Ann Nosseir
ABSTRACT
Parents keep monitoring their babies while sleeping to confirm their safety and comfort. A regular baby check is not easy. There are different products in the market that help parents to monitor their babies. Some products are wearable systems that are sensor-based and the majority are camera-based. It is either video streaming and parents check it often or sensors information that is about heartbeat, temperature or motion. These systems are not cheap especially for families who live in low income countries.
This work presents a low cost novel idea that provides parents information about their babies' discomfort states while sleeping. The system is based on real data which is not common for other systems. The system implemented reports state about the baby like being wet, sick, feeling hot, moving, or a combination of these states. The architecture of the system has four layers. The first layer is the input sensors that are room temperature, baby temperature, urine, and sound sensors to capture different information about the baby and his/her environment. The second layer is the storage, where the captured data is stored. The third is a developed classifier to accurately predict the baby states. M- SVM and KNN algorithms have the best accuracy.
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Index Terms
- Monitoring Baby State While Sleeping Using K-NN and M-SVM Classifiers
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