Implementing Non-Invasive Human Activity Recognition with PIR Sensors using LSTM-RNN

Abstract

This project introduces an innovative Human Activity Recognition and Multi-Subject Classification and Monitoring System, leveraging state-of-the-art Deep learning techniques to monitor human activities and direction without the need for intrusive camera systems. The system addresses privacy concerns and enhances cost-effectiveness, representing a significant advancement in surveillance technology. By effectively managing multi-person scenarios, it surpasses the limitations of previous systems reliant on PIR sensors, particularly in accurately detecting human activities and classifying multiple subjects. Distinguishing between specific individuals in densely populated environments, the system aims to positively impact settings where traditional camera systems compromise human privacy. With the overarching goal of delivering a versatile and ethically sound surveillance solution, it enhances security measures while upholding individual privacy rights. This innovative approach represents a paradigm shift, offering a sophisticated means of observation in diverse human-centric environments. Additionally, the project proposes a method to detect human direction and activities using fused information from network-connected PIR sensors, validated through simulations and experiments, demonstrating improved success rates in real-time detection of human moving positions and activities. Prior to performing feature enhancement operations on the gathered data, we first gather and examine the PIR sensor's time-domain signal. We then aggregate the data based on peak time sequence features. The long short-term memory neural network is then used to predict the activities and direction of moving subject. Lastly, improved success rates in detecting the moving activities and direction of humans are used to validate the effectiveness of the suggested method through simulations and experiments, meeting the real-time detection needs of multiple IOT-based systems.