Falls are a major health concern and result in high morbidity and mortality rates in older adults with high costs to health services. Automatic fall classification and detection systems can provide early detection of falls and timely medical aid. This paper proposes a novel Random Vector Functional Link (RVFL) stacking ensemble classifier with fractal features for classification of falls. The fractal Hurst exponent is used as a representative of fractal dimensionality for capturing irregularity of accelerometer signals for falls and other activities of daily life. The generalised Hurst exponents along with wavelet transform coefficients are leveraged as input feature space for a novel stacking ensemble of RVFLs composed with an RVFL neural network meta-learner. Novel fast selection criteria are presented for base classifiers founded on the proposed diversity indicator, obtained from the overall performance values during the training phase. The proposed features and the stacking ensemble provide the highest classification accuracy of 95.71% compared with other machine learning techniques, such as Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine. The proposed ensemble classifier is 2.3× faster than a single Decision Tree and achieves the highest speedup in training time of 317.7× and 198.56× compared with a highly optimised ANN and RF ensemble, respectively. The significant improvements in training times of the order of 100× and high accuracy demonstrate that the proposed RVFL ensemble is a prime candidate for real-time, embedded wearable device–based fall detection systems.

中文翻译：

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具有分形特征的新型功能性链路网络堆叠组件，用于多通道跌倒检测

跌倒是主要的健康问题，导致老年人的高发病率和高死亡率，并给医疗服务带来高昂的成本。自动跌倒分类和检测系统可以提供跌倒的早期检测和及时的医疗救助。提出了一种新颖的具有分形特征的随机矢量功能链接（RVFL）堆叠集成分类器，用于跌倒分类。分形赫斯特指数用作分形维数的代表，用于捕获加速度计信号在跌落和日常生活中的其他活动的不规则性。广义Hurst指数与小波变换系数一起被用作输入特征空间，用于由RVFL神经网络元学习器组成的新型RVFL堆叠集成。提出了基于建议的多样性指标的基础分类器的新的快速选择标准，该标准是从训练阶段的总体绩效值中获得的。与其他机器学习技术（例如，随机森林（RF），人工神经网络（ANN）和支持向量机）相比，所提出的功能和堆叠集成提供了95.71％的最高分类精度。所提出的集成分类器比单个决策树快2.3倍，并且与高度优化的ANN和RF集成相比，在训练时间上实现了最高的加速，分别为317.7和198.56。训练时间的显着改善达到了100倍左右，而且精度很高，这表明所提出的RVFL集合是基于嵌入式可穿戴设备的实时实时跌倒检测系统的主要候选对象。