Artificial Neural Network for Identification of Infant Feeding Tracking Using the Smart Bottle System
Jiajun Guan, Robert Brewster, Javier de la Fuente, Alison K. Ventura, Benjamin G. Hawkins · 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · 2021
In this work, we present the results of a comparison of simple artificial neural network (FFNN) designs intended to identify infant bottle-feeding events and appropriate feeding volume recording intervals using accelerometer data recorded from a custom designed "Smart Bottle" system. To properly identify and distinguish these events with an accuracy of 99.8%, while accommodating the constraints of the deployment environment, two concurrent FFNNs were implemented.