We have developed tactile sensor systems for next-generation robots. To install a large number of tactile sensors, we have proposed micro electro mechanical systems-large-scale integration (MEMS-LSI) integrated tactile sensors. The integrated device has the following features: capacitive type three-axis force sensing, embedded diode-based temperature sensing, signal processing for sensing data digitalization, and event-driven response for efficient serial bus communication. This paper demonstrates a sensor array system as up-to 40 integrated tactile sensors which are connected on one bus line. After acquiring the sensing data from the sensor array system, we applied a machine learning technique for target object judgment. The objective of the judgment is to classify the targets into normal object and abnormal object. With the sensor array system, data preprocessing and tuned Recurrent Neural network (RNN)/Long Short-Term Memory (LSTM) neural network models, we achieved high-accuracy, high-precision, and high-recall scores for the experiment of the judgment.

中文翻译：

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使用事件驱动的 MEMS-LSI 集成触觉传感器阵列系统进行物体判断

我们为下一代机器人开发了触觉传感器系统。为了安装大量触觉传感器，我们提出了微机电系统-大规模集成（MEMS-LSI）集成触觉传感器。该集成器件具有以下功能：电容式三轴力感测、基于嵌入式二极管的温度感测、用于感测数据数字化的信号处理以及用于高效串行总线通信的事件驱动响应。本文演示了一种传感器阵列系统，最多可连接 40 个集成触觉传感器，这些传感器连接在一条总线上。从传感器阵列系统获取传感数据后，我们应用机器学习技术来判断目标物体。判断的目的是将目标分为正常对象和异常对象。通过传感器阵列系统，