Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?
Joanna Duda-Goławska, Aleksander Rogowski, Zuzanna Laudańska, Jarosław Żygierewicz, Przemysław Tomalski, Dalin Zhang, Kaixuan Chen, Xu Cheng, Huan Liu · 2024
Read Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter? by Joanna Duda-Goławska, Aleksander Rogowski, Zuzanna Laudańska, Jarosław Żygierewicz, Przemysław Tomalski, Dalin Zhang, Kaixuan Chen, Xu Cheng, Huan Liu (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11644974.txt.
Description
Joanna Duda-Golawska and co-authors taught computers to read baby body position in two thousand twenty-four. Knowing how infants lie and move helps track early growth. It can also flag delays in motor skills sooner. Watching video by hand takes many hours of skilled work. Small motion sensors worn by the baby offer a faster route.
The sensors record speed changes, turning, and magnetic field strength. Computer models learn to sort positions from those signals. The study asks which signal traits matter most for correct sorting. It uses repeat measures as babies grow over time. Clear answers help teams build smaller, cheaper sensor sets. Home monitoring for at-risk infants could gain from that work.
Catalog record
| CREATOR | Joanna Duda-Goławska, Aleksander Rogowski, Zuzanna Laudańska, Jarosław Żygierewicz, Przemysław Tomalski, Dalin Zhang, Kaixuan Chen, Xu Cheng, Huan Liu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |