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

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Feasibility on Detecting Door Slamming towards

Monitoring Early Signs of Domestic Violence

Osian Morgan

School of Computer Science and

Informatics, Cardiff University

Cardiff, UK

MorganOB@cardiff.ac.uk

Hakan Kayan

School of Computer Science and

Informatics, Cardiff University

Cardiff, UK

KayanH@cardiff.ac.uk

Charith Perera

School of Computer Science and

Informatics, Cardiff University

Cardiff, UK

PereraC@cardiff.ac.uk

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, an

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.

Index Terms—Internet of Things, Anomaly Detection, On-

Device Audio Processing

I. INTRODUCTION AND MOTIVATION

With the ever-increasing availability of low-cost micro-

controllers and other computing devices, and advances in

more lightweight machine learning techniques, it is becoming

increasingly viable to make many of the everyday objects

found in our homes smarter. In this study we looked at the

feasibility of using inexpensive microcontrollers to leverage

machine learning techniques to detect speciﬁc phenomena. In

this case, we look at the possibility of using TinyML and an

Arduino Nano BLE 33 Sense to detect whether a door is being

slammed shut aggressively or being shut normally.

In a similar study [3], we see how the same Arduino board,

in conjunction with the Edge Impulse platform can be used to

detect the wing beats of Mosquitoes reasonably accurately.

The COVID-19 pandemic resulted in many changes and

restrictions for our daily lives, most notable being mandates to

work from home where possible, as well as legal requirements

to socially isolate. Between March 2020 and June 2020, police

in England and Wales recorded a 7% increase in offences

ﬂagged as domestic abuse related, with the ONS noting a

general increase in demand for domestic abuse victim support

services (including a 65% increase in calls to the National

Domestic Abuse Helpline between April and June 2020,

compared to the previous quarter). Overall, the entire 12-

month period between March 2020 and March 2021 saw an

overall increase of 6% in domestic abuse related crimes. [5]

This follows general increases seen in previous years and could

be associated with increased reporting by victims, in addition

to improved recording by police forces. [1]

With this in mind, we seek to demonstrate the viability of

using microcontrollers embedded within doors in the home, to

assist social workers and law enforcement in the monitoring

of potentially aggressive behaviors and perhaps the early signs

of domestic violence in social housing.

II. OUR APPROACH AND METHODS

We began by breaking the problem down into its component

parts. Namely what physical properties can we measure to

determine if a door has been slammed, how do we decide if

a sample shows a slam or a normal close, and how could we

implement this in a way that’s practical for local authorities,

social workers and law enforcement?

A. How to quantify a closing door?

We decided that accelerometer and audio data were the most

useful for this task. When thinking about how a human would

decide if a door is being closed aggressively or not, there are

typically two physical parameters that spring to mind; how fast

the door is closing, and how much sound is generated when it

hits the frame. We then selected a suitable microcontroller for

the task, we opted for the Arduino Nano BLE 33 Sense, as this

small board ships with the required instruments as standard,

in addition to having BLE capability. Next, we experimentally

determined that a 2 second sampling window was sufﬁcient

for slamming standardized MDF interior doors.

By recording variations in acceleration in three spatial

dimensions (x, y, z) we can see notable differences between a

slam and a normal close. We recorded this data using the IMU

on-board the Arduino and sent the samples to Edge Impulse, a

cloud-based web-platform for building and deploying machine

learning models for resource constrained devices, such as our

Arduino.

After collecting accelerometer data we moved on to collect-

ing audio samples, each sample once again being 2 seconds

in length. As shown in a 2021 study [4], we can see that

Arduinos are capable of handling audio analysis without the

need for a PC, thus keeping computing as close to the edge

as possible and allowing for their devices to be kept portable.

arXiv:2210.02642v1 [cs.SD] 6 Oct 2022

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FeasibilityonDetectingDoorSlammingtowardsMonitoringEarlySignsofDomesticViolenceOsianMorganSchoolofComputerScienceandInformatics,CardiffUniversityCardiff,UKMorganOB@cardiff.ac.ukHakanKayanSchoolofComputerScienceandInformatics,CardiffUniversityCardiff,UKKayanH@cardiff.ac.ukCharithPereraSchoolofCompute...

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

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