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首页> 外文期刊>Assembly Automation >Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network
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Hand gesture recognition using low-budget data glove and cluster-trained probabilistic neural network

机译：使用低预算数据手套和集群训练概率神经网络进行手势识别

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Purpose - Main purpose is to present methodology which allows efficient hand gesture recognition using low-budget, 5-sensor data glove. To allow widespread use of low-budget data gloves in engineering virtual reality (VR) applications, gesture dictionaries must be enhanced with more ergonomic and symbolically meaningful hand gestures, while providing high gesture recognition rates when used by different seen and unseen users. Design/methodology/approach - The simple boundary-value gesture recognition methodology was replaced by a probabilistic neural network (PNN)-based gesture recognition system able to process simple and complex static gestures. In order to overcome problems inherent to PNN -primarily, slow execution with large training data sets - the proposed gesture recognition system uses clustering ensemble to reduce the training data set without significant deterioration of the quality of training. The reduction of training data set is efficiently performed using three types of clustering algorithms, yielding small number of input vectors that represent the original population very well. Findings - The proposed methodology is capable of providing efficient recognition of simple and complex static gestures and was also successfully tested with gestures of an unseen user, i.e. person who took no part in the training phase. Practical implications - The hand gesture recognition system based on the proposed methodology enables the use of affordable data gloves with a small number of sensors in VR engineering applications which require complex static gestures, including assembly and maintenance simulations. Originality/value - According to literature, there are no similar solutions that allow efficient recognition of simple and complex static hand gestures, based on a 5-sensor data glove.
机译：目的-主要目的是提出一种使用低成本的5传感器数据手套进行有效手势识别的方法。为了允许在工程虚拟现实（VR）应用程序中广泛使用低预算的数据手套，必须使用更符合人体工程学和具有象征意义的手势来增强手势词典，同时当不同的可见和不可见用户使用时，手势识别率也要高。设计/方法/方法-简单的边界值手势识别方法已被基于概率神经网络（PNN）的手势识别系统所取代，该系统能够处理简单和复杂的静态手势。为了克服PNN固有的问题-首先是大型训练数据集的缓慢执行-所提出的手势识别系统使用聚类集成减少了训练数据集，而训练质量没有明显下降。使用三种类型的聚类算法可以有效地减少训练数据集，从而产生少量输入向量，它们很好地代表了原始种群。结果-所提出的方法能够有效识别简单和复杂的静态手势，并且还成功地用了看不见的用户（即未参加培训阶段的人）的手势进行了测试。实际意义-基于所提出方法的手势识别系统可以在需要复杂静态手势（包括组装和维护模拟）的VR工程应用中使用带有少量传感器的价格合理的数据手套。原创性/价值-根据文献，没有类似的解决方案可以基于5传感器数据手套有效识别简单和复杂的静态手势。

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