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人工智能生成的摔倒数据:评估可穿戴式摔倒检测的LLM和扩散模型

Sana Alamgeer¹, Yasine Souissi², Anne Ngu¹

¹Department of Computer Science, Texas State University, San Marcos, TX 78666, USA.

Sensors (Basel, Switzerland)

|August 28, 2025

概括

此摘要是机器生成的。

大型语言模型 (LLM) 可以生成合成落数据以改进落检测系统. 通过LLM生成的数据显示出有前途,特别是在低频设置中,但效率因数据集特征而异.

关键词:

扩散模型跌落探测器大型语言模型合成数据的生成文字转移生成文本到文本生成时间序列分析

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科学领域:

人工智能
生物医学工程
数据科学

背景情况:

由于缺乏真实情况的数据,特别是老年人群的数据,
合成数据生成提供了一个潜在的解决方案来增强有限的真实世界数据集.
评估先进的人工智能模型进行真实的数据模拟对于改善医疗技术至关重要.

研究的目的:

研究大型语言模型 (LLM) 在生成用于落检测系统的合成落数据方面的有效性.
将文本到动作和文本到文本LLM的性能与基于扩散的合成数据生成方法进行比较.
评估LLM生成的合成数据对长期短期记忆 (LSTM) 跌落检测模型的性能的影响.

主要方法:

使用各种LLM (GPT4o,GPT4,Gemini) 和文字转移模型 (SATO,ParCo) 生成合成落数据.
综合合成数据集与现实世界基线数据集用于训练和评估LSTM落检测模型.
将LLM生成的数据和基于扩散的合成数据与真实加速度计数据分布进行比较.

主要成果:

通过LLM生成的合成数据提高了降落检测性能,特别是在低频 (20 Hz) 设置中,但在高频 (200 Hz) 数据集中显示不稳定性.
文本对动作模型比文本对文本模型更具生物机械现实性的数据,尽管它们对检测的影响有所不同.
基于扩散的合成数据与真实数据分布非常相匹配,但并没有始终提高模型性能.

结论:

合成数据的有效性取决于数据集特征,传感器的位置和下降表现.
提供一种可行的方法来生成合成落数据,文本转移模型显示出实现现实的生物机械模拟的潜力.
需要进行进一步的研究,以优化合成数据生成策略,以便在各种条件下提供强大可靠的落检测系统.