Lower limb rehabilitation exoskeleton robot: A review

Abstract

Lower limb rehabilitation exoskeleton robots (LLRERs) play a positive role in lower limb rehabilitation and assistance for patients with lower limb disorders, and they are helpful to improve patients’ physical status. More and more experiments pay more attention to the kinematic and dynamic data characteristics of different patient groups. However, it is not clear whether these devices have broad adaptability and their clinical significance, so it is necessary to summarize and analyze these research results. This paper summarizes the LLRERs prototype and product in recent years, also compares the advantages and disadvantages of the theory and technology used in these research, and compares the functional characteristics of the devices, finally summarizes the aspects of the LLRERs to be improved. These devices apply advanced theories, techniques or structures, as well as human kinematics and dynamics data. However, due to the complexity of human body characteristics and movement rules, the theory or technology applied in the study design of LLRERs remains to be further studied, which can be improved in many aspects, such as improve the human-computer cooperation of equipment or carry out clinical trials. This paper can provide reference for researchers and designers in the future study, as well as understanding and selecting LLRERs for all kinds of therapist and patients.

Keywords

Lower limb rehabilitation gait rehabilitation joint rehabilitation lower limb exoskeleton rehabilitation devices

Introduction

One of the main reasons that people with lower limb dysfunction can’t autonomously complete basic activities of daily life is that their neural pathways are blocked. Medical knowledge has shown that repeated exercise reactivates the patient’s neural pathways.¹ LLRERs combines artificial intelligence and mechanical power device to help patients with lower limb movement and enhance muscle vitality,² which is a direct and effective means to compensate, replace or repair physical dysfunction, it is also one of the important ways to solve the problem of nursing shortage caused by population aging.³ LLRERs are sturdy and durable, which can enhance the self-care ability of patients. LLRERs provides the possibility for the patients with lower limb disorders to return to society.⁴ LLRERs also could reduce the burden on families and society.

In recent years, scholars have designed a variety of LLRERs, which can realize the movement and rehabilitation assistance for different joints, gait, as well as the daily life activities. The hip exoskeleton robot can improve the stability of the trunk, reduce the metabolic consumption, has a global impact on lower limb.^5,6 Shi et al.⁷ studied the torque field control for robot rehabilitation. The proposed control method can realize proper posture control of joints with three degrees of freedom or posture control of extremity end effectors. It is suitable for hip joint rehabilitation and can provide reference for controlling rehabilitation robots. The knee exoskeleton designed by Luo et al.^8,9 can provide power for the knee joint of the patient, effectively generating assistance that are voluntary and intuitively controlled by the user’s muscle activity. The ankle exoskeleton designed by McCain et al.¹⁰ could realize speed adaptive adjustment, improve the stress condition of the ankle joint, and protect the user from further injury during robot assisted movement or rehabilitation. Pan et al.¹¹ designed the LLRE system for gait rehabilitation and proposed the multi-loop modulation method (MMM; 3M), with better gait tracking response and stability, can assist the user to achieve normal gait. The rehabilitation robot designed by Singh et al.¹² can realize joint trajectory control. There are also some commercial devices that have been widely used in clinical practice. For example, LOKOMAT¹³ is a new type of robot that can simulate physiological gait trajectory, drive the patient’s unilateral or bilateral lower limbs, and accurately control the speed of the treadmill to match the patient’s gait Consistent, organically integrate functional exercise therapy with the patient’s assessment and feedback system. Ekso Bionics¹⁴ is a wearable exoskeleton that provides power to the legs. Designed to help patients stand and walk during rehabilitation, EksoNR works with clinicians to give the necessary support to the spine, trunk, and legs including hip, knee, and ankle joints promoting correct movement patterns in physical rehabilitation and challenging patients toward walking out of the EksoNR rehabilitation setting and back into their communities. EksoNR is also become the first exoskeleton cleared by the US Food and Drug Administration (FDA) for medical applications in treating patients with strokes, brain injuries, and SCI.

From these research results, it can be found that LLRERs plays a positive role in lower limb rehabilitation and assistance for patients with lower limb disorders, and is helpful to improve patients’ physical status. More and more experiments pay more attention to the kinematic and dynamic data characteristics of different patient groups. It is necessary to summarize and analyze these research results.

Materials and methods

The main purpose of this review is to summarize the state of the art of LLRERs and its control methods that are effective, advanced, and practically applicable through scholars’ research and experimental verification. According to the position of the LLRERs and the functions, it is divided into joints rehabilitation exoskeleton robots (JRERs), gait rehabilitation exoskeleton robots (GRERs), and daily life assistive exoskeleton robots (DLAERs). And summarizing the advantages and disadvantages of various robots and the applied technologies, providing a reference direction for subsequent LLRERs research, and can also help people with disabilities to understand and choose LLRERs.

We conducted an extensive search of articles related to LLERRs. The literature search was conducted from April to July 2020. The detail search results of each database are as follows:

IEEE Xplore (33 records) and Springer link (291 records) and Mendeley (35 records), by using the following groups of descriptors: “(Lower limb rehabilitation) AND (Exoskeleton) AND (Hip joint, Knee joint, and ankle joint).”

Science Direct databases (269 records), by using the following groups of descriptors: “(Rehabilitation assistive devices) AND (Rehabilitation) AND (Hip, Knee, ankle joint) AND (Exoskeleton) AND (Lower-limb Rehabilitation Robot).”

SAGE journals (97 records), by using the following groups of descriptors: “(Rehabilitation) AND (Hip, Knee, ankle joint) AND (Exoskeleton) AND (Lower-limb Rehabilitation Robot).”

PubMed (58 records), by using the following groups of descriptors: “(Exoskeleton) AND (rehabilitation) AND (lower limb joint) AND (lower-limb rehabilitation robot).”

Web of Science (136 records), by using the following groups of descriptors: “(Exoskeleton) AND (rehabilitation) AND (lower limb joint) AND (lower-limb rehabilitation robot) AND (lower limb).”

The inclusion criteria were: Original articles written in English, published until 2020, and articles related to the use of LLERRs to treat symptoms. Articles related to exoskeleton design such as lower limb joint rehabilitation and gait rehabilitation.

Data analysis: The references is divided into single-joint exoskeleton, gait rehabilitation exoskeleton, and daily life assisting robots according to the functional parts and names of the equipment designed by the author of these articles. In addition, the included articles all have structural design and experiments (design paper and test paper).

The exclusion criteria were: papers not related to the research topic, repetitive articles, articles related to walking aids for the blind and children’s rehabilitation aids, and papers other than English. On this basis, we first searched 919 papers. After reading the title and abstract and applying the inclusion and exclusion criteria, we excluded some papers related to children’s rehabilitation aids and walking aids for the blind, and finally 95 papers an overall assessment is required.

Result

Joint rehabilitation exoskeleton robots (JRERs)

Hip exoskeleton robots (HERs)

Ding et al.¹⁵ designed an IMU-based iterative controller for hip extension assistance, which can evaluate the user’s state from the aspects of biomechanics and physiological responses, so as to determine the most suitable assistance mode for hip joint extension for patients during walking. Xue et al.¹⁶ designed a new wearable hip joint exoskeleton, the flexible hip assistive exoskeleton with 4 DOF aligned with hip, which reduces the inherent mechanical impedance of the robot, realizes the compliant man-machine interaction, and realizes the walking intention estimation, provides comfortable and effective walking assistance for patients and reduce their metabolic consumption. Zhang et al.¹⁷ designed an asymmetric fully constrained parallel mechanism prototype that has three rotation degrees of freedom and three actuations, as shown in Figure 1. Novel parallel mechanism is synthesized and designed to provide assistance for the movement of hip joint. The asymmetric parallel mechanism (PM) with constrained is easy to design and the requests of machining and assembling are relatively low compared with symmetric PM. Developed a robotic hip exoskeleton called GEMS driven by motor. As shown in Figure 2, the thigh frame consists of two flexible plates that are constrained to slide on each other along the longitudinal direction. The exoskeleton can improve the gait function of users and reduce the metabolic cost of walking. The device is beneficial to the rehabilitation of patients after stroke and muscle strength of the elderly. Zhang et al.¹⁸ developed the robot hip exoskeleton named NREL Exo, as shown in Figure 3, The exoskeleton is driven by series elastic actor (SEA), which can help users actively participate in walking, and provide balance assistance for individuals with muscle weakness. The rehabilitation robot designed by Zhang et al.¹⁹ used an asymmetrical three-degree-of-freedom parallel mechanism to assist patients with hip joint stroke or trauma. As shown in Figure 4. The device using the reciprocal and closed-loop motion conditions, an improved force transmission index based on the previous method is proposed. A multi-objective optimization model is established. The device allows patients to use prone position for training, reducing physical burden. Mahdavian et al.²⁰ proposed a wearable HER based on ground reaction force and moment (GRF/M), as shown in Figure 5. It can help users to perform hip joint flexion and extension exercises and help users walk normally. Guzmán et al.²¹ proposed a control strategy based on generalized proportional integral (GPI) controller. Although patients’ weight is different, the gain of GPI controller does not need to be changed. This make HERs have higher adaptability. However, there is a common problem in the adaptability of robots that there will be a time delay between self-learning and reaction, and the delay may increase with the increase of time and reduce the safety. Improving the efficiency of adaptive adjustment should also be the focus of future research.

Figure 1.

Hip joint assistant asymmetric parallel mechanism prototype.¹⁷

Figure 2.

The evaluation system with GEMSv2 fastened.¹⁶

Figure 3.

NREL-Exo prototype.¹⁸

Figure 4.

HipBot.¹⁹

Figure 5.

Optical marker placements for motion tracking while wearing the hip exoskeleton.²⁰

The structure of the hip exoskeleton is similar, mostly worn on the hip and the root of the thigh. These HERs can assist patients with hip joint flexion/extension/abduction and adduction rehabilitation exercises. However, the degree of freedom of the human hip joint in space is multi-dimensional. Although the current degree of freedom design can achieve a good auxiliary effect, it still needs to be improved to improve the human-machine cooperation.

Knee exoskeleton robots (KERs)

The knee joint is the main lower limb motor joint and the most vulnerable and susceptible joint.²² The KERs can improve the leg movement ability, reduce the movement consumption, and provide power and assistance for the lower limb movement. Kamali et al.²³ proposed a kind of knee exoskeleton, as shown in Figure 6. The equipment uses a linkage mechanism. The inverse dynamic model is used to calculate the power and torque distribution of the knee joint, the initial joint angles as the input to predict the user’s intended sit-to-stand trajectory. Experimental results showed that the exoskeleton could provide strength support for the patient’s knee joint during sitting to standing. Gams et al.²⁴ proposed a prototype of robot knee exoskeleton, as shown in Figure 7, the equipment uses a linkage mechanism. It can provide power for users to squat. Experiments have proved the feasibility of the equipment.

Figure 6.

FUM-knee exo.²³

Figure 7.

Robotic knee exoskeleton.²⁴

Kim et al.²⁵ designed a modular knee exoskeleton system to support the knee joint of hemiplegic patients. The knee joint is designed to actively follow the polycentric path using a four-bar linkage structure, which could realize the multi-center motion of real human knee. The device weighs only 3.5 kg. The foot is equipped with a force sensitive resistor (FSR) sensor, the knee joint is equipped with a torque sensor. This exoskeleton could identify the movement intention of users, and provide walking assistance and sitting posture correction for users. Aguirre-Ollinger et al.²⁶ designed a single DOF knee exoskeleton, which could improve the swinging movement of the legs of the elderly. The increase of inertia compensation enables subjects to recover their normal motion frequency. Lee et al.²⁷ designed a knee exoskeleton with a multi-center (multi-axis) structure to minimize the misalignment between user and robot. Torque transfer can be carried out effectively and can provide a slightly lower speed than normal walking speed, which is suitable for passive walking rehabilitation training. This technology is expected to be developed to simulate the multi-dimensional motion of the human knee joint.

The exoskeleton designed by Sherwani et al.²⁸ applied an adaptive RISE (Robust Integral of Sign Error) controller, which has good robustness and the efficiency of tracking the reference trajectory. It can calculate the patient’s intention to control the walking trajectory or movement according to the electromyogram signal, and can assist users to achieve knee flexion/extension movement. Zhang et al.²⁹ proposed a wearable soft knee exoskeleton. The IMUs control system could measure the knee joint angle information. The average metabolic cost of walking can be reduced by 6.85% by helping the knee joint move during walking. The patient’s movement intention and the actual movement of the lower limbs are not uniform. The combination of these two technologies can make the knee exoskeleton more intelligent and make the patient more secure.

Ankle exoskeleton robot

The ankle joint supports the human body’s activities such as standing, walking, sitting, and standing transfer, the ankle joint provides most of the energy to support the individual’s activities.^30,31 The lightweight passive ankle exercise device studied by Wang et al.³² consists of four parts: the clever clutch, an extension spring, a shank brace, and a shoe, the spring can store and release energy, as shown in Figure 8. The device can assist the walking process, allow the ankle to move freely, which is suitable for nearly all users. Shao et al.³³ proposed an adaptive ankle exoskeleton for walking assistance. A new compact variable stiffness SEA (PVS-SEA) with nonlinear spring is developed. The physical stiffness of exoskeleton can be changed with the change of ankle joint, which can adapt to walking tasks at different speeds. Zhang et al.³⁴ proposed to use electromyography (EMG) signal to control the ankle exoskeleton. Four layer feed-forward neural network model and back propagation training algorithm are used. The experimental results shows that this method can accurately control the ankle dorsiflexion and plantar flexion. If PVS-SEA can be combined with EMG, the exoskeleton will become very flexible, and the stiffness of the device can be directly adjusted according to the patient’s body state instead of calculation and prediction, which will greatly improve the comfort of the patient.

Figure 8.

Lightweight passive ankle exercise device.³²

The ankle foot orthosis (AFO) proposed by Li et al.³⁵allows users to change the stiffness of AFO to complete different lower limb activities. A quick release concept for AFO was presented and validated, the equipment has good strength. Kim et al.³⁶ designed a foot ankle orthosis, including a basic single degree of freedom exoskeleton and an artificial pneumatic actuator. The feed-forward control can reduce the muscle stiffness of soleus muscle, and the plantar flexion torque of the ankle joint can be reduced, which can provide ankle plantar flexion motion for the elderly.

Gait rehabilitation exoskeleton (GREs)

Gait rehabilitation exoskeleton robots (GRERs)

GRERs can assist patients to complete walking in a normal gait and provide power for them, which is suitable for patients with abnormal gait trajectory or joint trajectory. Shi et al.³⁷ designed a novel force field control for the three-dimensional gait adaptation. Trajectories of ankle center position in the 3D space measured by motion capture system were fitted to establish the Frenet frame. The force field controller was designed to guide the ankle center to move on a target trajectory in the 3D space. Simulation results showed that the designed system can effectively realize motion control in different modes. The actuator can improve the adaptive control ability of exoskeleton robots, so that robots can provide patients with an acceptable working state, which is more conducive to gait rehabilitation or standing posture improvement of users.^38–41

The use of EEG signals for user intention recognition and robot state control can further increase the human-computer interaction function of GRERs. Wall et al.⁴² designed HAL (Hybrid Assistive Limb) as shown in Figure 9, a Hybrid gait machine, and its control system can capture the intention of the user. The Autoencoder-based Transfer Learning (ATL) framework designed by Tan et al.¹ can use EEG signals to classify tasks, which can help the rehabilitation robot to understand the intention of the patient and help the patient to carry out rehabilitation exercise effectively. Sanguantrakul et al.^43,44 have shown that if the use of EEG signals to assist patients to exercise with imagination can give play to the initiative of patients’ movement intention, then the working mode provided by rehabilitation robot may be the most suitable for users.

Figure 9.

HAL.⁴²

EMG signal recognition is also effective. Xie et al.⁴⁵ established an surface Electromyography (sEMG) signal and interactive force (IF) fusion model based on the results of plantar pressure and flexion and extension recognition, which can identify the flexion and extension state of the lower limbs according to the EMG signal, and the trajectory of the robot can be planned online based on the sEMG-IF model to improve human-robot interaction and adaptability of lower limb rehabilitation robot. Chen et al.⁴⁶ Proposed the “Assist When Needed (AWN)” control strategy of gait rehabilitation robot, which provides flexible adaptive robot assistance function only when the user needs it. If this control strategy is combined with EEG or EMG signal recognition technology, it may be more flexible to provide patients with the required movement mode. The nonlinear control strategy can also achieve good gait tracking function.

Zhou et al.⁴⁷ proposed a triple-step nonlinear control strategy based on dynamic model. The results show that each joint of the human-robot system can follow the reference gait trajectory well under different interaction torque levels. Wu et al.⁴⁸ designed a LLERR with 3 DOF, as shown in Figure 10, which has an adjustable yet simple structure with hip, knee and ankle joints for different heights of patients. This research result is conducive to the realization of the personalized development of LLRERs.

Figure 10.

LLERR with 3 DOF.⁴⁸

Shao et al.⁴⁹ introduced the conceptual design and size synthesis of the cam linkage mechanism for gait rehabilitation. The original movement mechanism is a two-degree-of-freedom seven-bar crank slider mechanism. A constant speed motor is sufficient to control the mechanism. The experimental results show that the generated trajectory is in good agreement with the expected trajectory.

Lowell et al.⁵⁰ designed a prototype of gait rehabilitation exoskeleton, as shown in Figure 11. It can be used to customize and verify the gait mode and modify the gait in the process of rehabilitation. It can be used to specify personalized gait mode in the future.

Figure 11.

Gait rehabilitation exoskeleton.⁵⁰

Some promising structures/devices

Pneumatic artificial muscle is a promising development direction because soft robots offer advantages over rigid robots in adaptability, robustness to uncertainty, and human safety.⁵¹ Gu et al.⁵² proposed a soft wearable orthopedic device for gait assistance, with Pneumatic artificial muscle (PAM) the actuator, which reduces the weight of the equipment. The orthopedic device weighs only 680 g. Hashimoto et al.⁵³ developed a gait assist device composed of PAM and a common bracket, which is small in size, light in weight, and low in cost. The user has little discomfort in the process of using the device. This device has important commercial value.

The lower limb training with sitting and lying posture can reduce the weight burden of the user’s buttocks and legs, and increase the range of motion of the lower limb joints.⁵⁴ Cheng et al.⁵⁵ have designed a sitting and lying exoskeleton robot for lower limb rehabilitation, as shown in Figure 12, which can be used to improve the stability of patient rehabilitation training. The sitting and lying rehabilitation robot designed by Chen et al.,⁵⁶ as shown in Figure 13, can realize adaptive and compliant control of reference trajectory. The robot can better protect patients and provide effective rehabilitation training for patients.

Figure 12.

Prototype of the robot.⁵⁵

Figure 13.

Mechanical structure of rehabilitation exoskeleton.⁵⁶ (1-Man-machine interactive display device; 2-Telescopic mechanical leg structure; 3-Patients; 4-Seat structure with adjustable backrest; 5-Mobile platform structure.).

Cable driven is a new direction. Veneman et al.⁵⁷ designed LOPES, a newly developed gait rehabilitation device, which can realize two-way man-machine interaction. There are two modes, “patient in charge” and “robot in charge,” which control the robot to follow or guide the user respectively. Wang et al.⁵⁸ designed a modular cable driven lower limb rehabilitation robot prototype, as shown in Figure 14, which can complete the rehabilitation training task of human lower limbs with high stability, but subjects have not been recruited for real gait experiment.

Figure 14.

Rehabilitation robot prototype.⁵⁸

Daily life assistive robot (DLAR)

Daily life assistive exoskeleton robots (DLAERs)

DLAERs can provide users with protection, support and assistance, reduce the burden of lower limbs, and assist patients to complete activities. It is suitable for stroke patients, patients with brain trauma, patients with movement disorders caused by spinal cord injury (SCI), and elderly people with muscle weakness and insufficient joint strength. DLAERs can provide power to lower limb⁵⁹ and can assist patients to climb stairs, squat, sit-to-stand, and walk.^60,61 Wu et al.⁶² designed a lower-limb-driven exoskeleton robot using linkage mechanism to help patients with SCI complete sit-to-stand, walk, and stand-to-sit by driving the hip and knee joints, as shown in Figure 15. Two participants with a complete SCI were recruited for this clinical study. The results indicated that with a lower level of exertion and walked faster and farther without any injury or fall incidence when using the powered exoskeleton than when using a knee–ankle–foot orthosis. Chen et al.⁶³ designed the exoskeleton CUHK-EXO, as shown in Figure 16, to provide users with assistive force/torque, the user is able to transfer his/her center of gravity (COG) left and right more easily during walking. The left hip joint has two DOFs including the hip flexion/extension and hip external rotation. The antiflexion bar is designed to be adjustable with three DOFs to better fit for different wearers.

Figure 15.

Wearable exoskeleton Suit.⁶²

Figure 16.

CUHK-EXO: (a) a patient with CUHK-EXO supported by a pair of smart crutches, (b) diagram of the overall mechanical structure, (c) waist structure, (d) thigh structure, (e) shank structure, and (f) foot structure.

The passive assisted walking device for the lower limbs proposed by Martin and Li⁶⁴ helps the user by negative work on the knee joint at the end of the swing, the device is capable of reducing metabolic energy expenditure equal to the amount of additional energy required to carry the weight of the device. Zheng et al.⁶⁵ designed a simple wearable walking assisted LLER which adopts unbundled lifting method, the mechanical structure is more flexible. The device can adjust the working state through the sensor to adapt to the user’s physical state. Zhou et al.⁶⁶ designed a gravity-assisted walking LLER with a compact layout, which improves the acceptance and safety for users when using the assistive device; this LLER could reduce the user’s hip and knee torque when walking at the same time helps to maintain balance at a lower walking speed.

Dong et al.⁶⁷ proposed an electric lower limb exoskeleton: “Human Universal Mobility Assistance (HUMA).” This device can be adapted to patients of different weights and can help patients walk at various speeds. Poliero et al.⁶⁸ designed a soft modular, energy-saving lower limb exoskeleton that integrates quasi-passive actuation into the soft exoskeleton to assist low to moderate intensity activities in daily life. For patients whose lower limb movement chain is damaged due to limb loss, prosthesis is needed to make up the missing lower limb parts and functions, promote physical activities, and make lower limb activities tend to be normal.^69–71 Li et al.⁷² designed an asymmetric exoskeleton, as shown in Figure 17, the left leg is an exoskeleton mechanical leg worn on the healthy leg of the amputees. The right leg is an assisted lower limb prosthesis composed of intelligent lower limb prosthesis and exoskeleton mechanical leg, which is connected to the stump of the disabled thigh through the prosthetic receiving cavity. The assistive lower limb prosthesis can be used as an intelligent prosthesis alone. The length of the thigh and shank of the device can be adjusted to adapt to different people.

Figure 17.

Asymmetric exoskeleton.⁷²

Technology to improve the safety of DLARs

For organisms and robots, the ability to perceive and respond to disturbances and changes in the environment is essential.⁷³ The DLARs should have the ability to recognize obstacles to improve safety. Some scholars have proved the practicality of some obstacle detectors.^74,75 Obstacle detectors can detect terrain and make adjustments to working conditions to replace the user in making some action decisions. Researchers should improve the accuracy of detection technology, so as not to let the wrong decision cause harm to users.

Some advanced learning algorithms and control algorithms are also could improve the human-machine coordination ability. For example, Gaussian process learning algorithm can improve the accuracy of joint torque prediction,⁷⁶ active disturbance rejection control (ADRC) can improve the anti-interference and tracking performance.⁷⁷ Nonlinear model predictive controller (NMPC) can improve the robustness and stability of the system.⁷⁸ Kalinowska et al.⁷⁹ proposed an algorithm, it can identify contact events without touching the sensor, which is particularly useful when providing real-time assistance during walking. The sensor⁸⁰ can collect the lower limb state signal, improve the robot’s prediction and recognition ability, so as to predict gait events.

Other medical methods for lower limb rehabilitation

The researchers have conducted studies and shown that the combination of multiple rehabilitation medical methods can play a better effect, such as the functional electrical stimulation (FES) or acupuncture therapy.

Functional electrical stimulation (FES) could stimulate muscles to produce joint movement, or to stimulate the lower limbs in motion to cooperate with exoskeleton.^81,82 The overall physical condition of patients with FES assisted training has been significantly improved: Hwan et al.⁸³ combined robot assisted exercise with FES, the system can be operated in three different modes allowing both passive and active exercises, it can better protect the user from secondary injury. Ama et al.⁸⁴ designed a dynamic hybrid exoskeleton called Kinesis that can effectively balance muscular and robotic actuation during walking. Li and Yin⁸⁵ realized the time control of electrical stimulation, which can adjust the working mode according to the muscle fatigue state. Hmed et al.⁸⁶ designed a real-time FES system used model-free control (MFC) strategy to calculate the pulse width in the stimulation mode.

FES has some shortcomings. Users need high-intensity stimulation, sometimes even beyond the necessary stimulation intensity, to try to stand up.⁸⁷ FES supported standing requires the user to have sufficient muscle strength, which is achieved through muscle training.^88,89

The acupuncture and moxibustion therapy is beneficial to correct the posture, muscle shape and gait of the lower limbs,^90,91 restore the lower limb movement function of patients.⁹²

In the future, a acupuncture module could be set on the corresponding acupoint according to the position of the human body. The aforementioned joint rehabilitation robot, gait/lower limb function rehabilitation robot, daily life assistive robot could all be combined with acupuncture and FES, this will achieve better training results.

Discussion and conclusion

The details of the rehabilitation robot mentioned in this paper are shown in Table 1.

Table 1.

Details of each rehabilitation robot.

Devices	Subjects	Control strategy	Equipment test data and results
A wearable hip assist robot⁵	30 elderly adults (15 males/15 females)	GEMS that uses a particularly-shaped adaptive oscillator (PSAO) to estimate the gait phase.	Provide 16% of walking torque
EMG Power hip exoskeleton⁶	10 healthy subjects	EMG.	A 5.4° reduction in hip extension
A knee exoskeleton⁸	N/A	Feedback control with an enchanced disturbance observer, Sliding Mode Control (SM) and Integral Sliding Mode Control (ISM).	The peak of the joint motion is around 20°
A knee exoskeleton device based stiffness and motion augmentation⁹	One healthy male	Tele-impedance based assistive control scheme based on the variability of human joints impedance. IMU-based control.	Stiffness parameter is varying from 0 Nm/rad to 200 Nm/rad
A powered ankle exoskeleton¹⁰	Six subjects (three males, three females)	Speed-adaptive proportional myoelectric control.	Applying ~25% of normal biological ankle plantarflexion moment
Lower limb rehabilitation exoskeleton¹¹	One healthy subject	Multi-loop modulation method (MMM; 3M)	The tracking error of the maximum amplitude was reduced to as little as ~3%
A Passive Lower-Body Mechanism¹²	N/A	PID control	N/A
A soft exosuit¹⁵	Three subjects	IMU-based control	Desired force delivers around 30% of the hip extension power
Flexible Hip Assistive Exoskeleton¹⁶	One healthy male subject	Walking intention estimation approach based on gait synchronizer, direct torque control	Contributing up to 16.7% of the nominal biological moment during walking
A hip joint rehabilitation robot²¹	Two subjects	Generalized proportional integral (GPI) control	The maximum torque output is 12 Nm
Sit-to-Stand Knee Exoskeleton²³	A single male adult	Trajectory prediction Control based on joint Angle	The average knee strength was reduced by about 30%
Robot knee exoskeleton prototype²⁴	Seven healthy young males	Control based on gravity compensation and oscillator	N/A
A Knee Exoskeleton²⁵	One healthy person	The control algorithm is based on a finite state machine (FSM)	The range of motion is 120°
A one-degree-of-freedom exoskeleton²⁶	10 male healthy subjects	Emulated inertia compensation method	Cable drive combination is capable of delivering a continuous torque output of about 20.0 Nm
A Polycentric Knee Exoskeleton²⁷	N/A	Sliding mode control (SMC) based control	The robot’s knee movement angle was designed to be limited from 0° to 90°
EICoSI exoskeleton²⁸	N/A	Robust Integral of the Sign of the Error (RISE) with differentiable high gain feedback	The whole actuator can provide a maximum torque of 18 Nm
A wearable soft knee exoskeleton²⁹	Eight subjects (seven female subjects and one male subject)	IMUs based control	The actuator can produce a maximum rotational angle of 60°
Ankle Exoskeleton³⁰	Four healthy subjects	Iterative learning control (ILC) algorithm based control	The designed ankle exoskeleton can provide a peak pulling force around 545.5 N
Ankle Exoskeleton³¹	Eight healthy subjects	Human-in-the-loop (HIP) optimization control method	N/A
Adaptive Ankle Exoskeleton³³	One healthy subject	Genetic algorithm	The peak power of the ankle joint is 2.91 W/kg during normal walking, and it reaches 3.52 W/kg at high speed and 4.45 W/kg under 22.5 kg added load
Ankle Exoskeleton³⁴	Nine male subjects	Feed-forward neural network model and EMG signal control	N/A
Powered ankle exoskeleton³⁶	15 young adults and 15 elderly people	Feed-forward control	The maximal torque of plantarflexion following a pneumatic support was 122.34 ± 15.31 Nm in young adults and 94.89 ± 9.30 Nm in elderly people
A controllable knee ankle foot orthosis³⁸	One woman subject whose left limb impaired by poliomyelitis and one male subject affected by poliomyelitis during childhood	The knee joint state (intermittent stiffness) was controlled by segmental angular rotation and segmental angular velocity	The plantar fall is limited at maximum 5 (mean, 4.5)
A variable-impedance ankle-foot orthosis³⁹	One healthy subject	Torque-control	It has a ROM of 32° in plantarflexion and 21° in dorsiflexion
Lower limb rehabilitation robot⁴⁵	Five male and one female able-bodied subjects	EMG signal control	In 0-3 s, the end’s velocity of robot was raised in the direction of lower limb motion with the increase of human-robot interactive force. In 6.9–9.8 s, the velocity began to decrease
Gait Rehabilitation Robot⁴⁶	One healthy subject	Assist-When-Needed (AWN) control	The mean peak knee joint angle decreased significantly from 53° in zero-assistance mode to 30°
A passive lower limb exoskeleton for walking assistance⁴⁷	One healthy subject	Triple-step nonlinear control strategy	The average absolute driving torque of hip joint was reduced by 79.0%, and that of knee joint was reduced by 66.4%
Powered lower limb exoskeleton robot⁴⁸	One healthy male subject	Closed-loop control, adaptive robust (AR) sub-control	Provide rated torque of 35 Nm and peak torque of more than 100 Nm
A wearable exoskeleton suit⁶³	One healthy subject	Assistive torque and position control based on wearer’s motion intention	N/A
A lower extremity exoskeleton for walking assist⁶⁵	N/A	Control based on direct force feedback	3 kg for walking assistance and 10 kg for standing assistance
A passive lower limb exoskeleton for walking assistance⁶⁶	One healthy subject	N/A	The average absolute driving torque was reduced by 79.0% at the hip joint and 66.4% at the knee joint. The mean maximum torque provided during knee bending was 42.9 ± 0.46 Nm and 28.4 ± 0.28 Nm during stretching
Soft wearable device for lower limb assistance⁶⁸	One healthy person	Gait segmentation algorithm	Provides 9.3% of hip torque

After statistics and analysis, it can be seen from the full text that the existing research still has problems that need improvement in the following aspects:

Structure/mechanism design. Most robots currently use linkage mechanisms. Such as Sit-to-Stand Knee Exoskeleton,²³ Robotic Knee Exoskeleton,²⁴ modular knee exoskeleton using a 4-bar linkage structure.²⁵ Some equipment uses a cable structure, such as literature.^57,58 Some scholars have designed new motion structures, such as multi-center (multi-axis) knee exoskeleton.²⁷ Each device has different DOF, such as flexible hip assistive exoskeleton¹⁶ with 4 DOF, 1-DOF knee exoskeleton.²⁶ Some devices use linkage mechanisms, and some devices have limited DOF, and they cannot fully simulate the multi-dimensional motion of the lower limbs, and will limit the freedom of movement of the lower limbs.

Control methods. As can be seen from the “control strategy” and “Equipment test data & results” column in the Table 1, researchers have used many different control methods, such as feedback control,^8,28,65 feed-forward control,^34,36 Speed-adaptive proportional myoelectric control,¹⁰ 3 M control method¹¹ PID control,¹² Directed torque control.¹⁶ The assistance types, data and metabolic reduction rate provided by these devices are different. Although the control system can well adjust the human-machine interaction relationship between users and devices, it cannot fully achieve personalized assistance to a certain extent.

Rehabilitation effect evaluation. As can be seen from the “subject” column in the Table 1, in the process of the study of exoskeleton, the most subjects are healthy people, the test was conducted under the laboratory environment, the test time and the number of subjects is limited, some devices has not been carried out clinical test. Some of the device studies have not yet recruited subjects to test the device. Scholars have designed various types of exoskeletons, but the long-term effects of various types of exoskeletons of the same type lack comparisons to further clarify their advantages and disadvantages.

Safety and human-machine cooperation. Rehabilitation robots cannot completely replace the lower limb function, or replace human to make decisions and adjustments in the complex environment, and the ability to identify obstacles and sense the environment still needs to be improved.

Therefore, future research can be improved in the following aspects:

Improve man-machine cooperation. The structural design should be as close as possible to the multi-dimensional movement of the lower limbs of the human body. Due to the rigidity of the material or structure, and the patient’s poor limb flexibility, it will cause a certain degree of damage to the human body. The flexibility of equipment movement should also be improved, so that patients will not lose balance or get injured due to the passive movement of limbs when the equipment is started. What’s more, the design of LLERRs should be simple, easy to understand, easy to use, easy to carry, or less affected by the place of use.

The control method should be further improved, tested, and more intelligent. The control system should be improved to specify more parameters (such as inertia, damping, stiffness) to adapt to different patients, to achieve personalized auxiliary services.

Every device should be clinically tested, and the source of data used in the research should be improved. Physiological data used in the design process of LLRERs should be close to the patient’s daily life state, or data tracking and collection is conducted in the patient’s daily life. Because whether the gait is abnormal or healthy, the state in laboratory conditions is not exactly the same as in daily life.⁹³ In addition, the sample size of the subjects was need to be increased to make the data more representative. Researchers should clarify the effect of long-term use, because LLRERs will reduces the energy consumption of patients, if patients rely on exoskeleton for a long time, it may cause muscle degeneration.

Lightweight. For example, with the increase of environmental recognition function, degree of freedom or some assistive treatment functions, corresponding equipment modules or drive system should also be added, this will increase the weight of LLRERs. Although LLRERs can balance the weight of the devices by providing torque and changing power, it will also affect the working efficiency of LLRERs.

Personalized customization. LLRERs can be personalized as much as possible, and patient initiative can be fully mobilized. Some modular design and pattern adjustment systems can be added to LLRERs. For example, add EMG signal, EEG signal recognition function. Studies have shown that patient-driven control strategies can stimulate patient participation and speed up the recovery process, compared with robot-driven control strategies that allow patients to remain passive during training.⁹⁴ In addition, patients should be specifically trained in the use of LLRERs so that each patients can receive maximum rehabilitation assistance.

The cost. The poor financial situation of some patients has affected their choice and use of LLRERs. Therefore, improve the appearance design and popularize the corresponding basic knowledge, and reduce the cost of LLRERs, so as to help patients accept, select and use LLRERs.

Footnotes

Handling Editor: James Baldwin

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Key Project of Tianjin Natural Science Foundation (19JCZDJC33200), Tianjin Natural Science Foundation (18JCQNJC75300).

ORCID iD

Qiang Xue

References

Tan

Sun

Fang

, et al. Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot. Int J Adv Robot Syst 2019; 16(2), pp.1–12.

Liu

Yin

Yan

. A survey on the exoskeleton rehabilitation robot for the lower limbs. In: 2016 2nd international conference on control, automation and robotics(ICCAR), Hong Kong, China, 28–30 April 2016, pp.90–94. New York: IEEE.

Yang

Bai

, et al. Assistive standing of omni-direetional mobile rehabilitation training robot based on support vector regression algorithm. In: 2016 IEEE international conference on information and automation(ICIA), Ningbo, China, 1–3 August 2016, pp.1050–1055. New York: IEEE.

Shi

Zhang

, et al. A review on lower limb rehabilitation exoskeleton robots. Chin J Mech Eng 2019; 32: 1–11.

Lee

Chang

, et al. A wearable hip assist robot can improve gait function and cardiopulmonary metabolic efficiency in elderly adults. IEEE Trans Neural Syst Rehabil Eng 2017; 25: 1549–1557.

Lenzi

Carrozza

Agrawal

. Powered hip exoskeletons can reduce the user’s hip and ankle muscle activations during walking. IEEE Trans Neural Syst Rehabil Eng 2013; 21: 938–948.

Shi

Zhang

, et al. Assist-as-needed attitude control in three-dimensional space for robotic rehabilitation. Mech Mach Theory 2020; 154: 104044.

Luo

Wang

, et al. (2018). Comparison of different control algorithms for a knee exoskeleton. In: 2018 15th international conference on control, automation, robotics and vision(ICARCV), Singapore, 18–21 November 2018, pp.585–590.

Karavas

Ajoudani

Tsagarakis

, et al. Tele-impedance based stiffness and motion augmentation for a knee exoskeleton device. In: 2013 IEEE international conference on robotics and automation, Karlsruhe, 6–10 May 2013, pp.2194–2200. New York: IEEE.

10.

McCain

Dick

TJM

Giest

, et al. Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control. J Neuroeng Rehabil 2019; 16(1): 1–12.

11.

Pan

Chang

Yang

, et al. Development a multi-loop modulation method on the servo drives for lower limb rehabilitation exoskeleton. Mechatronics 2020; 68: 102360.

12.

Singh

Singla

Virk

. Modeling and simulation of a passive lower-body mechanism for rehabilitation. In: Conference on mechanical engineering and technology(COMET-2016), at: department of mechanical engineering, IIT (BHU), Varanasi, 15–17 January 2016, pp.123–128.

13.

Jezernik

Colombo

Keller

, et al. Robotic orthosis lokomat: a rehabilitation and research tool. Neuromodulation 2003; 6: 108–115.

14.

https://eksobionics.com/

15.

Ding

Galiana

Asbeck

, et al. Multi-joint actuation platform for lower extremity soft exosuits. In: Robotics and automation(ICRA), 31 May–7 June 2014, pp.1327–1334. Hong Kong, China: IEEE.

16.

Xue

Wang

Zhang

, et al. The control system for flexible hip assistive exoskeleton. In: 2018 IEEE international conference on robotics and biomimetics(ROBIO), Kuala Lumpur, Malaysia, 12–15 December 2018, pp.697–702. New York: IEEE.

17.

Zhang

Shi

, et al. Design of hip joint assistant asymmetric parallel mechanism and optimization of singularity-free workspace. Mech Mach Theory 2018; 122: 389–403.

18.

Zhang

Tran

Huang

. NREL-Exo: a 4-DoFs wearable hip exoskeleton for walking and balance assistance in locomotion. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), Vancouver, Canada, 24–28 September 2017, pp.508–513.

19.

Zhang

Ding

, et al. Optimization of the rotational asymmetric parallel mechanism for hip rehabilitation with force transmission factors. J Mech Robot 2020; 12: 1–23.

20.

Mahdavian

Arzanpour

Park

. Motion generation of a wearable hip exoskeleton robot using machine learning-based estimation of ground reaction forces and moments. In: 2019 IEEE/ASME international conference on advanced intelligent mechatronics(AIM), Hong Kong, China, 8–12 July 2019, pp.796–801. New York: IEEE

21.

Guzmán

Blanco

Brizuela

, et al. Robust control of a hip–joint rehabilitation robot. Biomed Signal Process Control 2017; 35: 100–109.

22.

Zhang

Liu

Han

, et al. Knee joint biomechanics in physiological conditions and how pathologies can affect it: a systematic review. Appl Bionics Biomech 2020; 1: 1–22.

23.

Kamali

Akbari

Akbarzadeh

. Trajectory generation and control of a knee exoskeleton based on dynamic movement primitives for sit-to-stand assistance. Adv Robot 2016; 30: 846–860.

24.

Gams

Petric

Debevec

, et al. Effects of robotic knee exoskeleton on human energy expenditure. IEEE Trans Biomed Eng 2013; 60: 1636–1644.

25.

Kim

Shim

Ahn

, et al. Design of a knee exoskeleton using foot pressure and knee torque sensors. Int J Adv Robot Syst 2015; 12: 112.

26.

Aguirre-Ollinger

Colgate

Peshkin

, et al. Inertia compensation control of a one-degree-of-freedom exoskeleton for lower-limb assistance: initial experiments. IEEE Trans Neural Syst Rehabil Eng 2012; 20: 68–77.

27.

Lee

Song

, et al. Design and control of a polycentric knee exoskeleton using an electro-hydraulic actuator. Sensors 2019; 20: 211.

28.

Sherwani

KIK

Kumar

Chemori

, et al. RISE-based adaptive control for EICoSI exoskeleton to assist knee joint mobility. Robot Auton Syst 2020; 124: 103354.

29.

Zhang

Huang

Cai

, et al. A wearable soft knee exoskeleton using vacuum-actuated rotary actuator. IEEE Access 2020; 8: 61311–61326.

30.

Tang

, et al. The mechanical design and torque control for the ankle exoskeleton during human walking. Intell Robot Appl 2019(11744): 26–37.

31.

Yan

Tang

Xiang

, et al. Human-in-the-loop optimization control for the ankle exoskeleton during walking based on iterative learning and particle swarm optimization algorithm. In: 2019 IEEE 4th international conference on advanced robotics and mechatronics(ICARM), Toyonaka, Japan, 3–5 July 2019, pp.570–574. New York: IEEE.

32.

Wang

Guo

, et al. Design of a passive gait based ankle foot exoskeleton with self adaptive capability. Chin J Mech Eng 2020; 33: 86–96.

33.

Shao

Zhang

, et al. Design of a novel compact adaptive ankle exoskeleton for walking assistance. Adv Mech Mach Sci 2019; 173: 2159–2168.

34.

Zhang

Jiang

Peng

, et al. A method to control ankle exoskeleton with surface electromyography signals. ICIRA, Shanghai, China, 10–12 November 2010. pp.390–397. Berlin, Heidelberg: Springer.

35.

Lemaire

E D

Baddour

. Design and evaluation of a modularized ankle-foot orthosis with quick release mechanism. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society(EMBC), 20–24 July, 2020, pp.4831–4834. Montreal, QC, Canada: IEEE.

36.

Kim

Kang

, et al. Analysis of the assistance characteristics for the plantarflexion torque in elderly adults wearing the powered ankle exoskeleton. In: International conference on control automation and systems, ICCAS, Gyeonggi-do, Korea, 27–30 October 2010, pp.576–579. New York: IEEE.

37.

Shi

Zhang

, et al. Force field control for the three-dimensional gait adaptation using a lower limb rehabilitation robot. In: Uhl

(ed.) Advances in Mechanism and Machine Science. Cham: Springer, 2019. pp.1919–1928.

38.

Moreno

Brunetti

Rocon

, et al. Immediate effects of a controllable knee ankle foot orthosis for functional compensation of gait in patients with proximal leg weakness. Med Biol Eng Comput 2008; 46: 43–53.

39.

Moltedo

Baček

Langlois

, et al. Design and experimental evaluation of a lightweight, high-torque and compliant actuator for an active ankle foot orthosis. In: 2017 international conference on rehabilitation robotics(ICORR), London, 17–20 July 2017, pp.283–288. New York: IEEE.

40.

Kim

Durfee

. The application of series elastic actuators in the hydraulic ankle-foot orthosis. In: 2018 design of medical devices conference, Minneapolis, Minnesota, 9–12 April 2018, ASME.

41.

Pan

Chen

, et al. Multi-modal control scheme for rehabilitation robotic exoskeletons. Int J Rob Res 2017; 36: 759–777.

42.

Wall

Borg

Vreede

, et al. A randomized controlled study incorporating an electromechanical gait machine, the hybrid assistive limb, in gait training of patients with severe limitations in walking in the subacute phase after stroke. PLoS One 2020; 15: e0229707.

43.

Sanguantrakul

Soontreekulpong

Trakoolwilaiwan

, et al. Analysis of walking movement using eeg for the lower-limb paralysis. In: 2019 12th biomedical engineering international conference (BMEiCON), Ubon Ratchathani, Thailand, 19–22 November 2019, pp.1–4. New York: IEE.

44.

Bai

Kelly

Fei

, et al. A wireless, smart EEG system for volitional control of lower-limb prosthesis. In: TENCON 2015 IEEE region 10 conference, Macao, China, 1–4 November 2015, pp.1–6. New York: IEEE.

45.

Xie

Qiu

, et al. Adaptive trajectory planning of lower limb rehabilitation robot basedon EMG and human-robot interaction. In: 2016 IEEE international conference on information and automation (ICIA), Ningbo, China, 1–3 August 2016, pp.1273–1277. New York: IEEE.

46.

Chen

Liu

, et al. Adaptive control strategy for gait rehabilitation robot to assist-when-needed. In: 2018 IEEE international conference on real-time computing and robotics (RCAR), Kandima, Maldives, 1–5 August 2018, pp.538–543. New York: IEEE.

47.

Zhou

Yang

Zhao

, et al. Gait tracking based triple-step nonlinear control for a lower limb rehabilitation robot. In: 2019 IEEE 9th annual international conference onCYBER technology in automation, control, and intelligent systems(CYBER), Suzhou, China, 29 July–2 August 2019, pp.1006–1009. New York: IEEE.

48.

Gao

Song

, et al. The design and control of a 3DOF lower limb rehabilitation robot. Mechatronics 2016; 33: 13–22.

49.

Shao

Xiang

Liu

, et al. Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation. Mech Mach Theory 2016; 104: 31–42.

50.

Rose

Bazzocchi

MCF

de Souza

, et al. A framework for mapping and controlling exoskeleton gait patterns in both simulation and real-world. In: Proceedings of the 2020 design of medical devices conference, Minneapolis, Minnesota, 6–9 April 2020. ASME.

51.

Naclerio

Hawkes

. Simple, low-hysteresis, foldable, fabric pneumatic artificial muscle. IEEE Robot Autom Lett 2020; 5: 3406–3413.

52.

, et al. Lower-limb soft orthotic device for gait assistance. In: 2018 IEEE 4th information technology and mechatronics engineering conference(ITOEC), Chongqing, China, 14–16 December 2018, pp.894–897. New York: IEEE.

53.

Hashimoto

Nakanishi

Saga

, et al. Development of gait assistive device using pneumatic artificial muscle. In: 2016 Joint 8th international conference on soft computing and intelligent systems (SCIS) and 17th international symposium on advanced intelligent systems(ISIS), Sapporo, Japan, 25–28 August 2016, pp.710–713. New York: IEEE.

54.

Wang

Feng

, et al. Mechanical design and trajectory planning of a lower limb rehabilitation robot with a variable workspace. Int J Adv Robot Syst 2018; 15: 1–13.

55.

Cheng

Chen

, et al. Design and control of a sitting/lying style lower limb rehabilitation robot with magnetorheological actuators. In: 2019 IEEE 9th international conference on electronics information and emergency communication (ICEIEC), Beijing, China, 12–14 July 2019, pp.1–6. New York: IEEE.

56.

Chen

Huang

Guo

, et al. Parameter identification and adaptive compliant control of rehabilitation exoskeleton based on multiple sensors. Measurement 2020; 159: 107765.

57.

Veneman

Kruidhof

Hekman

EEG

, et al. Design and evaluationof the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng 2007; 15: 379–386.

58.

Wang

Zhang

. Design, comprehensive evaluation, and experimental study of a cable-driven parallel robot for lower limb rehabilitation. J Braz Soc Mech Sci Eng 2020; 42: 1–20.

59.

Aguirre

. Learning muscle activation patterns via nonlinear oscillators: application to lower-limb assistance. In: 2013 IEEE/RSJ international conference on intelligent robots and systems, Tokyo, Japan, 3–7 November 2013, pp.1182–1189. New York: IEEE

60.

Carrera

Moreno

Marín

SDS

, et al. Technologies for therapy and assistance of lower limb disabilities: sit to stand and walking. In: Exoskeleton robots for rehabilitation and healthcare devices. 2020. Singapore: Springer Briefs in applied sciences and technology, pp.43–66.

61.

Baek

Song

, et al. A motion phase-ased hybrid assistive controller for lower limb exoskeletons. In: 2014 IEEE 13th international workshop on advanced motion control(AMC), Yokohama, Japan, 14–16 March 2014, pp.197–202. New York: IEEE.

62.

Mao

, et al. The effects of gait training using powered lower limb exoskeleton robot on individuals with complete spinal cord injury. J Neuroeng Rehabil 2018; 15: 14.

63.

Chen

Zhong

Zhao

, et al. A wearable exoskeleton suit for motion assistance to paralysed patients. J Orthop Translat 2017; 11: 7–18.

64.

Martin

. The metabolic cost of walking with a passive lower limb assistive device. Lect Notes Electr Eng 2016: 301–305.

65.

Zheng

Mingjiang

Wulong

, et al. Design and implementation of a lower extremity exoskeleton for walking assist. Acta entiarum Naturalium Universitatis Pekinensis 2017; 53: 989–996.

66.

Zhou

Chen

, et al. Design of a passive lower limb exoskeleton for walking assistance with gravity compensation. Mech Mach Theory 2020; 150: 103840.

67.

Dong

Hyunseok

, et al. Biomechanical design of an agile, electricity-powered lower-limb exoskeleton for weight-bearing assistance. Robot Auton Syst 2017; 95: 181–195.

68.

Poliero

Natali

Sposito

, et al. Soft wearable device for lower limb assistance: assessment of an optimized energy efficient actuation prototype. In: 2018 IEEE international conference on soft robotics(RoboSoft), Livorno, 24– 28 April 2018, pp.559–564. New York: IEEE.

69.

Littman

Haselkorn

Arterburn

, et al. Pilot randomized trial of a telephone-delivered physical activity and weight management intervention for individuals with lower extremity amputation. Disabil Health J 2019; 12: 43–50.

70.

Christiansen

Miller

Murray

, et al. Behavior-change intervention targeting physical function, walking, and disability after dysvascular amputation: a randomized controlled pilot trial. Arch Phys Med Rehabil 2018; 99: 2160–2167.

71.

Pandit

Godiyal

Vimal

, et al. An affordable insole-sensor-based trans-femoral prosthesis for normal gait. Sensors 2018; 18: 706.

72.

Wang

Xie

, et al. Admittance control of four-link bionic knee exoskeleton with inertia compensation. Tehnicki Vjesn 2020; 27: 891–897.

73.

Krausz

Hargrove

. Subject- and environment-based sensor variability for wearable lower-limb assistive devices. Sensors 2019; 19: 4887.

74.

Khademi

Simon

. Convolutional neural networks for environmentally aware locomotion mode recognition of lower-limb amputees. In: Proceedings of the ASME dynamic systems and control conference (DSCC), Utah, 8–11 October 2019. ASME.

75.

Zhang

Xiong

Zhang

, et al. Environmental features recognition for lower limb prostheses toward predictive walking. IEEE Trans Neural Syst Rehabil Eng 2019; 27: 465–476.

76.

Yang

Yin

. Dependent-Gaussian-process-based learning of joint torques using wearable smart shoes for exoskeleton. Sensors 2020; 20: 3685.

77.

Yang

Han

Xia

, et al. An optimal fuzzy-theoretic setting of adaptive robust control design for a lower limb exoskeleton robot system. Mech Syst Signal Process 2020; 141: 106706.

78.

Tahamipour Zarandi

Hosseini Sani

Akbarzadeh Tootoonchi

, et al. Design and implementation of a real-time nonlinear model predictive controller for a lower limb exoskeleton with input saturation. Iran J Sci Technol Trans Electr Eng 2021; 45: 309–320.

79.

Kalinowska

Berrueta

Zoss

, et al. Data-driven gait segmentation for walking assistance in a lower-limb assistive device. In: 2019 international conference on robotics and automation, Montreal, Canada, 20–24 May 2019, pp.1390–1396. IEEE.

80.

Krausz

Hargrove

. A novel method for bilateral gait segmentation using a single thigh-mounted depth sensor and IMU. In: Proceedings of the IEEE international conference on biomedical robotics and biomechatronics (Biorob), Enschede, 26–29 August 2018, pp.807–812. New York: IEEE.

81.

Stauffer

Allemand

Bouri

, et al. The WalkTrainer—A New Generation of walking reeducation device combining orthoses and muscle stimulation. IEEE Trans Neural Syst Rehabil Eng 2009; 17: 38–45.

82.

Ambrosini

Parati

Peri

, et al. Changes in leg cycling muscle synergies after training augmented by functional electrical stimulation in subacute stroke survivors: a pilot study. J Neuroeng Rehabil 2020; 17(1): 1–14.

83.

Bong

Jung

Park

, et al. Development of a novel robotic rehabilitation system with muscle-to-muscle interface. Front Neurorobot 2020; 14(3): 1–13.

84.

del-Ama

Gil-Agudo

Pons

, et al. Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton. J Neuroeng Rehabil 2014; 11: 27.

85.

Yin

. A methodfor FES control of human knee joint with time-dependent model parameters. In: 2019 IEEE 15th international conference on control and automation (ICCA), Edinburgh, 16–19 July 2019, pp.1302–1306. New York: IEEE.

86.

Hmed

Bakir

Binczak

, et al. Model free control for muscular force by functional electrical stimulation using pulse width modulation. In: 2016 4th international conference on control engineering & information technology(CEIT), Hammamet, 16–18 December 2016, pp.1–6.

87.

Triolo

Bevelheimer

Eisenhower

, et al. Inter-rater reliability of a clinical test of standing function. J Spinal Cord Med 1995; 18: 14–22.

88.

Ibitoye

Hamzaid

Hasnan

, et al. Strategies for rapid muscle fatigue reduction during FES exercise in individuals with spinal cord injury: a systematic review. PLoS One 2016; 11: e0149024.

89.

Gera

Tharion

Devasahayam

, et al. Electrical stimulation and assessment of the induced force in the denervated muscle. In: TENCON 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 17–20 October 2019, pp.2046–2050. New York: IEEE.

90.

Ruan

Chen

Lin

, et al. Effect of Jiaotong Qiaomai needling technique of acupuncture on the morphology of lower limb muscle based on muscle-skeleton ultrasound measurement in patients with post-stroke strephenopodia. Zhongguo Zhen Jiu 2020; 40: 251–255.

91.

Wang

H-Q

Hou

, et al. Effects of acupuncture treatment on motor function in patients with subacute hemorrhagic stroke: a randomized controlled study. Complement Ther Med 2020; 49: 102296.

92.

Dolkun

Guo

, et al. Zhishen Tiaosui acupuncture on the unilateral spatial neglect after stroke: a randomized controlled trial. Chin Acupunct Moxibustion 2019; 39: 1041–1045.

93.

Kim

Colabianchi

Wensman

, et al. Wearable sensors quantify mobility in people with lower limb amputation during daily life. IEEE Trans Neural Syst Rehabil Eng 2020; 28: 1282–1291.

94.

Wang

, et al. A patient-driven control method for lower-limb rehabilitation robot. In: 2016 IEEE international conference on mechatronics and automation, Harbin, 7–10 August 2016, pp.908–913. New York: IEEE.