Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems

• URL: http://arxiv.org/abs/2012.08403v1
• Date: Tue, 15 Dec 2020 16:25:23 GMT
• Title: Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems
• Authors: Marcus Venzke, Daniel Klisch, Philipp Kubik, Asad Ali, Jesper Dell Missier and Volker Turau
• Abstract summary: We analyze the potential of artificial neural networks (ANNs) on low-cost micro-controllers with a few kilobytes of memory. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost micro-controllers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a high level of reliability. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs). We validate the developed ANNs in a case study of optical hand gesture recognition on an 8-bit micro-controller. The best reliability was found for an FFNN with two layers and 1493 parameters requiring an execution time of 36 ms. We propose a workflow to develop ANNs for embedded devices.

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