|arXiv ID:||2012.08403v1||Title:||Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems||Language:||English||Authors:||Venzke, Marcus
Dell Missier, Jesper
|Keywords:||Computer Science - Learning; Computer Science - Learning; Computer Science - Neural and Evolutionary Computing; Machine Learning; Artificial Neural Networks; Embedded Systems; Hand Gesture Recognition||Issue Date:||15-Dec-2020||Source:||arXiv: 2012.08403v1 (2020)||Abstract (english):||
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.
|URI:||http://hdl.handle.net/11420/8295||Institute:||Telematik E-17||Document Type:||Preprint|
|Appears in Collections:||Publications without fulltext|
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