Designing a Sensor Glove Using Deep Learning

When designing a smart glove for gesture recognition, the set of sensors available and their layout on the glove are crucial. However, once a computational model reaches acceptable recognition accuracy, it is often not clear which sensors are more important for the task. Nor whether some sensors can be strategically removed while retaining similar performance in order to save cost. Furthermore, when aiming for a personalized setup, there can be minor deviation in how gestures are performed by each participant, and so the importance of a sensor may vary between participants. In this paper, we use feature selection to explore whether a personalised glove can be produced, and whether the set of significant sensors persist between users. We present a deep learning algorithm which utilises a layer of weights to estimate the importance of each sensor in relation to each other. Besides estimating importance in relation to recognition accuracy, it is demonstrated how the importance estimates can be extended to take into account factors external to the computational model, such as costs. This allows for a cost effective elimination of sensors to reduce hardware redundancy whilst having a controlled impact on performance. We provide 2 methods: generic or specific. The generic method exploits the importance estimate from all participants to select a set of sensors for removal. Whereas the specific method estimates importance, and removes sensors based on individuals to provide a personalised setup.