Ahmed Moiz, Mehmood Nadeem, Nadeem Adnan, Mehmood Amir, Rizwan Kashif
Department of Computer Science, University of Karachi, Karachi, Pakistan.
Faculty of Computer and Information System, Islamic University in Madinah, Madinah, Saudi Arabia.
Healthc Inform Res. 2017 Jul;23(3):147-158. doi: 10.4258/hir.2017.23.3.147. Epub 2017 Jul 31.
Falling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data.
We first developed a data set, 'SMotion' of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall.
To evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups.
In this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.
老年人跌倒被视为主要死因。近年来，环境和无线传感器平台在发达国家已被广泛用于检测老年人跌倒情况。然而，我们认为在巴基斯坦等发展中国家，解决这一问题还需要付出额外努力，在这些国家，大多数跌倒致死事件甚至都未被报告。考虑到这一点，本文提出了一种基于实时微光传感器数据分类的跌倒检测系统原型。
我们首先通过使用微光传感器的人体区域网络开发了一个数据集“SMotion”，其中包含可能导致老年人跌倒的特定姿势，并将该数据集中的项目按年龄和体重分组。我们使用支持向量机（SVM）、K近邻（KNN）和神经网络（NN）这三种分类器开发了一个特征选择和分类系统。最后，制作了一个原型，以便在跌倒时向护理人员、健康专家或紧急服务机构发出警报。
为评估所提出的系统，使用了SVM、KNN和NN。本研究结果确定KNN是最准确的分类器，年龄组的最大准确率为96%，体重组的最大准确率为93%。
本文提出了一种基于分类的跌倒检测系统。为此，开发了SMotion数据集并将其分为两组（年龄组和体重组）。所提出的老年人跌倒检测系统通过使用第三代传感器的人体区域传感器网络来实现。评估结果表明，所提出的跌倒检测原型系统在测试场景中具有合理的性能。
