AI almost solved my toddler sleep issues (andreabaccega.com)

A parent turned to AI to address persistent sleep disturbances in their toddler by developing a custom automated sleep tracking system. Leveraging existing tools like the open-source Frigate for video surveillance, a custom convolutional neural network (CNN), and Babybuddy for tracking, they created a solution capable of detecting the child's sleep patterns with 99.8% accuracy in about 15 milliseconds. This system not only eliminated manual logging of sleep data but also provided actionable insights on the toddler's sleep quality, contributing to data-driven adjustments in their parenting strategies. This innovative approach is significant for the AI/ML community as it highlights the practical application of AI in everyday life, particularly in parenting. Utilizing transfer learning with a pre-trained YOLOv8 model, the parent fine-tuned the model on a dataset specific to toddler sleep images, unveiling critical patterns previously obscured by parental fatigue. The project's success underscores AI's potential to transform personal and family dynamics by facilitating informed decision-making, suggesting that similar methodologies could be adapted for various domains where behavioral data gathering and analysis are beneficial.