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AI-Guided Cloud-Based Baby Monitoring System

Northeastern researchers are forming a spinout corporation to commercialize AiWover, a system to monitor infant activity and development.

Published: 17th February 2022
AI-Guided Cloud-Based Baby Monitoring System


The difficulties associated with caring for a newborn during their first developmental stages include ensuring safety and passing of developmental milestones set by pediatricians. Baby monitoring systems in the market are bought by parents to directly observe their child and receive alerts when something important happens (crying, waking up, etc.). The visual triggers are often simple (“motion within zone”), which have many false alarms, and fail to catch events of interest like lethal/serious injuries caused by preventable accidents, identification of early signs of neurodevelopmental disorders (eg. Torticollis, cerebral palsy, autism spectrum disorder, sudden infant death syndrome during sleep). While these monitoring devices can provide a level of comfort and security to parents, even the most expensive systems are limited in the information and notification regarding an infant’s activity and developmental process.

There exist very few recent attempts initiated by the computer vision community to automatically perform body pose/posture estimation and movement tracking on videos of infants. These models all heavily rely on having access to both RGB and depth data sequences, which hinders their use in regular webcam-based monitoring systems. On the other hand, recent powerful RGB-based pose estimation models trained on large-scale adult activity datasets have limited success in estimating infant movements due to the significant differences in their body ratios, the complexity of infant poses, and types of their activities. More specifically, publicly available large-scale human pose datasets are predominantly scenes from sports, TV shows, and other daily activities performed by adult humans, and none of these datasets provides exemplars of activities of infants. Additionally, privacy and security considerations hinder the availability of adequate infant images/videos required for training of a robust model with deep structure from scratch. Hence, there is an unmet need for data-efficient and privacy-preserving infant pose and posture recognition models to promote the applications of AI-guided infant motor function screening tools towards early diagnosis and intervention 

Technology Overview

Researchers at Northeastern have developed an artificial intelligence (AI)-guided cloud-based baby monitoring system, called AiWover. AiWover tracks the baby’s movements in the crib and the playroom, categorizes the poses and postures, and analyzes the baby’s activity and development. AiWover’s advanced AI-based system is based on cutting-edge research and provides accurate monitoring by collecting massive amounts of data on the child’s activities, interpreting the data using advanced AI algorithms, and providing user-friendly summaries and alerts to parents, pediatricians, and developmental specialists. Initial models are focused on infant motion monitoring based on real time body pose assessment, but future components of this ecosystem will include other sensing modalities that parents can integrate in to the secure, privacy-preserving cloud-enabled infant activity monitoring system. Novel private datasets with babies provides AiWover’s algorithms a unique advantage over competitors that rely on adult body/pose imagery. This technology also allows models to learn efficiently from a small number of experimental (real) data supplemented by synthetic data produced by in-house generative models. 


  • Reliable updates through accurate pose and posture tracking allows for smart monitoring of baby’s activity and development
  • Notifications to alarm care-givers in situations of potential accidents
  • Identification of early developmental disorders, especially during the first two months after birth
  • Activity recognition and situational awareness
  • Privacy-preserving, due to being non-RGB as well its in-situ processing
  • Unlike existing approaches to in-bed behavior monitoring (i.e. wearable sensors, pressure mats) this system is fully non-contact
  • Small form factor
  • Low cost


  • Infant monitoring ‑ infant pose/posture monitoring for daily checkup, early motor screening, tele-health/rehabilitation
  • Sleep posture monitoring for pregnant women
  • Continuous bed-bound patient monitoring in nursing homes and hospitals


  • Idea validation through customer discovery
  • Connecting with prospective investors 
IP Status
  • Patented
  • Development partner
  • Commercial partner
  • University spin out
  • Seeking investment
  • Licensing