In this letter, we propose a multivariate time-series classification system that fuses multirate sensor measurements within the latent space of a deep neural network. In our network, the system identifies the surface category based on audio and inertial measurements generated from the surface impact, each of which has a different sampling rate and resolution in nature. We investigate the feasibility of categorizing ten different everyday surfaces using a proposed convolutional neural network, which is trained in an end-to-end manner. To validate our approach, we developed an embedded system and collected 60 000 data samples under a variety of conditions. The experimental results obtained exhibit a test accuracy for a blind test dataset of 93%, taking less than 300 ms for end-to-end classification in an embedded machine environment. We conclude this letter with a discussion of the results and future direction of research.

中文翻译：

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基于多速率传感器与深度神经网络融合的嵌入式表面识别

在这封信中，我们提出了一个多元时间序列分类系统，该系统融合了深度神经网络潜在空间内的多速率传感器测量。在我们的网络中，系统根据表面撞击产生的音频和惯性测量值来识别表面类别，每个测量值本质上具有不同的采样率和分辨率。我们研究了使用提议的卷积神经网络对十个不同的日常表面进行分类的可行性，该网络以端到端的方式进行训练。为了验证我们的方法，我们开发了一个嵌入式系统并在各种条件下收集了 60 000 个数据样本。获得的实验结果表明，盲测数据集的测试精度为 93%，在嵌入式机器环境中进行端到端分类的时间不到 300 毫秒。