论文标题
ECE496Y的最终报告：用于检测帕金森患者步态冻结的边缘机器学习
ECE496Y Final Report: Edge Machine Learning for Detecting Freezing of Gait in Parkinson's Patients
论文作者
论文摘要
帕金森氏病是一种常见的神经系统疾病，需要多种运动缺乏症状。在这个项目中，我们开发了一种带有上载的边缘机器学习算法的设备，该设备可以检测帕金森患者的步态症状的冻结发作。使用十名患者的数据，该算法在验证中的精度为83.7％。该模型已部署在微控制器Arduino Nano 33 BLE Sense板模型中，并在实时操作中验证了通过计算机将数据流传输到微控制器的实时操作。
Parkinson's disease is a common neurological disease, entailing a multitude of motor deficiency symptoms. In this project, we developed a device with an uploaded edge machine learning algorithm that can detect the onset of freezing of gait symptoms in a Parkinson's patient. The algorithm achieved an accuracy of 83.7% in a validation using data from ten patients. The model was deployed in a microcontroller Arduino Nano 33 BLE Sense Board model and validated in real-time operation with data streamed to the microcontroller from a computer.
