CHILD: AI-Based E-Health Framework for Infant Sleep Disorder Identification in 5G Smart Home

This paper proposes a new deep learning (DL) model for the detection of infant sleep disorders specific to Confusional Arousals (CA), Leg Restlessness (LR), and Sleep Apnea (SA) in 5G smart homes and e-Healthcare systems integration. The proposed framework, CHILD, consists of four critical layers: specific applications such as Infant Monitoring, Sensor and Data Acquisition, Artificial Intelligence systems and e-Healthcare and Smart Home systems. The smart home part increases the effectiveness of real-time environmental detection, the e-Healthcare system helps to provide convenient communication with doctors. The sleep disorder categorization problem is solved using an enhanced deep learning framework involving LSTM and GRU algorithms; the data are from sensors installed in the smart home., we obtained the 88% accuracy of LSTM model in consideration of the home automation and intelligent e-Healthcare system to enhance infant health and response actions.