Efficient light harvesting for accurate neural classification of human activities

Abstract

Energy autonomy extension of wearable devices is an ever increasing user need and it can be achieved by inexpensive energy harvesting from the broadly available solar and artificial light. However efficient conversion, relevant storage and utilization must be carefully implemented if the device supports power-hungry applications such as Artificial Intelligence for human activity classification based on Artificial Neural Networks. In this paper, a whole hardware and software system implementation is presented, which is able to achieve system autonomy extension and at the same time high classification accuracy. Quantitative and qualitative results are shown under real working conditions.