A novel gait-based synthesis procedure for the design of 4-bar exoskeleton with natural trajectories

A developed multibody knee model for unloading knee with cartilage penetration depth control

Unloader knee braces could relieve pain by decreasing the medial knee loading. Particularly for knee osteoarthritis (KOA) patients, this study investigates the relevance of the knee model after identifying the most influential parameter. Since KOA causes the cartilage in a joint to lose its elasticity and thickness, the lack of normal bone-to-bone separation can be painful. We believe that cartilage penetration depth control is an impactful strategy in this research. Moreover, the knee contact force in KOA is fewer than in healthy knees, confirming that the contact force control cannot be a straight factor. Therefore, a biomechanical human knee model is developed, and a generic procedure for dynamic analysis of contact problems in combination with the musculoskeletal model is introduced. The developed model includes the geometric expression of collision curves and an algorithm for determining collision points. This presentation addresses cartilage penetration depth and contact force calculation through nonlinear discontinuous contact law. In view of this, femur and tibia’s relative motion is analyzed through the combined collision reactions of cartilage and bone in the knee. In the simulation, maximum penetration depth in a healthy knee is reported to be 0.795 mm, while in a 75% KOA is 0.521 mm, including 0.5 mm cartilage-cartilage contact and 0.021 mm bone-bone contact. The top unloading 852 N is achieved, reducing penetration depth to 0.45 mm, avoiding bone-bone contact. This proposed procedure with low computation gives us a suitable analysis method for designing knee assistive devices.

Comparative Study on the Synthesis of Path-Generating Four-Bar Linkages Using Metaheuristic Optimization Algorithms

Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof’s criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems.

Neuronal Constraint-Handling Technique for the Optimal Synthesis of Closed-Chain Mechanisms in Lower Limb Rehabilitation

The optimal methods for the synthesis of mechanisms in rehabilitation usually require solving constrained optimization problems. Metaheuristic algorithms are frequently used to solve these problems with the inclusion of Constraint-Handling Techniques (CHTs). Nevertheless, the most used CHTs in the synthesis of mechanisms, such as penalty function and feasibility rules, generally prioritize the search for feasible regions over the minimization of the objective function, and it notably influences the exploration and exploitation of the algorithm such that it could induce in the premature convergence to the local minimum and thus the solution quality could deteriorate. In this work, a Neuronal Constraint-Handling (NCH) technique is proposed and its performance is studied in the solution of mechanism synthesis for rehabilitation. The NCH technique uses a neural network to search for the fittest solutions into the feasible and the infeasible region to pass them to the next generation of the evolutionary process of the Differential Evolution (DE) algorithm and consequently improve the obtained solution quality. Two synthesis problems with four–bar and cam–linkage mechanisms are the study cases for developing lower-limb rehabilitation routines. The NCH is compared with four state-of-the-art constraint-handling techniques (penalty function, feasibility rules, stochastic ranking, ϵ-constrained method) included into four representative metaheuristic algorithms. The irace package is used for both the algorithm settings and neuronal network training to fairly and meaningfully compare through statistics to confirm the overall performance. The statistical results confirm that, despite changes in the rehabilitation trajectories, the proposal presents the best overall performance among selected algorithms in the studied synthesis problems for rehabilitation, followed by penalty function and feasibility rule.

Mechanically Assisted Neurorehabilitation: A Novel Six-Bar Linkage Mechanism for Gait Rehabilitation

Repeated and intensive gait training can improve muscle strength and movement coordination of patients with neurological or orthopedic impairments. However, conventional physical therapy by a physiotherapist is laborious and expensive. Therefore, this therapy is not accessible for the majority of patients. This paper presents a six-bar linkage mechanism for human gait rehabilitation with a natural ankle trajectory. Firstly, a six-bar linkage mechanism is selected as the original mechanism to construct a gait rehabilitation device. Then the ankle trajectory is formulated as a function of the crank angle. And the rotation angle of the crank is set as a linear function of time. Therefore, constant speed motor is sufficient to control the mechanism. For the dimensional synthesis, the precise point distances of the gait trajectory and the coupler curve are set as objective functions, with the kinematic constraints including in the optimization procedure. To obtain the optimal structure design parameters, a cooperative dual particle swarm optimization algorithm is developed. The results show that the coupler curve matches well with the gait trajectory. The average distance between the 60 precision points is 3.5 mm.

The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis

Introduction: Although there is a need for rehabilitation treatment with the increase in the aging population, the shortage of skilled physicians frustrates this necessity. Robotic technology has been advocated as one of the most viable methods with the potential to replace humans in providing physical rehabilitation of patients with neurological impairment. However, because the pioneering robot devices suffer several reservations such as safety and comfort concerns in clinical practice, there is an urgent need to provide upgraded replacements. The rapid development of intelligent computing has attracted the attention of researchers concerning the utilization of computational intelligence algorithms for robots in rehabilitation. Areas covered: This article reviews the state of the art and advances of robotic neurorehabilitation with computational intelligence. We classified advances into two categories: mechanical structures and control methods. Prospective outlooks of rehabilitation robots also have been discussed. Expert opinion: The aggravation of global aging has promoted the application of robotic technology in neurorehabilitation. However, this approach is not mature enough to guarantee the safety of patients. Our critical review summarizes multiple computation algorithms which have been proved to be valuable for better robotic use in clinical settings and guide the possible future advances in this industry.