2,734 research outputs found

    New developments in prosthetic arm systems

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    Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold - insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods

    How to use analogies for breakthrough innovations

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    Analogies can trigger breakthrough ideas in new product development. Numerous examples demonstrate that substantial innovations often result from transferring problem solutions from one industry or domain to another. For instance, the designers of the new running shoe generation of Nike, Nike SHOX, use the same suspension concept like the technologies applied for Formula 1 racing cars, or the biological Lotus-effect led to the development of various self-cleaning surfaces. Academic research on analogical thinking has been so far heavily influenced by general theoretical work from cognitive psychology or systematic inventing. Only a small number of studies have investigated the application of analogies in the specific context of breakthrough innovation projects. This paper focuses on the question how analogies can be systematically used in the early innovation phases of new product development and which factors influence the successful use of analogical thinking in innovating companies. Special attention is paid to organizational facilitators and the requests on people involved in this process. --

    Totally Corrective Multiclass Boosting with Binary Weak Learners

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    In this work, we propose a new optimization framework for multiclass boosting learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two successful multiclass boosting algorithms, which can use binary weak learners. We explicitly derive these two algorithms' Lagrange dual problems based on their regularized loss functions. We show that the Lagrange dual formulations enable us to design totally-corrective multiclass algorithms by using the primal-dual optimization technique. Experiments on benchmark data sets suggest that our multiclass boosting can achieve a comparable generalization capability with state-of-the-art, but the convergence speed is much faster than stage-wise gradient descent boosting. In other words, the new totally corrective algorithms can maximize the margin more aggressively.Comment: 11 page

    Neural-Like Growing Networks in Intelligent System of Recognition of Images

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    The neural-like growing networks used in the intelligent system of recognition of images are under consideration in this paper. All operations made over the image on a pre-design stage and also classification and storage of the information about the images and their further identification are made extremely by mechanisms of neural-like networks without usage of complex algorithms requiring considerable volumes of calculus. At the conforming hardware support the neural network methods allow considerably to increase the effectiveness of the solution of the given class of problems, saving a high accuracy of result and high level of response, both in a mode of training, and in a mode of identification
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