Can computer vision technology be employed for accurate, unobtrusive, non-invasive, objective measurement and quantification of maternal sleeping position across the third trimester of pregnancy in the home setting?

Recent research studies have pointed to sleeping on the back in late pregnancy as possibly increasing the risk of serious problems pregnancy such as low birth weight and delivering a baby that died in the womb (stillbirth). However, because it is impossible to completely and accurately recollect one’s body position while sleeping, these studies are limited because sleeping position was recollected by the participants rather than monitored and recorded by technology. Due to the lack of products to monitor and record sleeping position in pregnancy, this research project aims to determine whether artificial intelligence can be used to detect and record sleeping position in late pregnancy in the home without bothering the pregnant woman or her partner. By helping Shiphrah Biomedical Inc.’s (Toronto, Canada) Clinical Research Team develop a product for simple, botherless, and accurate detection and recording of sleeping position during late pregnancy in the home, this project will benefit researchers, healthcare providers, and patients in Canada and around the world.

Faculty Supervisor:

Elham Dolatabadi

Student:

Partner:

Shiphrah Biomedicals

Discipline:

Engineering

Sector:

Manufacturing; Retail trade

University:

University of Toronto

Program: