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Updated: Apr 30, 2026

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一种基于惯性的时间序列生成的扩散模型,用于稀缺的数据可用性,以改善人类活动识别

Heiko Oppel1, Michael Munz2

  • 1AI for Sensor Data Analytics Research Group, Ulm University of Applied Sciences, Ulm, 89081, Germany. heiko.oppel@thu.de.

Scientific reports
|May 15, 2025
PubMed
概括

使用扩散模型的合成数据生成显著提高了人类活动识别 (HAR) 的准确性,即使现实数据有限. 这种方法通过为看不见的对象创建多样化,多IMU运动序列来提高分类性能.

关键词:
扩散模型的扩散模型人类活动识别 人类活动识别合成方式 合成方式时间序列时间序列

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