Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhancing the accuracy of decoding methods, often overlooking the magnitude and computational resource demands of deep learning models. In this study, we propose a novel lightweight Multi-Scale Feature Residual Convolutional Neural Network (MFRC-Net). MFRC-Net primarily consists of two blocks: temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks. The former captures dynamic changes in EEG signals across various time scales through multi-scale grouped convolution and backbone temporal convolution skip connections; the latter improves local spatial feature extraction and calibrates feature mapping through the introduction of cross-domain spatial filtering layers. Furthermore, by specifically optimizing the loss function, MFRC-Net effectively reduces sensitivity to outliers. Experiment results on the BCI Competition IV 2a dataset and the SHU dataset demonstrate that, with a parameter size of only 13 K, MFRC-Net achieves accuracy of 85.1% and 69.3%, respectively, surpassing current state-of-the-art models. The integration of temporal multi-scale residual convolution blocks and cross-domain dual-stream spatial convolution blocks in lightweight models significantly boosts performance, as evidenced by ablation studies and visualizations.

Personalized robotic exoskeleton control is essential in assisting individuals with motor deficits. However, current research still lacks a solution from the end of a practical need of the problem to the end of its successful demonstration in physical environments, namely an end-to-end solution, that enables stable and continuous walking across different tasks. This study addresses this challenge by introducing a hierarchical control framework for the purpose. At the low level, impedance control ensures joint compliance without causing injury to users. At the high level, a reinforcement learning (RL)-based optimal adaptive controller automatically personalizes assistance to both hip extension and flexion (namely, bi-directional) to reach a target range of motion (ROM) under multiple walking conditions. As the first potentially feasible approach to this challenging problem and to meet practical use requirements, we developed a least-square policy iteration-based solution to configure the intrinsic parameters within the well-established finite state machine impedance control (FSM-IC). We successfully tested the control solution on eight young unimpaired participants and one participant post-stroke wearing a hip exoskeleton while walking on an instrumented treadmill. The proposed method can be applied to solving for optimal impedance parameters for individual users and different task scenarios to increase joint ROM. Our next step is to further evaluate this solution framework on additional people with hemiparesis who may benefit from hip joint assistance in therapy or daily activities to restore normative or improve gait patterns.

Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint.

Human-in-the-loop (HIL) optimization usually optimizes assistive torque of exoskeletons to minimize the human's energetic expenditure in walking, quantified by metabolic cost. This formulation can, however, result in altered gait pattern of the human joint from the natural pattern, which is undesired. In this paper, we proposed a novel concept of HIL optimization of a hip exoskeleton. The optimization goal was to maintain the hip kinematics while providing optimal mechanical energy from the exoskeleton by modulating the admittance control. Policy iteration was used to optimize the switching time within the gait phase, at which a single parameter of the admittance controller was altered to provide assistance. The stiffness and equilibrium angle were considered as the two parameters for altering at the switching time, resulting in three possible modes of operation for the algorithm: (i) switching the equilibrium point, (ii) switching stiffness while equilibrium point is set at maximum extension and, (iii) maximum flexion. The optimization algorithm was found to converge for all three modes, with the equilibrium mode resulting in multiple solutions. Further analysis of power injected by the exoskeleton in the three modes showed that the first and third mode reduced human energetic exertion while the second mode increased human exertion. Implications of the results as well as the observed muscle activation patterns in response to assistance are discussed.

Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel data-driven solution framework based on reinforcement learning (RL), without first modeling the human-robot dynamics, to provide optimal and adaptive personalized torque assistance for reducing human efforts during walking. Our automatic personalization solution framework includes the assistive torque profile with two control timing parameters (peak and offset timings), the least square policy iteration (LSPI) for learning the parameter tuning policy, and a cost function based on a transferred work ratio. The proposed controller was successfully validated on a healthy human subject to assist unilateral hip extension in walking. The results showed that the optimal and adaptive RL controller as a new approach was feasible for tuning assistive torque profile of the hip exoskeleton that coordinated with human actions and reduced activation level of hip extensor muscle in human.

Nerve injury can cause lower limb paralysis and gait disorder. Currently lower limb rehabilitation exoskeleton robots used in the hospitals need more power to correct abnormal motor patterns of stroke patients’ legs. These gait rehabilitation robots are powered by cumbersome and bulky electric motors, which provides a poor user experience. A newly developed gait rehabilitation exoskeleton robot actuated by low-cost and lightweight pneumatic artificial muscles (PAMs) is presented in this research. A model-free proxy-based sliding mode control (PSMC) strategy and a model-based chattering mitigation robust variable control (CRVC) strategy were developed and first applied in rehabilitation trainings, respectively. As the dynamic response of PAM due to the compressed air is low, an innovative intention identification control strategy was taken in active trainings by the use of the subject’s intention indirectly through the estimation of the interaction force between the subject’s leg and the exoskeleton. The proposed intention identification strategy was verified by treadmill-based gait training experiments.

To regain mobility, stroke patients need to receive repetitive and intensive therapy. Robot-assisted rehabilitation is an active area of research. Cheap robotic leg rehabilitation devices should be developed to meet the demands and assist most patients. A low cost hip-knee exoskeleton prototype powered by pneumatic muscles was developed. On this basis, Functional Electrical Stimulation (FES) induced paralyzed muscles to realize ankle joint rehabilitation training. These three ankle muscles: the tibialis anterior, the soleus, and the gastrocnemius under electrical stimulation cooperated together to realize optimally coordinated control of dorsiflexion and plantar-flexion movement. As both of pneumatic muscle and FES induced muscle possess highly nonlinear characteristics, a sliding control algorithm called Chattering mitigation Robust Variable Control (CRVC) was applied to leg hybrid rehabilitation. The combination of exoskeleton and FES is a promising way to reduce the cost and the complexity of designing hip-knee-ankle exoskeleton. The proposed hybrid method was verified by treadmill-based gait training experiments.

A wearable, portable, low-cost, and easy-to-use upper extremity exoskeleton robot, RUPERT, is presented here for clinical and in-home therapies of patients who have survived a stroke. The robot system has five degrees-of-freedom and is driven by compliant and safe pneumatic muscles. Its primary function is to assist the movement of an affected arm in 3-D space and perform a daily training program in a virtual environment. Subjects are recruited for experiments of both clinical and in-home therapies. While using RUPERT, most subjects exhibited significant improvements in various functional measures. During their in-home therapy trials, subjects expressed enthusiasm regarding the modules and the use of our robotic system. The experimental results look promising and the proposed robotic system exhibits a good prospect for a future commercialized in-home therapy product.