  Automated hand gesture recognition has been a focus of the AI community for
decades. Traditionally, work in this domain revolved largely around scenarios
assuming the availability of the flow of images of the user hands. This has
partly been due to the prevalence of camera-based devices and the wide
availability of image data. However, there is growing demand for gesture
recognition technology that can be implemented on low-power devices using
limited sensor data instead of high-dimensional inputs like hand images. In
this work, we demonstrate a hand gesture recognition system and method that
uses signals from capacitive sensors embedded into the etee hand controller.
The controller generates real-time signals from each of the wearer five
fingers. We use a machine learning technique to analyse the time series signals
and identify three features that can represent 5 fingers within 500 ms. The
analysis is composed of a two stage training strategy, including dimension
reduction through principal component analysis and classification with K
nearest neighbour. Remarkably, we found that this combination showed a level of
performance which was comparable to more advanced methods such as supervised
variational autoencoder. The base system can also be equipped with the
capability to learn from occasional errors by providing it with an additional
adaptive error correction mechanism. The results showed that the error
corrector improve the classification performance in the base system without
compromising its performance. The system requires no more than 1 ms of
computing time per input sample, and is smaller than deep neural networks,
demonstrating the feasibility of agile gesture recognition systems based on
this technology.
