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![Figure 2. Exoskeletons used in different applications [28-30].](publication/372424868/figure/fig1/AS:11431281175317378@1689684651258/Exoskeletons-used-in-different-applications-28-30_Q320.jpg)
Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human–machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we presen...
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... Designs with Other Optimization Techniques (Non-Evolutionary) Table 4 reports the list of non-EC techniques used in the studies we evaluated, along with their abbreviation and a reference to the original article where the method was proposed, whereas Table 5 shows the studies in which non-EC techniques were used to optimize force transmission, workspace, compliance, weight, and size. We observed that the most commonly used non-EC techniques are the interior point algorithm, the Levenberg-Marquardt algorithm, the Simplex algorithm, Pareto local search, the goal attainment method, and geometric differentiation. ...Citations
... Designing of walking robot leg-linkage with foot center tracing along straight-line has certain advantages, considering first of all energy efficiency and simplified control [41][42][43][44][45][46][47][48][49][50]. The prototype of horizontal propulsion mechanism designed for the legged robot is shown in Fig. 1a with foots F1 and F2 on support phase (on ground) and F3 and F4 on transfer phase (foot swing phase) [51][52]. ...
In the ever-evolving domain of robotic locomotion, leg linkage design emerges not just as an intricate engineering puzzle, but also as a decisive element in realizing optimal horizontal propulsion. The research meticulously interrogates this pivotal concern, endeavoring to harmonize multiple design objectives that traditionally exist in tension. Leveraging the robust computational prowess of the Non-dominated Sorting Genetic Algorithm (NSGA) for multiobjective optimization, this study orchestrates a deliberate foray into the expansive and complex design space. The overarching aim is not merely to pinpoint a singular, universal design zenith, but to painstakingly chart a continuum of Pareto-optimal solutions, thereby accommodating the myriad, often contradictory, imperatives that animate robotic design—from the quest for energy efficiency to the pursuit of agility, speed, and robust structural integrity. This methodology yields a rich tapestry of insights: notable among them is the discernible predilection of specific linkage configurations towards distinct performance outcomes. While certain geometries resonate more profoundly with rapid, fluid motion, others evince a marked inclination towards stability or frugal energy consumption. By dissecting these intricate relationships, and presenting them within a structured framework, this study contributes profoundly to the literature, offering both theoretical depth and pragmatic design templates to the robotics community. This synergistic marriage of computational algorithms with nuanced design challenges holds the promise to significantly recalibrate and enhance contemporary paradigms in leg linkage design for horizontally propelling robots. This study marks a significant advancement in robotic locomotion by employing the Non-dominated Sorting Genetic Algorithm (NSGA) for the first time in the optimization of leg linkage design for walking robots, providing a more nuanced understanding of the balance between structural integrity, energy efficiency, and propulsion agility. Our research elucidates a spectrum of Pareto-optimal solutions, a novel approach that offers a comprehensive understanding of the trade-offs involved in leg linkage design. Specifically, the optimized designs achieved an improvement in propulsion efficiency by reducing the approximation error to less than 0.006, and enhancing force transmission angles to over 25 degrees. These experimental results validate the practical applicability of these designs, demonstrating a balance of improved efficiency and stability, thereby setting a new benchmark for leg linkage design in walking robots. The findings underscore the potential of NSGA in robotic design, offering a robust framework for future advancements in the field.
While considerable research has been conducted, the field of neurodesign and human-computer interaction (N-HCI) has yet to be methodically delineated in light of contemporary studies. In this context, this paper undertakes a systematic review with dual objectives: (a) to uncover emerging research themes within N-HCI, and (b) to provide future directions for research on N-HCI. Employing robust systematic review methodologies, this paper scrutinized 122 pertinent documents, revealing four principal emerging research themes and constructing a conceptual framework that synthesized key concepts in N-HCI. The results underscored the relative underdevelopment in research that leverages neural evidence to enhance general human-computer interface design, and the notable absence of research at the intersections of the “user” and “environment” aspects of neurodesign with HCI. Accordingly, this paper identifies underdeveloped areas in existing research that require more depth and blank areas in the conceptual framework that call for exploration as future directions.
Variability is a fact of life. Variability is variations that occur in Human performance after multiple repetitions. The central concept of behavioral flexibility in motor control was presented by Bernstein when he stated that movements are a “repetition without repetition” to describe how, well-learned movements, show variation when achieving the task outcome. Handwriting is an example of a complex task that results from a sequence of movements. It has a specific variability structure, and temporal organization, that inform the regularity with which children write as well as their adaptability to the task, e.g., a fractal dynamics behavior. Movement analysis using nonlinear dynamical systems theory for human behavior provides a better understanding of the execution of pathologies, psychomotor problems, or problems in motor control. Dynamic Systems theory suggests that biological systems self-organize according to the environment, and biomechanical and morphological constraints to find the most stable solution for producing a given movement. The concepts of variability and chaotic variation in human movement, along with advanced tools used to measure human movement variability open new perspectives to guide practice and a fundamental complementary means of diagnosis.
