An Intelligent Wearable Framework for Real-Time Harassment Detection Using Machine Learning

The work presents an intelligent wearable harassment-detection framework that integrates FSR-based physical interaction sensing, NRF51822 BLE-enabled real-time communication, and cloud-based machine learning. Its main contribution is a stacking ensemble model combining XGBoost, LightGBM, CatBoost, and MLP, with Logistic Regression as the meta-learner, to improve harassment classification performance. The framework also incorporates SHAP-based explainability to interpret sensor contributions and uses multi-metric performance evaluation to assess the reliability and practical suitability of the proposed detection system.