基于PCA-ANN的跌倒检测系统设计与实现

Design and implementation of fall detection system based on PCA-ANN

针对当前可穿戴跌倒检测系统存在的精度低、隐私保护性差等问题,设计并实现了一款高精度、低延时的跌倒检测系统.首先使用Arduino Nano 33 BLE开发板为检测装置的主控部件,借助专门针对物联网场景的轻量级开源机器学习框架TensorFlow Lite,设计了采用主成分分析PCA结合人工神经网络ANN的跌倒检测算法(简称PCA-ANN);其次使用TensorFlow框架对网上公开的跌倒数据集进行模型的训练和转化,并将模型部署到嵌入式跌倒检测终端;最后使用得到的跌倒检测装置对志愿者进行实际环境的跌倒检测实验,实验结果表明系统的跌倒检测准确率达到了99.04%,敏感度和特异性分别为97.57%和99.58%.该系统利用边缘计算技术完成了在计算能力和存储单元受限的嵌入式设备上运行深度学习的任务,将这项技术应用到可穿戴跌倒检测装置中,为后续的研究提供了资料.相比现存的需要进行云端数据传输的跌倒检测系统,本系统还具有低延时和高精度的特点,同时消除了用户隐私方面的隐患,适合老年人佩戴.

In view of low accuracy and poor privacy protection in current wearable fall detection systems,we had designed and implemented a fall detection system with high precision and low delay.First,the Arduino Nano 33 BLE development board as the main control component of the detection device is used.A fall detection algorithm(PCA-ANN)based on principal component analysis combined with artificial neural network is designed and implemented.It uses a lightweight open-source machine learning framework TensorFlow Lite,which is specifically fit for IoT scenarios.Secondly,we trained and transformed the model based on the fall data set published online publicly using TensorFlow framework and deployed the model to the embedded fall detection terminal.Finally,the fall detection device was used to carry out the actual fall detection experiment on the volunteers The experimental results show that the fall detection accuracy of the system is 99.04%,the sensitive is 97.57%,and the specificity is 99.58%.The system uses edge computing technology to complete the task of running deep learning on embedded devices with limited computing power and storage units,and creatively applies this technology to wearable fall detection devices,which provides valuable data for subsequent research.Compared with the existing fall detection systems that require data transmission to cloud,this system has the characteristics of low latency and high accuracy.Besides,this system also eliminates hidden dangers in user privacy,which is suitable for elderly people to wear.