  In recent years, machine learning has developed rapidly, enabling the
development of applications with high levels of recognition accuracy relating
to the use of speech and images. However, other types of data to which these
models can be applied have not yet been explored as thoroughly. Labelling is an
indispensable stage of data pre-processing that can be particularly
challenging, especially when applied to single or multi-model real-time sensor
data collection approaches. Currently, real-time sensor data labelling is an
unwieldy process, with a limited range of tools available and poor performance
characteristics, which can lead to the performance of the machine learning
models being compromised. In this paper, we introduce new techniques for
labelling at the point of collection coupled with a pilot study and a
systematic performance comparison of two popular types of deep neural networks
running on five custom built devices and a comparative mobile app (68.5-89%
accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These
devices are designed to enable real-time labelling with various buttons, slide
potentiometer and force sensors. This exploratory work illustrates several key
features that inform the design of data collection tools that can help
researchers select and apply appropriate labelling techniques to their work. We
also identify common bottlenecks in each architecture and provide field tested
guidelines to assist in building adaptive, high-performance edge solutions.
