Voluntary Control of an Ankle Joint Exoskeleton by Able-Bodied Individuals and Stroke Survivors Using EMG-Based Admittance Control Scheme

Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model

Human-machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects' performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements.

An Online Estimating Framework for Ankle Actively Exerted Torque under Multi-DOF Coupled Dynamic Motions via sEMG

Ankle rehabilitation robots can offer tailored rehabilitation training, and facilitate the functional recovery of patients. Accurate estimation of the actively exerted torque from the ankle joint complex (AJC) can increase the engagement of patients during rehabilitation training. Given the three degrees of freedom (DOFs) of AJC and its coupled motion, it becomes essential to accurately estimate the actively exerted torque under multi-DOF. This work introduces an estimation framework that includes the Hill-based sEMG-force model, the ankle musculoskeletal dynamic decoupling model, and the parameter identification-calibration strategy. The Hill-based sEMG-force model estimates the force generated by individual muscles involved in AJC; The parameter identification-calibration strategy combined with pre-experiment identifies unknown variables in the ankle musculoskeletal dynamic decoupling model; Finally, the musculoskeletal dynamic decoupling model relates the muscle forces to the AJC's actively exerted torque. The musculoskeletal dynamic decoupling model combines anatomical and biomechanical features, enabling parameters derived from a single DOF pre-experiment through identification-calibration strategy to be applicable in multi-DOF dynamic motion. To evaluate the estimation performance of the framework, experiments were conducted in various directions involving both single and multiple DOFs. The results show that the proposed framework can estimate the actively exerted torque with a normalized root mean square error (NRMSE) of 10.29% ± 2.86% (mean ± SD) for torque estimation under a single DOF, and NRMSE of 11.35% ± 4.51% under multiple DOFs, compared to the actual measured values. This framework can improve human-robot interaction training and improve the effectiveness of robot-assisted ankle rehabilitation training. It can also provide accurate neuro-information and joint torque data for medical teams, which can lead to early diagnosis of diseases and patient-specific treatment protocols.

Motor Interaction Control Based on Muscle Force Model and Depth Reinforcement Strategy

The current motion interaction model has the problems of insufficient motion fidelity and lack of self-adaptation to complex environments. To address this problem, this study proposed to construct a human motion control model based on the muscle force model and stage particle swarm, and based on this, this study utilized the deep deterministic gradient strategy algorithm to construct a motion interaction control model based on the muscle force model and the deep reinforcement strategy. Empirical analysis of the human motion control model proposed in this study revealed that the joint trajectory correlation and muscle activity correlation of the model were higher than those of other comparative models, and its joint trajectory correlation was up to 0.90, and its muscle activity correlation was up to 0.84. In addition, this study validated the effectiveness of the motion interaction control model using the depth reinforcement strategy and found that in the mixed-obstacle environment, the model's desired results were obtained by training 1.1 × 103 times, and the walking distance was 423 m, which was better than other models. In summary, the proposed motor interaction control model using the muscle force model and deep reinforcement strategy has higher motion fidelity and can realize autonomous decision making and adaptive control in the face of complex environments. It can provide a theoretical reference for improving the effect of motion control and realizing intelligent motion interaction.

Design and assessment of a reconfigurable behavioral assistive robot: a pilot study

Introduction

For patients with functional motor disorders of the lower limbs due to brain damage or accidental injury, restoring the ability to stand and walk plays an important role in clinical rehabilitation. Lower limb exoskeleton robots generally require patients to convert themselves to a standing position for use, while being a wearable device with limited movement distance.

Methods

This paper proposes a reconfigurable behavioral assistive robot that integrates the functions of an exoskeleton robot and an assistive standing wheelchair through a novel mechanism. The new mechanism is based on a four-bar linkage, and through simple and stable conformal transformations, the robot can switch between exoskeleton state, sit-to-stand support state, and wheelchair state. This enables the robot to achieve the functions of assisted walking, assisted standing up, supported standing and wheelchair mobility, respectively, thereby meeting the daily activity needs of sit-to-stand transitions and gait training. The configuration transformation module controls seamless switching between different configurations through an industrial computer. Experimental protocols have been developed for wearable testing of robotic prototypes not only for healthy subjects but also for simulated hemiplegic patients.

Results

The experimental results indicate that the gait tracking effect during robot-assisted walking is satisfactory, and there are no sudden speed changes during the assisted standing up process, providing smooth support to the wearer. Meanwhile, the activation of the main force-generating muscles of the legs and the plantar pressure decreases significantly in healthy subjects and simulated hemiplegic patients wearing the robot for assisted walking and assisted standing-up compared to the situation when the robot is not worn.

Discussion

These experimental findings demonstrate that the reconfigurable behavioral assistive robot prototype of this study is effective, reducing the muscular burden on the wearer during walking and standing up, and provide effective support for the subject's body. The experimental results objectively and comprehensively showcase the effectiveness and potential of the reconfigurable behavioral assistive robot in the realms of behavioral assistance and rehabilitation training.

Autonomous motion and control of lower limb exoskeleton rehabilitation robot

Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient's motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation. Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient's desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory. Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively.

Effects of EMG-based robot for upper extremity rehabilitation on post-stroke patients: a systematic review and meta-analysis

Objective: A growing body of research shows the promise and efficacy of EMG-based robot interventions in improving the motor function in stroke survivors. However, it is still controversial whether the effect of EMG-based robot is more effective than conventional therapies. This study focused on the effects of EMG-based robot on upper limb motor control, spasticity and activity limitation in stroke survivors compared with conventional rehabilitation techniques. Methods: We searched electronic databases for relevant randomized controlled trials. Outcomes included Fugl-Meyer assessment scale (FMA), Modified Ashworth Scale (MAS), and activity level. Result: Thirteen studies with 330 subjects were included. The results showed that the outcomes post intervention was significantly improved in the EMG-based robot group. Results from subgroup analyses further revealed that the efficacy of the treatment was better in patients in the subacute stage, those who received a total treatment time of less than 1000 min, and those who received EMG-based robotic therapy combined with electrical stimulation (ES). Conclusion: The effect of EMG-based robot is superior to conventional therapies in terms of improving upper extremity motor control, spasticity and activity limitation. Further research should explore optimal parameters of EMG-based robot therapy and its long-term effects on upper limb function in post-stroke patients. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/; Identifier: 387070.

Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training

Stroke has become a major disease that seriously threatens human health due to its high incidence and disability rates. Most patients undergo upper limb motor dysfunction after stroke, which significantly impairs the ability of stroke survivors in their activities of daily living (ADL). Robots provide an optional solution for stroke rehabilitation by attending therapy in the hospital and the community, however, the rehabilitation robot still has difficulty in providing needed assistance interactively like human clinicians in conventional therapy. For safe and rehabilitation training, a human-robot interaction space reshaping method was proposed based on the recovery states of patients. According to different recovery states, we designed seven experimental protocols suitable for distinguishing rehabilitation training sessions. To achieve assist-as-needed (AAN) control, a PSO-SVM classification model and an LSTM-KF regression model were introduced to recognize the motor ability of patients with electromyography (EMG) and kinematic data, and a region controller for interaction space shaping was studied. Ten groups of offline and online experiments and corresponding data processing were conducted, and the machine learning and AAN control results were presented, which ensured the effective and the safe upper limb rehabilitation training. To discuss the human-robot interaction in different training stages and sessions, we defined a quantified assistance level index that characterizes the rehabilitation needs by considering the engagement of the patients and had the potential to apply in clinical upper limb rehabilitation training.

Implementation of Robotic Ankle–Foot Orthosis With an Impedance-Based Assist-as-Needed Control Strategy

In this paper, a robotic ankle–foot orthosis (AFO) is developed for individuals with a paretic ankle, and an impedance-based assist-as-needed controller is designed for the robotic AFO to provide adaptive assistance. First, a description of the robotic AFO hardware design is presented. Next, the design of the finite state machine is introduced, followed by an introduction to the modeling of the robotic AFO. Additionally, the control of the robotic AFO is presented. An impedance-based high-level controller that is composed of an ankle impedance based torque generation controller and an impedance controller is designed for the high-level control. A compensated low-level controller that is composed of a braking controller and a proportional-derivative controller with a compensation part is designed for the low-level control. Finally, a pilot study with eight healthy participants is conducted, and the experimental results demonstrate that with the proposed control algorithm, the robotic AFO has the potential for ankle rehabilitation by providing adaptive assistance. In the assisted condition with a high level of assistance, reductions of 8% and 20.1% of the root mean square of the tibialis anterior and lateral soleus activities are observed, respectively.

An sEMG-Based Human-Exoskeleton Interface Fusing Convolutional Neural Networks With Hand-Crafted Features

In recent years, the human-robot interfaces (HRIs) based on surface electromyography (sEMG) have been widely used in lower-limb exoskeleton robots for movement prediction during rehabilitation training for patients with hemiplegia. However, accurate and efficient lower-limb movement prediction for patients with hemiplegia remains a challenge due to complex movement information and individual differences. Traditional movement prediction methods usually use hand-crafted features, which are computationally cheap but can only extract some shallow heuristic information. Deep learning-based methods have a stronger feature expression ability, but it is easy to fall into the dilemma of local features, resulting in poor generalization performance of the method. In this article, a human-exoskeleton interface fusing convolutional neural networks with hand-crafted features is proposed. On the basis of our previous study, a lower-limb movement prediction framework (HCSNet) in patients with hemiplegia is constructed by fusing time and frequency domain hand-crafted features and channel synergy learning-based features. An sEMG data acquisition experiment is designed to compare and analyze the effectiveness of HCSNet. Experimental results show that the method can achieve 95.93 and 90.37% prediction accuracy in both within-subject and cross-subject cases, respectively. Compared with related lower-limb movement prediction methods, the proposed method has better prediction performance.

Design and Implementation of a Rehabilitation Upper-limb Exoskeleton Robot Controlled by Cognitive and Physical Interfaces

This paper presents an upper limb exoskeleton that allows cognitive (through electromyography signals) and physical user interaction (through load cells sensors) for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury. For the exoskeleton to be easily accepted by patients who suffer from a neurological injury, we used the ISO9241-210:2010 as a methodology design process. As the first steps of the design process, design requirements were collected from previous usability tests and literature. Then, as a second step, a technological solution is proposed, and as a third step, the system was evaluated through performance and user testing. As part of the technological solution and to allow patient participation during the rehabilitation process, we have proposed a hybrid admittance control whose input is load cell or electromyography signals. The hybrid admittance control is intended for active therapy exercises, is easily implemented, and does not need musculoskeletal modeling to work. Furthermore, electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human-robot interaction.

MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram

The human-robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human-exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.

Channel Synergy-based Human-Robot Interface for a Lower Limb Walking Assistance Exoskeleton

The human-robot interface (HRI) based on surface electromyography(sEMG) can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. The sEMG signal of the paraplegic patients' lower limbs is weak. How to achieve accurate prediction of the lower limb movement of patients with paraplegia has always been the focus of attention in the field of HRI. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human-exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs a channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51% and 80.75% respectively.