On Neural Inertial Classification Networks for Pedestrian Activity Recognition

• URL: http://arxiv.org/abs/2502.17520v1
• Date: Sun, 23 Feb 2025 08:15:26 GMT
• Title: On Neural Inertial Classification Networks for Pedestrian Activity Recognition
• Authors: Zeev Yampolsky, Ofir Kruzel, Victoria Khalfin Fekson, Itzik Klein,
• Abstract summary: Inertial sensors are crucial for recognizing pedestrian activity.<n>Recent advances in deep learning have greatly improved inertial sensing performance and robustness.<n>Different domains and platforms use deep-learning techniques to enhance network performance, but there is no common benchmark.<n>The aim of this paper is to fill this gap by defining and analyzing ten data-driven techniques for improving neural inertial classification networks.
• Score: 2.374912052693646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inertial sensors are crucial for recognizing pedestrian activity. Recent advances in deep learning have greatly improved inertial sensing performance and robustness. Different domains and platforms use deep-learning techniques to enhance network performance, but there is no common benchmark. The latter is crucial for fair comparison and evaluation within a standardized framework. The aim of this paper is to fill this gap by defining and analyzing ten data-driven techniques for improving neural inertial classification networks. In order to accomplish this, we focused on three aspects of neural networks: network architecture, data augmentation, and data preprocessing. The experiments were conducted across four datasets collected from 78 participants. In total, over 936 minutes of inertial data sampled between 50-200Hz were analyzed. Data augmentation through rotation and multi-head architecture consistently yields the most significant improvements. Additionally, this study outlines benchmarking strategies for enhancing neural inertial classification networks.

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