Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2242-2247. doi: 10.1109/EMBC46164.2021.9630462.
The recent COVID-19 pandemic has further high-lighted the need for improving tele-rehabilitation systems. One of the common methods is to use wearable sensors for monitoring patients and intelligent algorithms for accurate and objective assessments. An important part of this work is to develop an efficient evaluation algorithm that provides a high-precision activity recognition rate. In this paper, we have investigated sixteen state-of-the-art time-series deep learning algorithms with four different architectures: eight convolutional neural networks configurations, six recurrent neural networks, a combination of the two and finally a wavelet-based neural network. Additionally, data from different sensors' combinations and placements as well as different pre-processing algorithms were explored to determine the optimal configuration for achieving the best performance. Our results show that the XceptionTime CNN architecture is the best performing algorithm with normalised data. Moreover, we found out that sensor placement is the most important attribute to improve the accuracy of the system, applying the algorithm on data from sensors placed on the waist achieved a maximum of 42% accuracy while the sensors placed on the hand achieved 84%. Consequently, compared to current results on the same dataset for different classification categories, this approach improved the existing state of the art accuracy from 79% to 84%, and from 80% to 90% respectively.
最近的 COVID-19 大流行进一步凸显了改进远程康复系统的必要性。一种常见的方法是使用可穿戴传感器来监测患者和使用智能算法进行准确和客观的评估。这项工作的一个重要部分是开发一种高效的评估算法，提供高精度的活动识别率。在本文中，我们研究了十六种最先进的时间序列深度学习算法，它们具有四种不同的架构：八种卷积神经网络配置、六种递归神经网络、两者的组合以及基于小波的神经网络。此外，还探索了来自不同传感器组合和放置位置以及不同预处理算法的数据，以确定实现最佳性能的最佳配置。我们的结果表明，在归一化数据下，XceptionTime CNN 架构是表现最好的算法。此外，我们发现传感器位置是提高系统准确性的最重要属性，将算法应用于放置在腰部的传感器数据可实现高达 42%的准确率，而放置在手上的传感器则可实现 84%的准确率。因此，与同一数据集上不同分类类别的现有结果相比，这种方法将现有技术的准确率从 79%提高到了 84%，分别从 80%提高到了 90%。
