Search

Accurate and fast human motion pattern recognition is the key to ensuring lower limb assistive devices' appropriate assistance. The research on human motion pattern recognition of lower limb assistive devices mainly focuses on sagittal gait. The motion pattern such as circular walking (CW) is asymmetric about the sagittal plane of the body. CW is common in daily living. However, the recognition algorithm of CW is rarely reported. Since lower limb assistive devices interact with humans, lacking the capability of recognizing CW is dangerous. Thus, to realize the accurate and fast recognition of CW, this article proposed a finite class Bayesian interference system (FC-BesIS). FC-BesIS is designed to recognize walking activities (linear walking and CW) and gait events (heel contact, load response, mid stance, terminal stance, pre-swing, initial swing, mid swing, and terminal swing). A finite class method which reduces the number of potential classes according to elimination rules before decision-making is introduced. Elimination rules are designed based on likelihood estimation and sensor information. The experiments show that walking activities and gait events can be accurately and fastly recognized by FC-BesIS. The experiments also show that the performance of FC-BesIS in mean recognition accuracy (MRA) and mean decision time (MDT) is improved compared with BesIS. The MRA of walking activities and gait events are 100% and 97.38%, respectively. The MDT of walking activities and gait events are 28.19 ms and 33.94 ms, respectively. Overall, FC-BesIS has been proved to be an accurate and fast recognition algorithm for human motion patterns using wearable sensors.

The condition of active assistance is the basic design criteria of target assistance torque profile. Based on the human-exoskeleton interaction model, the condition of active assistance was derived first. And then, some biomechanical simulation experiments based on OpenSim were carried out to demonstrate the the condition of active assistance. The condition of active assistance for exoskeleton robot is that the target assistance torque and the human joint muscle torque should be in a same direction and the assistance ratio should be greater than 0 and less than 1.

Heel-strike (HS) and toe-off (TO) events of the human walking are the basis for gait analysis. The gait event detection algorithm is applied in the fields of spatio-temporal parameter analysis, rehabilitation, and wearable auxiliary devices. Based on the shank’s absolute angular velocity in the sagittal plane, offline/online detection of HS-TO for normal walking, incline walking and other gaits in sagittal plane can be achieved. This type of detection algorithms assume that the motion occurs only in the sagittal plane, and the motion characteristics of the left and right shanks in one gait cycle are consistent. However, the effectiveness of this type of gait event detection algorithms for the turning gait is unclear. The turning gait in daily life is frequent and inevitable. Ignoring the gait event detection for the turning gait will limit the application of the existing gait event detection algorithms. To this end, this paper compared the degree of symmetry of the absolute angular velocity of the left and right shanks under normal walking and turning gait. The validity of a typical HS-TO detection algorithm based on shank joint angular velocity is analyzed, and the necessity of studying the turning gait event detection algorithm is demonstrated.

The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton&rsquo, s controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.

With the increasing prevalence of lower limb dyskinesia, it is urgent to study exoskeleton devices that can help patients walk normally. Combined with the passive exoskeleton which is rarely studied at present, aiming at the difficulty of walking in the patients with lower limb dyskinesia, this paper makes a simulation study on the metabolism of human muscles at this stage. Firstly, we analyzed the biomechanical characteristics and metabolism of human body during normal walking with a human motion simulation software, OpenSim, and determined the position and stage of the assist, and added the passive exoskeleton equivalent model to the human body model. Then we compared the metabolism of human body with different passive assist stiffness and determined a stiffness which can reduce the body stride metabolism by 18%. Finally, we analyzed the changes of the force of related muscles with and without assist, and optimized the assist stage. After simulation analysis, the loading of passive exoskeleton can reduce the metabolism of human body by 19.47%.

The passive exoskeleton has attracted more and more attention from researchers because of its small weight and portability. However, research on the passive exoskeleton of the hip joint during walking is rarely reported. In order to expand the application of passive exoskeletons in walking assist, this article analyzed human biomechanical characteristics through OpenSim and conducted passive assist research of hip joints based on the characteristics of muscle metabolism. First, starting from the biomechanical properties of the human body, the muscles and their functions related to hip flexion/extension were identified. Further, through the study of muscle metabolism, the support phase was used as the assist phase, and a variable-stiffness hip passive assisted exoskeleton was added to the simplified human musculoskeletal model. Finally, through the OpenSim simulation, the total metabolism of the support phase at different stiffness and the metabolism of the muscles which drive the hip joint were compared. The effect of changes in the stiffness of passive exoskeleton on human metabolism was studied, and a stiffness which is capable of reducing metabolism by 12.84% was obtained. On this basis, the stage of assist was optimized based on the muscle force with/without assist, and the metabolism was further reduced by 21.56%.

Foot placement plays an important role in human stable walking. Utilizing the linear inverted pendulum model and the capture point theory, the laws of foot placement and two stable walking conditions are derived in this paper. These stable walking conditions are demonstrated by the optical motion capture experiment. The experiment not only shows that the foot placement during walking is within a capture region and locates on an equal velocity point, but also demonstrates the foot placement of human during stable walking can be predicted by the stable condition derived in this paper.

A novel balance assistance control strategy of a hip exoskeleton robot was proposed in this paper. The organic fusion of the human balance assessment and the exoskeleton balance assistance control strategy are the assurance of balance recovery. However, currently there are few human balance assessment methods that are suitable for detecting balance loss during standing and walking, and very little research has focused on exoskeleton balance recovery control. In this paper, a single step balance assessment method was proposed first, and then based on this method an "assist-as-needed" balance assistance control strategy was established. Finally, the exoskeleton balance assistance control experiment was carried out. The experiment results verified the effectiveness of the single balance assessment method and the active balance assistance control strategy.