用可穿戴传感器和AI提前预警帕金森患者冻结步态，精准率达97%。
Wearable Sensor-Based IoT XAI Framework for Predicting Freezing of Gait in Parkinsons Disease
- 基于ESP32和LoRa的可穿戴设备，用机器学习分类步态异常。
- XGBoost模型准确率达97%，显著优于其他算法。
- 结合位置追踪与SHAP分析，适合医疗监护与辅助设备开发。
本研究提出一种基于可穿戴传感器与LoRa通信的物联网可解释AI框架，用于早期预测帕金森病患者的冻结步态（FOG）。系统采用ESP32微控制器，利用Micromlgen库部署训练好的模型，通过Catboost、XGBoost和Extra Tree等机器学习算法对临床数据进行精准分类。结果显示，XGBoost模型分类准确率达97%，Catboost为96%，Extra Trees为90%。SHAP可解释性分析表明，GYR SI度是影响预测的关键因素。该系统可实时追踪患者位置并提供辅助支持，具备在医疗健康与生物医学技术领域解决实际问题的巨大潜力。
原文摘要 · Abstract (English)
This research discusses the critical need for early detection and treatment for early prediction of Freezing of Gaits (FOG) utilizing a wearable sensor technology powered with LoRa communication. The system consisted of an Esp-32 microcontroller, in which the trained model is utilized utilizing the Micromlgen Python library. The research investigates accurate FOG classification based on pertinent clinical data by utilizing machine learning (ML) algorithms like Catboost, XGBoost, and Extra Tree classifiers. The XGBoost could classify with approximately 97% accuracy, along with 96% for the catboost and 90% for the Extra Trees Classifier model. The SHAP analysis interpretability shows that GYR SI degree is the most affecting factor in the prediction of the diseases. These results show the possibility of monitoring and identifying the affected person with tracking location on GPS and providing aid as an assistive technology for aiding the affected. The developed sensor-based technology has great potential for real-world problem solving in the field of healthcare and biomedical technology enhancements.
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