James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Mobile Health, Centre of Intelligent Healthcare, Coventry University, Coventry CV1 5RW, UK.
Sensors (Basel). 2021 Jun 4;21(11):3881. doi: 10.3390/s21113881.
The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to 'unstable incapacity'. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.
老年人的健康状况可以通过检查与衰老相关的疾病的累积效应来确定，这可能导致“不稳定的失能”。这种健康状况取决于日常生活活动（ADL）明显下降的独立性。检测 ADL 为改善老年人的家庭生活提供了可能性，因为它可以应用于跌倒检测系统。本文提出了一种基于雷达图像分类的老年人跌倒检测方法，通过检查他们的日常活动，利用之前为 99 名志愿者收集的雷达数据。使用机器学习技术对六个人类活动进行分类，分别是行走、坐着、站立、取物、喝水和跌倒事件。不同的机器学习算法，如随机森林、K 最近邻、支持向量机、长短期记忆、双向长短期记忆和卷积神经网络，用于数据分类。为了获得最佳结果，我们对可用的雷达图像应用了数据处理技术，如主成分分析和数据增强。本文的目的是在使用公共可用数据集的基础上提高研究结果，以进一步改进跌倒检测系统的研究。结果表明，在主成分分析和数据增强的基础上使用 CNN 算法可以获得最佳结果，准确率为 95.30%。结果还表明，当通过扩展可用数据来扩充训练数据时，主成分分析最为有益。与现有技术相比，我们提出的方法的结果显示出最高的准确性。
