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Background/ObjectiveHuman walking involves the coordination of brain, nerves, and muscles. A disturbance in their coordination may result in gait disorder. The gait disorder may be treated through manually assisted gait training or with the aid of assistive devices/robotic devices. These robotic devices involve mechanisms which are synthesized using complex conventional procedures. Therefore, in this study, a new gait-based synthesis procedure is proposed, which simplifies the mechanism synthesis and helps to develop a mechanism which can be used in rehabilitation devices, bipeds, etc.MethodsThis article presents a novel procedure for the synthesis of 4-bar linkage using the natural gait trajectories. As opposed to the conventional synthesis procedures, in this procedure, a global reference frame is considered, which allows the use of hip trajectory while moving. Moreover, this method is divided into two stages, and five precision points are considered on the hip trajectory in each stage. In the first stage, the 4-bar linkage is designed, thereafter, the configurations of the linkage for the remaining precision points are determined in the second stage. The proposed synthesis procedure reduces the complexity involved in the synthesis and helps in the simplification of the problem formulation. A two-stage optimization problem is formulated for minimizing the error between the generated and desired hip trajectories. Two nature-inspired algorithms are used for solving the optimization problem. The obtained best results are presented, and the designed linkage is simulated in MATLAB.ResultsThe best design of the linkage is obtained using particle swarm optimization. The trajectories generated by the designed linkage using the proposed methodology can accurately track the desired path, which indicates that designed linkage can achieve all the orientations required during walking. The positions of a whole lower limb at all the desired precision points are demonstrated by stick diagram for one gait.ConclusionThe proposed methodology has reduced the complexity of synthesis procedures and used optimization techniques to obtain a feasible design of the mechanism. The stick diagram of the designed mechanism obtained using the proposed method indicates that the designed mechanism can walk smoothly. Hence, the designed mechanism can be used in the rehabilitation devices. Furthermore, a conceptual design of an exoskeleton knee is also presented.The Translational Potential of this ArticleMany hospitals and individuals have used the immobile and portable rehabilitation devices. These devices involve mechanisms, and the design of mechanism plays a vital role in the functioning of these devices; therefore, we have developed a new synthesis procedure for the design of the mechanism. Besides synthesis procedure, a mechanism is developed that can be used in the rehabilitation devices, bipeds, exoskeletons, etc., to benefit the society.

The set of measured data points acquired from the Coordinate Measuring Machine (CMM) need to be processed and analyzed for evaluating the form errors inside the manufactured components. This paper presents a modified algorithm of particle swarm optimization (MPSO) for assessing the form error from the set of coordinate measured data points. In the classical algorithm of the particle swarm optimization (PSO), the value of the candidate solution is updated from its existing value without actually comparing the value obtained in the consecutive iterations for fitness. This behaviour attributes to a lack of exploitation ability in the defined search space. The proposed algorithm generates new swarm position and fitness solution for the objective function through an improved and modified search equation based on a proposed heuristic step. In this step, the swarm searches around the best solution of the previous iteration for improving the swarm exploitation capability. The particle swarm uses greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for different types of form errors and then optimized using proposed MPSO. Five benchmark functions are used to prove the effectiveness of the modified algorithm, which is verified by comparing its solution and convergence with those obtained from the established algorithms namely PSO and genetic algorithm (GA). Finally, the result of the proposed algorithm for form error evaluation is compared with previous work and other established nature-inspired algorithms. The results demonstrate that the proposed MPSO algorithm is more efficient and accurate than the other conventional heuristic optimization algorithms. Furthermore, it is well suited for form error evaluation using CMM acquired data.

This paper proposes a novel hybrid teaching-learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teachinglearning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.

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