Wearable Sensor-Based IoT XAI Framework for Predicting Freezing of Gait in Parkinsons Disease

TL;DR

This paper presents a wearable sensor-based IoT framework using machine learning for early prediction of Freezing of Gait in Parkinson's Disease, achieving high classification accuracy and interpretability.

Contribution

The study introduces a novel IoT wearable sensor system with ML models for accurate and interpretable FOG prediction in Parkinson's patients.

Findings

XGBoost achieved 97% accuracy in FOG classification.

SHAP analysis identified GYR SI degree as key predictive factor.

The system enables real-time monitoring and aid for Parkinson's patients.

Abstract

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.