Smart Bassinet Multi-Sensor Fusion for Infant Sleep Analysis

The patent combines multiple sensors (motion sensors, temperature sensors, humidity sensors, audio sensors) into a single integrated bassinet system that collectively monitors infant sleep patterns, environmental conditions, and physiological states. This merging approach enables comprehensive data integration without proportionally increasing system complexity, as all sensors are coordinated through a centralized processing unit that provides unified real-time recommendations.

The patent implements federated learning by segmenting the data processing architecture into distributed components: local bassinet devices collect and pre-process sensor data, while a centralized server performs aggregate analysis across multiple devices. This segmentation allows the system to leverage crowd-sourced data from numerous infants for improved recommendation accuracy while distributing computational complexity across the network rather than concentrating it in a single device.

The patent employs preliminary action by pre-training deep learning models offline using historical infant data and environmental conditions. These pre-trained models are then deployed to the bassinet system, enabling real-time personalized recommendations with minimal computational resource consumption during actual operation. The system only needs to perform lightweight inference on current sensor data rather than executing full training algorithms, significantly reducing energy usage while maintaining recommendation quality.