FSID:一种新的人类活动识别方法,使用几次射击重量印记
Mohammad Belal1, Taimur Hassan2, Abdelfatah Hassan3
1Department of Mechanical and Nuclear Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.
Scientific reports
|July 2, 2025
概括
这项研究介绍了使用有限步态感官数据进行人类活动识别 (HAR) 的Few-Shot Imprinted DINO (FSID). FSID有效地识别了采用最小样本的活动,在低数据场景中表现优于传统方法.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 人类活动识别 (HAR) 对医疗保健至关重要,但受到有限的标记数据和传统深度学习中不良概括的挑战.
- 现有的模型在稀缺,不平衡或新的活动中扎,阻碍了个性化和动态的医疗保健应用程序.
研究的目的:
- 提出Few-Shot Imprinted DINO (FSID),这是一个在低数据系统中用于HAR的新框架.
- 解决传统深度学习模型在现实世界场景中的局限性,数据集不足或不平衡.
主要方法:
- 将时间序列传感器数据 (EMG,IMU) 通过短时间里埃转换转换为光谱图像.
- 使用预先训练的DINO (无标签蒸) 模型作为可转移表示的特征提取器.
- 采用Few-Shot学习与重量印记来创建没有代微调的分类原型.
主要成果:
- 在只有20个新样本的情况下,FSID在HuGaDB数据集上达到高达55.47%的准确性,在LARa数据集上达到35.81%.
- 与基线模型相比,在低数据设置中的各种配置中表现出卓越的性能.
- 谱图被证明是时间频率表示的有效和计算效率高.
结论:
- FSID为低数据制度中的HAR提供了有效的解决方案,提高了个性化医疗保健中的适用性.
- 该框架成功地利用自主监督学习和重量印记,以最小的数据进行强大的活动识别.
- 这些发现突出了FSID在现实世界应用中的潜力,这些应用需要从步行感官数据中获得高效和准确的HAR.
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