Agile gesture recognition for low-power applications: customisation for
  generalisation
Ying Liu Liucheng Guo Valeri A. Makarovc Alexander Gorbana Evgeny Mirkesa Ivan Y. Tyukin
Automated hand gesture recognition has long been a focal point in the AI
community. Traditionally, research in this field has predominantly focused on
scenarios with access to a continuous flow of hand's images. This focus has
been driven by the widespread use of cameras and the abundant availability of
image data. However, there is an increasing demand for gesture recognition
technologies that operate on low-power sensor devices. This is due to the
rising concerns for data leakage and end-user privacy, as well as the limited
battery capacity and the computing power in low-cost devices. Moreover, the
challenge in data collection for individually designed hardware also hinders
the generalisation of a gesture recognition model.
  In this study, we unveil a novel methodology for pattern recognition systems
using adaptive and agile error correction, designed to enhance the performance
of legacy gesture recognition models on devices with limited battery capacity
and computing power. This system comprises a compact Support Vector Machine as
the base model for live gesture recognition. Additionally, it features an
adaptive agile error corrector that employs few-shot learning within the
feature space induced by high-dimensional kernel mappings. The error corrector
can be customised for each user, allowing for dynamic adjustments to the
gesture prediction based on their movement patterns while maintaining the agile
performance of its base model on a low-cost and low-power micro-controller.
This proposed system is distinguished by its compact size, rapid processing
speed, and low power consumption, making it ideal for a wide range of embedded
systems.