In this paper, we propose to use brain-computer interface (BCI) to control a lower-limb exoskeleton. The exoskeleton follows the wearer's motion intention through decoding of electroencephalography (EEG) signals and multi-modal cognition. Motion patterns as standing up, sitting down, and walking forward can be performed. We implemented two types of BCIs, one based on steady-state visual evoked potentials, which used canonical correlation analysis to extract the frequency the subject focused on. The other BCI is based on motor imagery, where the common spatial patterns method was employed to extract the features from the EEG signal. Then, the features were classified by support vector machine to recognize the intention of the subject. We invited four healthy subjects to participate in the experiments, including off-line and online. The off-line experiments trained the classifier and then were used online to test the performance of the BCI controlled exoskeleton system. The results showed high accuracy rate in motion intention classification tasks for both BCIs.

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

Gait phase estimation is important technology in controlling the exoskeleton robot to assist elderly walking. Several kinds of Gait estimation methods have been proposed, however, the previously proposed methods were mainly aiming at one kinds of walking task e.g. level ground walking. There are only a few studies aiming at continuous gait phase estimation during continuous multi-locomotion tasks. In this paper, we design a continuous gait phase estimator based on adaptive oscillator network. In order to overcome the problem that the traditional adaptive oscillator does not converge or converge slowly when the gait task is switching, a new structure of gait phase estimator including a gait tasks classifier, an adaptive oscillator reset, a peak detector and a model-based transition gait phase estimator is designed to improve the performance of adaptive oscillators network. The switching unit is designed to reorganize the output gait phase. Considering the stabilization of the sensors in continuous multi-locomotion tasks, the gait tasks classifier only utilizes the angle of hip joints. The results show that the constructed classifier has similar performance to other gait tasks classifier and requires minimum of sensing sources. The continuous gait phase estimation results during continuous multi-locomotion tasks show that the proposed method has better performance than the traditional adaptive oscillator and the adaptive oscillator network with self-designed reset.

In the lower limb exoskeleton assistance system, motion intention understanding based on biological signals is the human-computer interface’s (HMI) key technology. Due to the close coupling of human and machine, wheelchair-type control modes (combination of motion intentions and switches of different actions) are not applicable. For example, the right/left-hand motor imagery (MI) controls the exoskeleton to move forward/stop. There is a mismatch between MI-based control instructions and actual actions because the forward process alternates between left and right. Therefore, this study aims to design a reasonable HMI for a lower limb exoskeleton assistance system and to prove its rationality and effectiveness. We have designed a new HMI combining the human walking process and multimodal biosignals. Experimental data were collected from 14 subjects. We use feature-based and end-to-end analysis methods to analyze multimode signals. In a feature-based analysis, the advantage of using sEMG to distinguish between different movements does not apply to distinguishing different magnitudes of the same movement. A time-frequency subband selection method based on difference patterns is designed for EEG signals in the feature-based analysis. The fusion of EEG and sEMG is implemented to complement each other. The average recognition rate of the multimodal HMI was evaluated to be up to 89.5%, which is 12% and 7.8% higher than that of EEG and sEMG, respectively. Based on end-to-end analysis, a time-domain translation strategy was used to expand the sample and obtain a cross-subject identification rate of 83.6%. All the results indicate that the designed HMI is rational and effective, and there exists potential for practical application.

This paper presents a bioinspired lower extremity exoskeleton robot. The proposed exoskeleton robot can be adjusted in structure to meet the wearer’s height of 150–185 cm and has a good gait stability. In the gait control part, a method of identifying different locomotion modes is proposed; five common locomotion modes are considered in this paper, including sitting down, standing up, level‐ground walking, ascending stairs, and descending stairs. The identification is depended on angle information of the hip, knee, and ankle joints. A deep locomotion mode identification model (DLMIM) based on long‐short term memory (LSTM) architecture is proposed in this paper for exploiting the angle data. We conducted two experiments to verify the effectiveness of the proposed method. Experimental results show that the DLMIM is capable of learning inherent characteristics of joint angles and achieves more accurate identification than the other models. The last experiment demonstrates that the DLMIM can recognize transitions between different locomotion modes in time and the real‐time performance varies with each individual.

The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.