User authentication through gait analysis is a promising application of discriminative neural networks -- particularly due to the ubiquity of the primary sources of gait accelerometry, in-pocket cellphones. However, conventional machine learning models are often too large and computationally expensive to enable inference on low-resource mobile devices. We propose that binarized neural networks can act as robust discriminators, maintaining both an acceptable level of accuracy while also dramatically decreasing memory requirements, thereby enabling on-device inference. To this end, we propose BiPedalNet, a compact CNN that nearly matches the state-of-the-art on the Padova gait dataset, with only 1/32 of the memory overhead.
@article{arxiv.2103.16609,
title = {Binarized Neural Networks for Resource-Constrained On-Device Gait Identification},
author = {Daniel J. Wu and Avoy Datta and Vinay Prabhu},
journal= {arXiv preprint arXiv:2103.16609},
year = {2021}
}