Feasibility on Detecting Door Slamming towards Monitoring Early Signs of Domestic Violence

TL;DR

This paper explores using low-cost microcontrollers and TinyML to detect aggressive door slamming as an early indicator of domestic violence, achieving high accuracy in controlled and noisy environments.

Contribution

It demonstrates the feasibility of deploying a convolutional neural network on a microcontroller for real-time detection of aggressive door slamming signals.

Findings

Achieved 88.89% accuracy in no-noise conditions

Accuracy declined slightly to 87.50% with background noise

Successfully deployed on Arduino Nano BLE 33 Sense for real-time detection

Abstract

By using low-cost microcontrollers and TinyML, we investigate the feasibility of detecting potential early warning signs of domestic violence and other anti-social behaviors within the home. We created a machine learning model to determine if a door was closed aggressively by analyzing audio data and feeding this into a convolutional neural network to classify the sample. Under test conditions, with no background noise, accuracy of 88.89\% was achieved, declining to 87.50\% when assorted background noises were mixed in at a relative volume of 0.5 times that of the sample. The model is then deployed on an Arduino Nano BLE 33 Sense attached to the door, and only begins sampling once an acceleration greater than a predefined threshold acceleration is detected. The predictions made by the model can then be sent via BLE to another device, such as a smartphone of Raspberry Pi.