Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.
Department of Cardiology, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan.
Sensors (Basel). 2021 May 30;21(11):3797. doi: 10.3390/s21113797.
Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitable part of aging. Many different fall detection systems have been developed to help people avoid falling. However, traditional systems based on wearable devices or image recognition-based have many disadvantages, such as user-unfriendly, privacy issues. Recently, WiFi-based fall detection systems try to solve the above problems. However, there is a common problem of reduced accuracy. Since the system is trained at the original signal collecting/training place, however, the application is at a different place. The proposed solution only extracts the features of the changed signal, which is caused by a specific human action. To implement this, we used Channel State Information (CSI) to train Convolutional Neural Networks (CNNs) and further classify the action. We have designed a prototype to test the performance of our proposed method. Our simulation results show an average accuracy of same place and different place is 93.2% and 90.3%, respectively.
老年人很容易摔倒，这对他们的健康是一个重大威胁。然而，摔倒时可以预防的，而且不一定是衰老的必然结果。已经开发出许多不同的跌倒检测系统来帮助人们避免跌倒。然而，基于可穿戴设备或基于图像识别的传统系统存在许多缺点，例如用户不友好、隐私问题。最近，基于 WiFi 的跌倒检测系统试图解决上述问题。然而，存在一个常见的精度降低的问题。由于系统是在原始信号采集/训练的地方进行训练的，然而，应用是在不同的地方。所提出的解决方案只提取由特定人体动作引起的特定信号的特征。为了实现这一点，我们使用信道状态信息（CSI）来训练卷积神经网络（CNN），并进一步对动作进行分类。我们设计了一个原型来测试我们提出的方法的性能。我们的仿真结果表明，同一地点和不同地点的平均准确率分别为 93.2%和 90.3%。
