IoT-Based Vibration Sensor Data Collection and Emergency Detection Classification using Long Short Term Memory (LSTM)

Abstract

In this paper, we used a vibration sensor known as G-Link 200 to collect real time vibration data. The sensor is connected through the internet gateway and Long Short Term Memory (LSTM) used for the classification of sensor data. The classification allows for detecting normal and anomaly activity situation which allows for triggering emergency situation. This is implemented in smart homes where privacy is an issue of concern. Example of such places are toilets, bedrooms and dressing rooms. It can also be applied to smart factory where detecting excessive or abnormal vibration is of critical importance to factory operation. The system eliminates the discomfort for video surveillance to the user. The data collected is also useful for the research community in similar research areas of sensor data enhancement. MATLAB R2019b was used to develop the LSTM. The result showed that the accuracy of the LSTM is 97.39% which outperformed other machine learning algorithm and is reliable for emergency classification.