  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.
