The tactile sensor design and data measurements have been playing an important role in surface material recognition. In inevitable unstructured environments, the performance of the material classification is bottlenecked by multimodal deficiency or data collection intermittency. This article investigates the influence of surface tactile data quantity on material classification in unstructured environments. We extracted tactile data features according to the material-surface-texture model and proposed an approach to determine the scope of the time window of tactile data. We utilized the machine learning classifiers to verify the effectiveness of the hand-designed features and the determined time window. Based on the combination of the Mel frequency cepstrum coefficient and statistics, the majority of the algorithms can classify the tactile data of the upper limit (60 ms in this article) with an accuracy of at least 85%. Exploiting tactile data of the lower limit (50 ms in this article), linear discriminant analysis, quadratic discriminant analysis, and support vector machine can achieve about 93% classification accuracy, and area under curve is about 0.99. The accuracy is not significantly improved with the time window beyond the upper limit, while the performance is degraded and unstable with it beneath the lower limit.

中文翻译：

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非结构化环境中表面触觉数据量对材料分类的影响


触觉传感器设计和数据测量在表面材料识别中发挥着重要作用。在不可避免的非结构化环境中，材料分类的性能因多模式缺陷或数据收集间歇性而受到瓶颈。本文研究了非结构化环境中表面触觉数据量对材料分类的影响。我们根据材料-表面-纹理模型提取触觉数据特征，并提出一种确定触觉数据时间窗口范围的方法。我们利用机器学习分类器来验证手工设计的特征和确定的时间窗口的有效性。基于梅尔频率倒谱系数和统计量的结合，大多数算法可以对上限（本文中为60 ms）的触觉数据进行分类，准确率至少为85%。利用下限（本文为50 ms）的触觉数据，线性判别分析、二次判别分析和支持向量机可以实现约93%的分类精度，曲线下面积约为0.99。当时间窗超过上限时，精度没有明显提高，而当时间窗低于下限时，性能下降且不稳定。