arXiv ID: 2012.08403v1
Title: Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems
Language: English
Authors: Venzke, Marcus  
Klisch, Daniel 
Kubik, Philipp 
Ali, Asad 
Dell Missier, Jesper 
Turau, Volker 
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.
Institute: Telematik E-17 
Document Type: Preprint
Appears in Collections:Publications without fulltext

Show full item record

Google ScholarTM


Add Files to Item

Note about this record


Items in TORE are protected by copyright, with all rights reserved, unless otherwise indicated.