Accelerometer and Camera-Based Strategy for Improved Human Fall Detection
Accelerometer and Camera-Based Strategy for Improved Human Fall Detection
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DOI：
10.1007/s10916-016-0639-6
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发表时间：
2016-12
影响因子：
5.3
通讯作者：
Nabil Zerrouki;F. Harrou;Ying Sun;A. Houacine
中科院分区：
文献类型：
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作者：
Nabil Zerrouki;F. Harrou;Ying Sun;A. Houacine
在本文中，我们解决了使用异常检测来检测人体跌倒的问题。跌倒的检测和分类基于加速度数据和人体轮廓形状的变化。首先，我们使用指数加权移动平均（EWMA）监控方案来检测加速度数据的潜在下降。我们使用 EWMA 来识别与特定跌倒类型相对应的特征，从而使我们能够对跌倒进行分类。在分类阶段仅使用与检测到的跌倒相对应的特征。使用原始数据的子集来设计分类模型的好处是可以最大限度地减少训练时间并简化模型。根据检测到的跌倒对应的特征，我们使用支持向量机（SVM）算法来区分真实跌倒和类似跌倒事件。我们将此策略应用于热舒夫大学公开的跌倒检测数据库。结果表明，我们的策略准确地检测到跌倒事件并对其进行分类，表明其在跌倒情况下的早期警报机制中的潜在应用及其对检测到的跌倒进行分类的能力。将基于 EWMA 的 SVM 分类器方法的分类结果与使用三种常用机器学习分类器（神经网络、K 最近邻和朴素贝叶斯）的分类结果进行比较，证明了我们的模型的优越性。
In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow’s. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.