Replication Data and Code for: Real-Time Recognition of Multivariate Event-Based Time Series on Embedded Devices Using Recurrent Neural Networks: A Practical Study

This repository contains the source code and processed datasets for a deep learning framework designed to multivariate event-based time series classification applied to monitor police firearm usage via embedded sensor fusion. The software features a custom-built Recurrent Neural Network (RNN) library implemented in pure Python/NumPy, enabling the training of lightweight models (Vanilla RNN, GRU, MGRU) suitable for deployment on low-resource embedded systems. The framework utilizes a multi-objective architecture to process simultaneous inputs from piezoelectric (vibration) and photoelectric (light) sensors. Key capabilities include "Zoneout" regularization, custom Backpropagation Through Time (BPTT), and export functionality to C-style headers for microcontroller integration. The included datasets consist of pre-processed, labelled time-series vectors representing real-world firearm manipulations (shots, reloads, and handling events)