采样频率对机器学习对人类活动识别的影响,以临床应用为目标
Takahiro Yamane1, Moeka Kimura2, Mizuki Morita1,2
1Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama 700-8530, Japan.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
将加速度计数据采样频率降低到10 Hz,保持了人类活动识别的准确性. 这使得有效的长期患者监测和设备小型化为临床应用.
科学领域:
- 生物医学工程 生物医学工程
- 数字健康数字健康
- 可穿戴技术可穿戴技术
背景情况:
- 使用可穿戴加速度计识别人类活动是评估疾病的关键数字生物标志物.
- 尽量减少数据量对于长期临床监测至关重要,以减少电力,计算和通信需求.
研究的目的:
- 为了确定准确识别人类活动的最低采样频率.
- 为长期基于可穿戴设备的临床监测优化数据收集.
主要方法:
- 三十名健康的参与者在身体的五个部位戴着九轴加速度计.
- 活动识别模型经过训练和测试,使用数据采样频率包括100,50,25,20,10和1赫兹.
- 分析了非主导手腕和胸部的数据.
主要成果:
- 将采样频率降低到10 Hz并没有显著影响手腕或胸部的识别精度.
- 当频率降低到1Hz时,观察到精度显著下降,特别是在刷牙等活动中.
- 10 Hz 采样频率有效地平衡了识别精度与数据量减少.
结论:
- 10 Hz的采样频率足以保持临床环境中人类活动识别的准确性.
- 优化的采样频率促进了长期的患者监测和较小的临床设备的开发.
- 这种方法支持有效的数字生物标志物开发,用于疾病严重程度的评估.
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