Quantifying the Chaos Level of Infants' Environment via Unsupervised Learning
This work addresses the need for objective measurement of environmental chaos, which can impact cognitive development in children, representing an incremental advance over subjective methods.
The paper tackled the problem of objectively quantifying household chaos in infants' homes using unsupervised machine learning techniques, achieving promising results with data from 9 participants totaling 197 hours.
Acoustic environments vary dramatically within the home setting. They can be a source of comfort and tranquility or chaos that can lead to less optimal cognitive development in children. Research to date has only subjectively measured household chaos. In this work, we use three unsupervised machine learning techniques to quantify household chaos in infants' homes. These unsupervised techniques include hierarchical clustering using K-Means, clustering using self-organizing map (SOM) and deep learning. We evaluated these techniques using data from 9 participants which is a total of 197 hours. Results show that these techniques are promising to quantify household chaos.