motions between the stair and flat ground walking. As known, there is less research focusing on the classification of transition motions. The aim of this paper is to classify these motion patterns through a learning algorithm. The convolutional neural network (CNN) and gated recurrent unit (GRU) framework were combined to improve the classification accuracy. Specifically, CNN was used to extract the features of the motion pattern, while GRU was used to extract the temporal correlation during walking. Experimental works showed that the proposed CNN-GRU possessed a significantly high prediction accuracy in motion pattern classification. Compared with CNN, GRU and LSTM-CNN models whose accuracy score does not exceed 93.22%, the proposed CNN-GRU gained a high accuracy of 95.51%.

Modeling and biomechanical analysis of lower extremity exoskeleton

Dış iskeletler, çeşitli nedenlerle uzuvlarını kaybeden kişilerin kas rehabilitasyonu ve/veya sosyal hayata adaptasyonu için geliştirilmiş cihazlardır Bu çalışmada, Solidworks programı kullanılarak A Glass Fiber malzemeden yapılmış bir alt ekstremite dış iskeleti tasarlanmıştır. AnyBody programında tasarım modeli kas-iskelet sistemi üzerine bindirilerek iki yürüyüş döngüsü boyunca biyomekanik analizler yapılmıştır. Bu biyomekanik analizler sonucunda kas aktiviteleri, kas kuvveti, eklem momenti ve reaksiyon kuvveti verileri elde edilmiştir. Veriler, dış iskeletli ve dış iskeletsiz olmak üzere iki farklı yürüme yapan model üzerinde toplanmış ve analiz edilmiştir. Tasarımda kullanılan A Glass Fiber malzemenin dış iskeletin ağırlığını motor ağırlığı dahil yaklaşık 8-9 kg'a kadar düşürdüğü gözlemlenmiştir. Ayrıca dış iskelet tasarımının, literatürde tasarlanan benzer dış iskeletlere göre kaslar ve eklemler üzerindeki kuvveti daha fazla azalttığı görülmüştür. Ayrıca simülasyon sonuçları, dış iskeletin femur ve tibianın S şeklindeki yapısının insan anatomik daha uygun olduğunu göstermiştir. Ayrıca yürüyüşte gövdeden bacaklara kuvvet aktarımının daha dengeli olduğu gözlemlenmiştir. Son olarak dış iskelet ile yürümenin psoas major kasını daha fazla çalıştırarak kalçanın ön-arka kuvvetini arttırdığı sonucuna varılmıştır.

Tracking Control for a Lower Extremity Exoskeleton Based on Adaptive Dynamic Programing.

The utilization of lower extremity exoskeletons has witnessed a growing presence across diverse domains such as the military, medical treatment, and rehabilitation. This paper introduces a novel design of a lower extremity exoskeleton specifically tailored for individuals engaged in heavy object carrying tasks. The exoskeleton incorporates an impressive 12 degrees of freedom (DOF), with four of them being effectively controlled through hydraulic cylinders. To achieve optimal control of this intricate lower extremity exoskeleton system, the authors propose an adaptive dynamic programming (ADP) algorithm. Several crucial components are established to implement this control scheme. These include the formulation of the state equation for the lower extremity exoskeleton system, which is well-suited for the ADP algorithm. Additionally, a corresponding performance index function based on the tracking error is devised, along with the game algebraic Riccati equation. By employing the value iteration ADP scheme, the lower extremity exoskeleton demonstrates highly effective tracking control. This research not only highlights the potential of the proposed control approach but also showcases its ability to enhance the overall performance and functionality of lower extremity exoskeletons, particularly in scenarios involving heavy object carrying. Overall, this study contributes to the advancement of lower extremity exoskeleton technology and offers valuable insights into the application of ADP algorithms for achieving precise and efficient control in demanding tasks.

Static structural analysis of lower extremity Exoskeleton for industry working Personnel using an analysis software

The problem faced by active workers, Teachers, laboratory technicians, etc is that they have to work for a long period standing and this leads to sore feet, swelling of the legs, general muscular fatigue, low back pain, stiffness in the neck, and shoulders, and other health problems. To reduce these health risks, a Lower body Exoskeleton or Assistive Exoskeleton Device for Industrial Personnel has been invented. An Assistive Exoskeleton Device for Industrial Personnel is a portable device that looks nothing like a chair and allows you to sit on it whenever and wherever you want. This flexible, ergonomic device looks more like an exoskeleton and extends from the hip to the feet, and adapts to different body sizes. With the Assistive Exoskeleton Device for Industrial Personnel, the user can walk along with the sitting support while wearing it, without obstructing the workspace, at the same time avoiding strenuous postures such as bending, squatting, or crouching. It is meant to reduce worker fatigue and work-related accidents while improving productivity. It can be customized to fit all sizes and outfits. This project aims to design and analyze the Assistive Exoskeleton Device for Industrial Personnel for different weights and positions. The Assistive Exoskeleton Device for Industrial Personnel is designed by using CREO Parametric 8.0.2.0 And analysis is completed by using ANSYS workbench 2022 R1. Based on the results of the research, a comfortable, safe-to-use Assistive Exoskeleton Device for Industrial Personnel was designed with local components. The results of this study will be the reference for further development.

A Novel BILSTM-CNN Method for Pattern Recognition in Real Time Under Triple Physical Loads in Lower Extremity Exoskeleton

Pattern recognition is of great importance in compliant control of lower extremity exoskeleton. We propose a novel method based on the BILSTM-CNN model for pattern recognition in real time under triple physical loads on different terrains. BILSTM is skilled in dealing with temporal series data while CNN is proficient in coping with spatial series data. Five patterns include level ground walking (LW), stair ascending (SA), stair descending (SD), ramp ascending (RA), and ramp descending (RD). Their accuracies in multigrade loads of 0kg, 20kg, and 40kg reach 98.81%, 98.24%, and 97.04% respectively. Moreover, compared with the universal methods of LSTM, CNN, and BP, the hybrid method has competitive advantage in evaluation indexes of accuracy, precision, recall, and F1 score. In addition to five steady patterns, eight pattern transitions are identified between two neighboring states, such as LW to SA, LW to SD, LW to RA, LW to RD, SA to LW, SD to LW, RA to LW, and RD to LW. For pattern transitions, prediction time (Pre-T) of next pattern in multilevel loads of 0kg, 20kg, and 40kg are 190-620 ms, 180-420 ms, and 50-90 ms respectively prior to the step into that pattern. Pattern transition time (Aug-In) in multilevel loads of 0kg, 20kg, and 40kg are 50-190 ms, 30-310 ms, and 0-280 ms respectively before Pre-T of next pattern. Eventually, the experimental results indicate the proposed method has excellent performance on pattern recognition and pattern transition.

Research on the Influence of Exoskeletons on Human Characteristics by Modeling and Simulation Using the AnyBody Modeling System

Lower limb-powered exoskeletons can help rehabilitate patients with lower limb disabilities. However, the changes in the biomechanical load on the human body when exoskeletons are used are still poorly understood. The goal of this study was to investigate the changes in kinematic and biomechanical parameters of the lower extremity exoskeleton when worn by normal subjects and patients with unilateral motor impairment using a virtual prototype. The effect of wearing the exoskeleton on gait was derived, and the basis for exoskeleton optimization was given. Virtual prototyping is a cost-effective method to validate the performance of exoskeleton robots. Therefore, two models, a human-exoskeleton model and an asymmetric movement disorder (SSP) subject-exoskeleton model, were developed in AnyBody software for this study. The human-exoskeleton model was driven by the kinematic data of 20 healthy participants walking in an exoskeleton at normal speed (3.6 km/h). As a comparison, the SSP subject-exoskeleton model was driven by data from five SSP subjects walking in an exoskeleton. The experimental results show that after wearing the lower limb exoskeleton, the walking gait satisfies the normal human gait characteristics, but some of the muscle forces suddenly increase. The maximum activation level does not exceed 1, which means that the exoskeleton does not cause muscle damage or fatigue in a short period of time. In both models, the vertical ground reaction force (GRF) Z correlation was the strongest (R > 0.90). The center of pressure (COP) X trajectory correlation was the weakest (R < 0.35). These findings will support the study of the effects of exoskeletal optimization. Also, some gait characteristics of exoskeletons worn by patients with unilateral dyskinesia can be initially explored.

Study on the rehabilitation training effect of exoskeletal diseases based on the skeletal muscle geometry modeling method

Abstract Plantar pressure is the main mechanical parameter to understand human lower limb movement, which can fully show the health status of human spine, foot, leg and other regions.At present, researchers have carried out empirical analysis on the rehabilitation training process of lower limbs, but due to the defects of guidance methods and too vague evaluation indicators, it is impossible to conduct comprehensive monitoring and feedback on the rehabilitation process of patients. Therefore, on the basis of the modeling method based on the understanding of skeletal muscle set, the rest of the lower limbs dyskinesia patients rehabilitation training, puts forward the plantar pressure changes as the core of human lower limb rehabilitation training monitoring and feedback methods, at the same time using autoregressive model to construct the algorithm model of lower limb exoskeleton, updating method of the weighted coefficient of contrast research. The final results show that the least mean square error (LMS) algorithm can provide effective driving force for the rehabilitation training model of lower extremity exoskeleton disease, and feedback evaluation of the whole rehabilitation training process

Optimal gait planning of bionic lower extremity exoskeleton based on elite mechanism bacterial foraging optimization algorithm

To improve the stability and smoothness of bionic lower extremity exoskeleton walking gait, the elitist mechanism was introduced to enhance the diversity and improve the search ability of the optimal solution. The simulation analyses showed that the improved bacterial foraging algorithm had higher convergence accuracy, and was not easy to fall into local extremum. The improved foraging optimization algorithm was used for the optimization of the bionic lower extremity exoskeleton gait zero moment point stability margin. The simulation results showed that the optimal gait planning method proposed can increase the stability margin and ensure the stable and smooth walking.

NBLex: emotion prediction in Kannada-English code-switch text using naïve bayes lexicon approach

<span lang="EN-US">Emotion analysis is a process of identifying the human emotions derived from the various data sources. Emotions can be expressed either in monolingual text or code-switch text. Emotion prediction can be performed through machine learning (ML), or deep learning (DL), or lexicon-based approach. ML and DL approaches are computationally expensive and require training data. Whereas, the lexicon-based approach does not require any training data and it takes very less time to predict the emotions in comparison with ML and DL. In this paper, we proposed a lexicon-based method called non-binding lower extremity exoskeleton (NBLex) to predict the emotions associated with Kannada-English code-switch text that no one has addressed till now. We applied the One-vs-Rest approach to generate the scores for lexicon and also to predict the emotions from the code-switch text. The accuracy of the proposed model NBLex (87.9%) is better than naïve bayes (NB) (85.8%) and bidirectional long short-term memory neural network (BiLSTM) (84.7%) and for true positive rate (TPR), the NBLex (50.6%) is better than NB (37.0%) and BiLSTM (42.2%). From our approach, it is observed that a simple additive model (lexicon approach) can also be an alternative model to predict the emotions in code-switch text.</span>

Parametric and Noise Effects on Magnetic Sensing System for Monitoring Human-Joint Motion of Lower Extremity in Sagittal Plane

This article presents a three-degree-of-freedom (3-DOF) magnetic sensor, referred to here as a pantographic exoskeleton (PGE) sensor, for monitoring in real time the internal human-joint motion in the sagittal plane. With two sets of embedded magnetic sensors and a permanent magnet, the PGE wearable on a healthy leg or lower extremity exoskeleton (LEE) independently measures the 2-DOF translations and the joint angle. Two sensor estimation methods, which are the model-based and the artificial neural network (ANN), are experimentally analyzed in the presence of measurement noise. As an illustration, the PGE sensors are evaluated for sit-to-stand (STS) exercises, where the real-time measurements are verified by comparing with the joint angles determined by a commercial VICON motion capture system, and the translational deviations measured on a 4-DOF platform manipulated to follow a specified internal motion trajectory of an ankle joint during STS. With the ANNs appropriately trained to account for measurement noise, the PGE sensors can track the joint angles while measuring the internal motions of both legs with or without the LEE demonstrating that the PGE sensor has the potential to serve as an indicator of stroke rehabilitation where patients lack force perception and suffer an increased risk of falls due to the weak affected leg.

Knee Flexion-Assisted Method for Human-Exoskeleton System.

The knee has gradually become an important research target for the lower extremity exoskeleton. However, the issue that whether the flexion-assisted profile based on the contractile element (CE) is effective throughout the gait is still a research gap. In this study, we first analyze the effective flexion-assisted method through the passive element's (PE) energy storage and release mechanism. Specifically, ensuring assisting within an entire joint power period and the human's active movement is a prerequisite for the CE-based flexion-assisted method. Second, we design the enhanced adaptive oscillator (EAO) to ensure the human's active movement and the integrity of the assistance profile. Third, a fundamental frequency estimation based on discrete Fourier transform (DFT) is proposed to shorten the convergence time of EAO significantly. The finite state machine (FSM) is designed to improve the stability and practicality of EAO. Finally, we demonstrate the effectiveness of the prerequisite condition for the CE-based flexion-assisted method by using electromyography (EMG) and metabolic indicators in experiments. In particular, for the knee joint, CE-based flexion assistance should be within an entire joint power period rather than just in the negative power phase. Ensuring the human's active movement will also significantly reduce the activation of antagonistic muscles. This study will aid in designing assistive methods from the perspective of natural human actuation and apply the EAO to the human-exoskeleton system.

Examining the Efficacy of Lower Extremity Exoskeletons in the Rehabilitation Process of Spinal Cord Injury Patients – Case Studies

Examining the Efficacy of Lower Extremity Exoskeletons in the Rehabilitation Process of Spinal Cord Injury Patients – Case Studies

Opportunities and challenges in the development of exoskeletons for locomotor assistance.

Exoskeletons can augment the performance of unimpaired users and restore movement in individuals with gait impairments. Knowledge of how users interact with wearable devices and of the physiology of locomotion have informed the design of rigid and soft exoskeletons that can specifically target a single joint or a single activity. In this Review, we highlight the main advances of the past two decades in exoskeleton technology and in the development of lower-extremity exoskeletons for locomotor assistance, discuss research needs for such wearable robots and the clinical requirements for exoskeleton-assisted gait rehabilitation, and outline the main clinical challenges and opportunities for exoskeleton technology.