Human-in-the-Loop Trajectory Optimization Based on sEMG Biofeedback for Lower-Limb Exoskeleton.

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human-machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified.

Research on the Motion Control Strategy of a Lower-Limb Exoskeleton Rehabilitation Robot Using the Twin Delayed Deep Deterministic Policy Gradient Algorithm.

The motion control system of a lower-limb exoskeleton rehabilitation robot (LLERR) is designed to assist patients in lower-limb rehabilitation exercises. This research designed a motion controller for an LLERR-based on the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to control the lower-limb exoskeleton for gait training in a staircase environment. Commencing with the establishment of a mathematical model of the LLERR, the dynamics during its movement are systematically described. The TD3 algorithm is employed to plan the motion trajectory of the LLERR's right-foot sole, and the target motion curve of the hip (knee) joint is deduced inversely to ensure adherence to human physiological principles during motion execution. The control strategy of the TD3 algorithm ensures that the movement of each joint of the LLERR is consistent with the target motion trajectory. The experimental results indicate that the trajectory tracking errors of the hip (knee) joints are all within 5°, confirming that the LLERR successfully assists patient in completing lower-limb rehabilitation training in a staircase environment. The primary contribution of this study is to propose a non-linear control strategy tailored for the staircase environment, enabling the planning and control of the lower-limb joint motions facilitated by the LLERR.

Design and Control of a Single-Leg Exoskeleton with Gravity Compensation for Children with Unilateral Cerebral Palsy.

Children with cerebral palsy (CP) experience reduced quality of life due to limited mobility and independence. Recent studies have shown that lower-limb exoskeletons (LLEs) have significant potential to improve the walking ability of children with CP. However, the number of prototyped LLEs for children with CP is very limited, while no single-leg exoskeleton (SLE) has been developed specifically for children with CP. This study aims to fill this gap by designing the first size-adjustable SLE for children with CP aged 8 to 12, covering Gross Motor Function Classification System (GMFCS) levels I to IV. The exoskeleton incorporates three active joints at the hip, knee, and ankle, actuated by brushless DC motors and harmonic drive gears. Individuals with CP have higher metabolic consumption than their typically developed (TD) peers, with gravity being a significant contributing factor. To address this, the study designed a model-based gravity-compensator impedance controller for the SLE. A dynamic model of user and exoskeleton interaction based on the Euler-Lagrange formulation and following Denavit-Hartenberg rules was derived and validated in Simscape™ and Simulink® with remarkable precision. Additionally, a novel systematic simplification method was developed to facilitate dynamic modelling. The simulation results demonstrate that the controlled SLE can improve the walking functionality of children with CP, enabling them to follow predefined target trajectories with high accuracy.

Estimation of the Continuous Walking Angle of Knee and Ankle (Talocrural Joint, Subtalar Joint) of a Lower-Limb Exoskeleton Robot Using a Neural Network.

A lower-limb exoskeleton robot identifies the wearer's walking intention and assists the walking movement through mechanical force; thus, it is important to be able to identify the wearer's movement in real-time. Measurement of the angle of the knee and ankle can be difficult in the case of patients who cannot move the lower-limb joint properly. Therefore, in this study, the knee angle as well as the angles of the talocrural and subtalar joints of the ankle were estimated during walking by applying the neural network to two inertial measurement unit (IMU) sensors attached to the thigh and shank. First, for angle estimation, the gyroscope and accelerometer data of the IMU sensor were obtained while walking at a treadmill speed of 1 to 2.5 km/h while wearing an exoskeleton robot. The weights according to each walking speed were calculated using a neural network algorithm programmed in MATLAB software. Second, an appropriate weight was selected according to the walking speed through the IMU data, and the knee angle and the angles of the talocrural and subtalar joints of the ankle were estimated in real-time during walking through a feedforward neural network using the IMU data received in real-time. We confirmed that the angle estimation error was accurately estimated as 1.69° ± 1.43 (mean absolute error (MAE) ± standard deviation (SD)) for the knee joint, 1.29° ± 1.01 for the talocrural joint, and 0.82° ± 0.69 for the subtalar joint. Therefore, the proposed algorithm has potential for gait rehabilitation as it addresses the difficulty of estimating angles of lower extremity patients using torque and EMG sensors.

Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton.

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

Compliant lower limb exoskeletons: a comprehensive review on mechanical design principles.

Exoskeleton technology has made significant advances during the last decade, resulting in a considerable variety of solutions for gait assistance and rehabilitation. The mechanical design of these devices is a crucial aspect that affects the efficiency and effectiveness of their interaction with the user. Recent developments have pointed towards compliant mechanisms and structures, due to their promising potential in terms of adaptability, safety, efficiency, and comfort. However, there still remain challenges to be solved before compliant lower limb exoskeletons can be deployed in real scenarios. In this review, we analysed 52 lower limb wearable exoskeletons, focusing on three main aspects of compliance: actuation, structure, and interface attachment components. We highlighted the drawbacks and advantages of the different solutions, and suggested a number of promising research lines. We also created and made available a set of data sheets that contain the technical characteristics of the reviewed devices, with the aim of providing researchers and end-users with an updated overview on the existing solutions.

Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure.

While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments.

[Study on the influence of wearable lower limb exoskeleton on gait characteristics].

The purpose of this paper was to investigate the effects of wearable lower limb exoskeletons on the kinematics and kinetic parameters of the lower extremity joints and muscles during normal walking, aiming to provide scientific basis for optimizing its structural design and improving its system performance. We collected the walking data of subjects without lower limb exoskeleton and selected the joint angles in sagittal plane of human lower limbs as driving data for lower limb exoskeleton simulation analysis. Anybody (the human biomechanical analysis software) was used to establish the human body model (the human body model without lower limb exoskeleton) and the man-machine system model (the lower limb exoskeleton model). The kinematics parameters (joint force and joint moment) and muscle parameters (muscle strength, muscle activation, muscle contraction velocity and muscle length) under two situations were compared. The experimental result shows that walking gait after wearing the lower limb exoskeleton meets the normal gait, but there would be an occasional and sudden increase in muscle strength. The max activation level of main lower limb muscles were all not exceeding 1, in another word the muscles did not appear fatigue and injury. The highest increase activation level occurred in rectus femoris (0.456), and the lowest increase activation level occurred in semitendinosus (0.013), which means the lower limb exoskeletons could lead to the fatigue and injury of semitendinosus. The results of this study illustrate that to avoid the phenomenon of sudden increase of individual muscle force, the consistency between the length of body segment and the length of exoskeleton rod should be considered in the design of lower limb exoskeleton extremity.

EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.

Electroencephalography (EEG) signals have great impact on the development of assistive rehabilitation devices. These signals are used as a popular tool to investigate the functions and the behavior of the human motion in recent research. The study of EEG-based control of assistive devices is still in early stages. Although the EEG-based control of assistive devices has attracted a considerable level of attention over the last few years, few studies have been carried out to systematically review these studies, as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used for assistive technology. Therefore, this research has three main goals. The first aim is to systematically gather, summarize, evaluate and synthesize information regarding the accuracy and the value of previous research published in the literature between 2011 and 2018. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control for assistive rehabilitation devices to all the experts and scientists. The third goal is to recognize the gap of knowledge that demands further investigation and to recommend directions for future research in this area.

A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking.

BACKGROUND: Carrying load alters normal walking, imposes additional stress to the musculoskeletal system, and results in an increase in energy consumption and a consequent earlier onset of fatigue. This phenomenon is largely due to increased work requirements in lower extremity joints, in turn requiring higher muscle activation. The aim of this work was to assess the biomechanical and physiological effects of a multi-joint soft exosuit that applies assistive torques to the biological hip and ankle joints during loaded walking. METHODS: The exosuit was evaluated under three conditions: powered (EXO_ON), unpowered (EXO_OFF) and unpowered removing the equivalent mass of the device (EXO_OFF_EMR). Seven participants walked on an instrumented split-belt treadmill and carried a load equivalent to 30 % their body mass. We assessed their metabolic cost of walking, kinetics, kinematics, and lower limb muscle activation using a portable gas analysis system, motion capture system, and surface electromyography. RESULTS: Our results showed that the exosuit could deliver controlled forces to a wearer. Net metabolic power in the EXO_ON condition (7.5 ± 0.6 W kg(-1)) was 7.3 ± 5.0 % and 14.2 ± 6.1 % lower than in the EXO_OFF_EMR condition (7.9 ± 0.8 W kg(-1); p = 0.027) and in the EXO_OFF condition (8.5 ± 0.9 W kg(-1); p = 0.005), respectively. The exosuit also reduced the total joint positive biological work (sum of hip, knee and ankle) when comparing the EXO_ON condition (1.06 ± 0.16 J kg(-1)) with respect to the EXO_OFF condition (1.28 ± 0.26 J kg(-1); p = 0.020) and to the EXO_OFF_EMR condition (1.22 ± 0.21 J kg(-1); p = 0.007). CONCLUSIONS: The results of the present work demonstrate for the first time that a soft wearable robot can improve walking economy. These findings pave the way for future assistive devices that may enhance or restore gait in other applications.

Accelerometry-enabled measurement of walking performance with a robotic exoskeleton: a pilot study.

BACKGROUND: Clinical scores for evaluating walking skills with lower limb exoskeletons are often based on a single variable, such as distance walked or speed, even in cases where a host of features are measured. We investigated how to combine multiple features such that the resulting score has high discriminatory power, in particular with few patients. A new score is introduced that allows quantifying the walking ability of patients with spinal cord injury when using a powered exoskeleton. METHODS: Four spinal cord injury patients were trained to walk over ground with the ReWalk™ exoskeleton. Body accelerations during use of the device were recorded by a wearable accelerometer and 4 features to evaluate walking skills were computed. The new score is the Gaussian naïve Bayes surprise, which evaluates patients relative to the features' distribution measured in 7 expert users of the ReWalk™. We compared our score based on all the features with a standard outcome measure, which is based on number of steps only. RESULTS: All 4 patients improved over the course of training, as their scores trended towards the expert users' scores. The combined score (Gaussian naïve surprise) was considerably more discriminative than the one using only walked distance (steps). At the end of training, 3 out of 4 patients were significantly different from the experts, according to the combined score (p < .001, Wilcoxon Signed-Rank Test). In contrast, all but one patient were scored as experts when number of steps was the only feature. CONCLUSION: Integrating multiple features could provide a more robust metric to measure patients' skills while they learn to walk with a robotic exoskeleton. Testing this approach with other features and more subjects remains as future work.

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors.

Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher's linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton.

Development of an individualized stable and force-reducing lower-limb exoskeleton.

In this study, an individualized and stable passive-control lower-limb exoskeleton robot was developed. Users' joint angles and the center of pressure (CoP) of one of their soles were input into a convolutional neural network (CNN)-long short-term memory (LSTM) model to evaluate and adjust the exoskeleton control scheme. The CNN-LSTM model predicted the fitness of the control scheme and output the results to the exoskeleton robot, which modified its control parameters accordingly to enhance walking stability. The sole's CoP had similar trends during normal walking and passive walking with the developed exoskeleton; they-coordinates of the CoPs with and without the exoskeleton had a correlation of 91%. Moreover, electromyography signals from the rectus femoris muscle revealed that it exerted 40% less force when walking with a stable stride length in the developed system than when walking with an unstable stride length. Therefore, the developed lower-limb exoskeleton can be used to assist users in achieving balanced and stable walking with reduced force application. In the future, this exoskeleton can be used by patients with stroke and lower-limb weakness to achieve stable walking.

Overground Walking With a Transparent Exoskeleton Shows Changes in Spatiotemporal Gait Parameters.

Lower-limb gait training (GT) exoskeletons have been successfully used in rehabilitation programs to overcome the burden of locomotor impairment. However, providing suitable net interaction torques to assist patient movements is still a challenge. Previous transparent operation approaches have been tested in treadmill-based GT exoskeletons to improve user-robot interaction. However, it is not yet clear how a transparent lower-limb GT system affects user's gait kinematics during overground walking, which unlike treadmill-based systems, requires active participation of the subjects to maintain stability. In this study, we implemented a transparent operation strategy on the ExoRoboWalker, an overground GT exoskeleton, to investigate its effect on the user's gait. The approach employs a feedback zero-torque controller with feedforward compensation for the exoskeleton's dynamics and actuators' impedance. We analyzed the data of five healthy subjects walking overground with the exoskeleton in transparent mode (ExoTransp) and non-transparent mode (ExoOff) and walking without exoskeleton (NoExo). The transparent controller reduced the user-robot interaction torque and improved the user's gait kinematics relative to ExoOff. No significant difference in stride length is observed between ExoTransp and NoExo (p = 0.129). However, the subjects showed a significant difference in cadence between ExoTransp (50.9± 1.1 steps/min) and NoExo (93.7 ± 8.7 steps/min) (p = 0.015), but not between ExoTransp and ExoOff (p = 0.644). Results suggest that subjects wearing the exoskeleton adjust their gait as in an attention-demanding task changing the spatiotemporal gait characteristics likely to improve gait balance.

Review of Vision-Based Environmental Perception for Lower-Limb Exoskeleton Robots.

The exoskeleton robot is a wearable electromechanical device inspired by animal exoskeletons. It combines technologies such as sensing, control, information, and mobile computing, enhancing human physical abilities and assisting in rehabilitation training. In recent years, with the development of visual sensors and deep learning, the environmental perception of exoskeletons has drawn widespread attention in the industry. Environmental perception can provide exoskeletons with a certain level of autonomous perception and decision-making ability, enhance their stability and safety in complex environments, and improve the human-machine-environment interaction loop. This paper provides a review of environmental perception and its related technologies of lower-limb exoskeleton robots. First, we briefly introduce the visual sensors and control system. Second, we analyze and summarize the key technologies of environmental perception, including related datasets, detection of critical terrains, and environment-oriented adaptive gait planning. Finally, we analyze the current factors limiting the development of exoskeleton environmental perception and propose future directions.

Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization.

Background: Efficient gait assistance by augmentative exoskeletons depends on reliable control strategies. While numerous control methods and their effects on the metabolic cost of walking have been explored in the literature, the use of different exoskeletons and dissimilar protocols limit direct comparisons. In this article, we present and compare two controllers for hip exoskeletons with different synchronization paradigms. Methods: The implicit-synchronization-based approach, termed the Simple Reflex Controller (SRC), determines the assistance as a function of the relative loading of the feet, resulting in an emerging torque profile continuously assisting extension during stance and flexion during swing. On the other hand, the Hip-Phase-based Torque profile controller (HPT) uses explicit synchronization and estimates the gait cycle percentage based on the hip angle, applying a predefined torque profile consisting of two shorter bursts of assistance during stance and swing. We tested the controllers with 23 naïve healthy participants walking on a treadmill at 4 km ⋅ h-1, without any substantial familiarization. Results: Both controllers significantly reduced the metabolic rate compared to walking with the exoskeleton in passive mode, by 18.0% (SRC, p < 0.001) and 11.6% (HPT, p < 0.001). However, only the SRC led to a significant reduction compared to walking without the exoskeleton (8.8%, p = 0.004). The SRC also provided more mechanical power and led to bigger changes in the hip joint kinematics and walking cadence. Our analysis of mechanical powers based on a whole-body analysis suggested a reduce in ankle push-off under this controller. There was a strong correlation (Pearson's r = 0.778, p < 0.001) between the metabolic savings achieved by each participant with the two controllers. Conclusion: The extended assistance duration provided by the implicitly synchronized SRC enabled greater metabolic reductions compared to the more targeted assistance of the explicitly synchronized HPT. Despite the different assistance profiles and metabolic outcomes, the correlation between the metabolic reductions with the two controllers suggests a difference in individual responsiveness to assistance, prompting more investigations to explore the person-specific factors affecting assistance receptivity.

Lower-Limb Exoskeletons for Gait Training in Parkinson's Disease: The State of the Art and Future Perspectives.

Gait dysfunction (GD) is a common impairment of Parkinson's disease (PD), which negatively impacts patients' quality of life. Among the most recent rehabilitation technologies, a lower-limb powered exoskeleton (LLEXO) arises as a useful instrument for gait training in several neurological conditions, including PD. However, some questions relating to methods of use, achievable results, and usefulness compared to traditional rehabilitation methodologies still require clear answers. Therefore, in this review, we aim to summarise and analyse all the studies that have applied an LLEXO to train gait in PD patients. Literature research on PubMed and Scopus retrieved five articles, comprising 46 PD participants stable on medications (age: 71.7 ± 3.7 years, 24 males, Hoehn and Yahr: 2.1 ± 0.6). Compared to traditional rehabilitation, low-profile lower-limb exoskeleton (lp-LLEXO) training brought major improvements towards walking capacity and gait speed, while there are no clear major benefits regarding the dual-task gait cost index and freezing of gait symptoms. Importantly, the results suggest that lp-LLEXO training is more beneficial for patients with an intermediate-to-severe level of disease severity (Hoehn and Yahr > 2.5). This review could provide a novel framework for implementing LLEXO in clinical practise, highlighting its benefits and limitations towards gait training.

Design and validation of a pediatric gait assistance exoskeleton system with fast non-singular terminal sliding mode controller.

Existing exoskeletons for pediatric gait assistance have limitations in anthropometric design, structure weight, cost, user safety features, and adaptability to diverse users. Additionally, creating precise models for pediatric rehabilitation is difficult because the rapid anthropometric changes in children result in unknown model parameters. Furthermore, external disruptions, like unpredictable movements and involuntary muscle contractions, add complexity to the control schemes that need to be managed. To overcome these limitations, this study aims to develop an affordable stand-aided lower-limb exoskeleton specifically for pediatric subjects (8-12 years, 25-40 kg, 128-132 cm) in passive-assist mode. The authors modified a previously developed model (LLESv1) for improved rigidity, reduced mass, simplified motor arrangement, variable waist size, and enhanced mobility. A computer-aided design of the new exoskeleton system (LLESv2) is presented. The developed prototype of the exoskeleton appended with a pediatric subject (age: 12 years old, body mass: 40 kg, body height: 132 cm) is presented with real-time hardware architecture. Thereafter, an improved fast non-singular terminal sliding mode (IFNSTSM) control scheme is proposed, incorporating a double exponential reaching law for expedited error convergence and enhanced stability. The Lyapunov stability warrants the control system's performance despite uncertainties and disturbances. In contrast to fast non-singular terminal sliding mode (FNSTSM) control and time-scaling sliding mode (TSSM) control, experimental validation demonstrates the effectiveness of IFNSTSM control by a respective average of 5.39% and 42.1% in tracking desired joint trajectories with minimal and rapid finite time converging errors. Moreover, the exoskeleton with the proposed IFNSTSM control requires significantly lesser control efforts than the exoskeleton using contrast FNSTSM control. The Bland-Altman analysis indicates that although there is a minimal mean difference in variables when employing FNSTSM and IFNSTSM controllers, the latter exhibits significant performance variations as the mean of variables changes. This research contributes to affordable and effective pediatric gait assistance, improving rehabilitation outcomes and enhancing mobility support.

Gait Recognition and Assistance Parameter Prediction Determination Based on Kinematic Information Measured by Inertial Measurement Units.

The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

Actuators and transmission mechanisms in rehabilitation lower limb exoskeletons: a review.

Research has shown that rehabilitation lower limb exoskeletons (RLLEs) are effective tools for improving recovery or regaining lower limb function. This device interacts with the limbs of patients. Thus, actuators and power transmission mechanisms are the key factors in determining smooth human‒machine interaction and comfort in physical therapy activities. A multitude of distinct technologies have been proposed. However, we questioned which consideration point in actuator selection and power transmission mechanisms are used for RLLE. A review of the technical characteristics and status of advanced RLLE designs is discussed. We review actuator selection for RLLE devices. Furthermore, the power transmission mechanisms over the years within each of the RLLE devices are presented. The development issues and possible research directions related to actuators and power transmission mechanisms are provided. Most RLLEs are still in the research phase, and only a few have been commercialized. The aim of this paper is to provide researchers with useful information for investigating technological progress and highlight the latest technological choices in RLLE development.