基于深度长短期记忆自编码器的疲劳检测

K. Balaskas, K. Siozios
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引用次数: 2

摘要

有效的时间序列数据挖掘技术是现实世界测量系统的重要组成部分,它可以利用特征提取原理从未标记的数据中获得有意义的结果。在本文中,我们使用几种无监督机器学习技术,对来自IMU传感器的时间序列数据进行运动学分析,用于跑步者的疲劳检测。利用递归神经网络的优点和自编码器的数据压缩能力,提出了一种由LSTM自编码器组成的鲁棒特征提取方案。该模型结合了几种聚类算法的优点,可实现实时准确的疲劳检测,适合在嵌入式设备中实现。特征提取算法的实验评估表明,它们能够产生有意义的特征,克服了训练数据极其有限的障碍。推理过程在43%的代表性样本中成功检测,表明我们的模型在从未见过的运动数据中提取鲁棒特征方面的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fatigue Detection Using Deep Long Short-Term Memory Autoencoders
Efficient time series data mining techniques are an essential part of real world measurement systems and can yield meaningful results from unlabeled data by taking advantage of feature extraction principles. In this paper, we perform kinematic analysis on time series data from IMU sensors for fatigue detection on runners, using several unsupervised machine learning techniques. We propose a robust feature extraction scheme composed of an LSTM Autoencoder, to exploit the advantages of recurrent neural networks and the data compression capabilities of an Autoencoder. The proposed model combines the advantages of several clustering algorithms for accurate fatigue detection in real time, making it suitable for implementation in an embedded device. Experimental evaluation of the feature extraction algorithms showcased their capabilities to produce meaningful features, overcoming the obstacle of extremely limited training data. The inference procedure yielded successful detection in 43% of our representative sample, indicating the efficiency of our model in extracting robust features from unseen kinematic data.
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