Binarized Neural Networks for Resource-Constrained On-Device Gait Identification
This addresses the challenge of user authentication through gait analysis for mobile device users, but it is incremental as it applies an existing binarization technique to a specific domain.
The paper tackled the problem of enabling gait identification on low-resource mobile devices by proposing binarized neural networks, achieving near state-of-the-art accuracy with only 1/32 of the memory overhead on the Padova gait dataset.
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.