On Neural Inertial Classification Networks for Pedestrian Activity Recognition

Zeev Yampolsky, Ofir Kruzel, Victoria Khalfin Fekson, Itzik Klein

Peer Reviewed

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. Index Terms—Inertial sensing, Deep-learning, Data augmentation, Human Activity Recognition
Published in:	2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) April 28 - 1, 2025 Salt Lake Marriott Downtown at City Creek Salt Lake City, UT
Pages:	15 - 22
Cite this article:	Yampolsky, Zeev, Kruzel, Ofir, Fekson, Victoria Khalfin, Klein, Itzik, "On Neural Inertial Classification Networks for Pedestrian Activity Recognition," 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS), Salt Lake City, UT, April 2025, pp. 15-22.
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