  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.
