2020/12/15 by Marcus Venzke, Venzke, Marcus, Daniel Klisch +9 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Time Series Analysis and Forecasting #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2012.08403
openalex publication_date 2020/12/15 · openalex created_date 2020/12/21 · openalex updated_date 2026/07/28
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.