提出十种提升行人活动识别的神经网络方法并建立基准。
On Neural Inertial Classification Networks for Pedestrian Activity Recognition
- 研究网络结构、数据增强与预处理三方面改进方法。
- 旋转增强与多头架构使性能提升最显著。
- 适用于智能穿戴设备中的活动识别研究者。
惯性传感器在行人活动识别中至关重要。深度学习的进展显著提升了惯性感知的性能与鲁棒性。尽管不同领域和平台采用深度学习技术优化网络，但缺乏统一基准，制约了公平比较与评估。本文旨在填补这一空白，系统定义并分析了十种数据驱动的神经惯性分类网络优化技术。研究聚焦于网络架构、数据增强和数据预处理三个关键方面。实验基于4个来自78名参与者的数据集展开，共分析超过936分钟、采样频率为50-200Hz的惯性数据。结果表明，通过旋转增强和多头架构的数据增强策略，性能提升最为显著。此外，本文还提出了用于提升神经惯性分类网络的基准测试策略。
原文摘要 · Abstract (English)
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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