Du-Xin Liu

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

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

Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on -Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

Deep Spatial-Temporal Model for rehabilitation gait: optimal trajectory generation for knee joint of lower-limb exoskeleton

Purpose The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer. Design/methodology/approach In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features. Findings With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject. Originality/value Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

Deep rehabilitation gait learning for modeling knee joints of lower-limb exoskeleton

Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict the suitable gait trajectories of wearer. In this paper, we propose a Deep Rehabilitation Gait Learning (DRGL) for modeling the knee joints of lower-limb exoskeleton, which firstly leverage Long-Short Term Memory (LSTM) to learn the inherent spatial-temporal correlations of gait features. With DRGL, the abnormal knee joint trajectories can be predicted and corrected based on wearers other joints. This learning based method avoids gait analysis by building complex kinematic and dynamic models for human body and exoskeleton. More importantly, the new recovery gait pattern is not only in accordance with the healthy walking gait, but also including wearers own gait profile. To verify the effectiveness of DRGL, a new recovery gait is obtained from DRGL based on “pathological gait” which is obtained by a healthy subject imitating knee injury. Experiments demonstrate that the subject can walk normally with SIAT lower-limb exoskeleton in new recovery gait pattern.

Non-binding lower extremity exoskeleton (NextExo) for load-bearing

In this paper, we present a novel non-binding lower extremity exoskeleton (NextExo) for bearing load, where there is no binding point between the NextExo and human. With the innovative structure, the NextExo is able to stand in balance without attaching human, and bear the weights of its own and load completely. This also avoids the damage to operator caused by long-time binding. The NextExo has eight degrees of freedom, all of which are active joints powered by hydraulic actuators. It shadows human motion by one-to-one joints mapping. The man is as the core in the system to keep the NextExo in balance. Meanwhile, the constraint based on Zero Moment Point theory is adopted. The design concept, hardware structure, control scheme and preliminary experiments of NextExo are discussed.