用扩散模型生成手机位置识别的惯性数据，减少真实采集负担
Diffusion-Driven Inertial Generated Data for Smartphone Location Classification
- 用扩散模型生成特定力数据，模拟不同手机摆放状态
- 合成数据在多指标上媲美真实数据，支持有效训练
- 适合需要大量惯性数据但难采集的定位研究者
尽管惯性测量在运动追踪与导航系统中至关重要，但收集大量惯性数据耗时且资源密集，制约了该领域机器学习模型的发展。近年来，扩散模型作为一类革命性生成模型，显著提升了人工数据生成能力，优于生成对抗网络等先进方法。本文提出基于扩散模型生成特定力数据，用于智能手机位置识别。通过多项指标对比合成数据与真实记录的特定力数据，结果表明，所提扩散生成模型能有效捕捉不同手机放置条件下特定力信号的特征。通过生成多样且逼真的合成数据，可在降低数据采集成本的同时，为机器学习模型提供高质量训练数据。
原文摘要 · Abstract (English)
Despite the crucial role of inertial measurements in motion tracking and navigation systems, the time-consuming and resource-intensive nature of collecting extensive inertial data has hindered the development of robust machine learning models in this field. In recent years, diffusion models have emerged as a revolutionary class of generative models, reshaping the landscape of artificial data generation. These models surpass generative adversarial networks and other state-of-the-art approaches to complex tasks. In this work, we propose diffusion-driven specific force-generated data for smartphone location recognition. We provide a comprehensive evaluation methodology by comparing synthetic and real recorded specific force data across multiple metrics. Our results demonstrate that our diffusion-based generative model successfully captures the distinctive characteristics of specific force signals across different smartphone placement conditions. Thus, by creating diverse, realistic synthetic data, we can reduce the burden of extensive data collection while providing high-quality training data for machine learning models.
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