标题： 基于边缘深度学习的帕金森病患者冻结步态检测 显示英文标题

标题： Edge Deep Learning Enabled Freezing of Gait Detection in Parkinson's Patients

摘要： 本文提出了一种无线传感器网络的设计，用于检测和提醒帕金森病患者出现的冻结步态（FoG）症状。 每个集成三轴加速度计的传感节点可以放置在患者的脚踝、大腿和躯干上。每个传感节点都可以使用设备上的深度学习（DL）模型独立检测FoG，该模型以挤压激励卷积神经网络（CNN）为特色。在使用公共数据集的验证中，开发的原型在每个传感节点使用少于20k可训练参数的情况下达到了88.8%的FoG检测灵敏度和85.34%的F1分数。一旦检测到FoG，就会生成听觉信号提醒用户，并且如果需要，还会将警报信号发送到手机以采取进一步行动。传感节点可以通过感应耦合轻松无线充电。系统是自包含的，所有用户数据都在本地处理，不会将数据流传输到外部设备或云，从而消除了与无线数据传输相关的网络安全风险和功率惩罚。所开发的方法可以在广泛的应用中使用。 显示英文摘要

摘要： This paper presents the design of a wireless sensor network for detecting and alerting the freezing of gait (FoG) symptoms in patients with Parkinson's disease. Three sensor nodes, each integrating a 3-axis accelerometer, can be placed on a patient at ankle, thigh, and truck. Each sensor node can independently detect FoG using an on-device deep learning (DL) model, featuring a squeeze and excitation convolutional neural network (CNN). In a validation using a public dataset, the prototype developed achieved a FoG detection sensitivity of 88.8% and an F1 score of 85.34%, using less than 20 k trainable parameters per sensor node. Once FoG is detected, an auditory signal will be generated to alert users, and the alarm signal will also be sent to mobile phones for further actions if needed. The sensor node can be easily recharged wirelessly by inductive coupling. The system is self-contained and processes all user data locally without streaming data to external devices or the cloud, thus eliminating the cybersecurity risks and power penalty associated with wireless data transmission. The developed methodology can be used in a wide range of applications.