Computer-based system for soothing a baby and methods of use thereof

A method, comprising:

• receiving, by a processor, from at least one electronic resource comprising a database;
• wherein the database comprises:
• (i) baby-specific stimuli data for a plurality of baby-specific stimuli provided to a plurality of babies,
• (ii) baby-specific response data for a plurality of baby-specific responses acquired in response to the plurality of baby-specific stimuli provided to the plurality of babies, and
• (iii) baby personal data for each baby in the plurality of babies;
• receiving, by the processor, from an imaging device, image data of an image of at least one baby from the plurality of images;
• receiving, by the processor, from a microphone, audio signal data of the at least one baby;
• receiving, by the processor, from a plurality of sensors, sensor data associated with the at least one baby;
• determining, by the processor, baby-specific physiological data of the at least one baby based on the image data, the sensor data, and the audio signal data;
• wherein the baby-specific physiological data comprises:
• (i) breathing rate signal data of the at least one baby,
• (ii) spatial body temperature distribution data of the at least one baby,
• (iii) heartbeat signal data of the at least one baby,
• (iv) baby motion data of the at least one baby, and
• (v) baby voice classification data of the at least one baby;
• inputting, by the processor, to at least one baby-specific behavioral state detection machine learning model, the image data, the audio signal data, the sensor data, the baby-specific physiological data, and the baby personal data of the at least one baby;
• wherein the at least one baby-specific behavioral state detection machine learning model is trained using a dataset based at least in part on the baby-specific stimuli data, the baby-specific response data, and the baby personal data for each baby in the plurality of babies;
• receiving, by the processor, an output from the at least one baby-specific behavioral state detection machine learning model determining that the at least one baby is agitated, about to wake up, or both;
• transmitting, by the processor, over a communication network, to at least one communication device of at least one user associated with the at least one baby, the sensor data, an alert that the at least one baby is agitated, an alert that the at least one baby is about to wake up, or any combination thereof;
• and
• transmitting, by the processor, instructions based on the output that cause a speaker, a projection device, or both, to perform at least one of:
• (i) generating by the speaker, a soothing sound based on the determination that the at least one baby is agitated,
• (ii) generating by the speaker, a sleep-enhancing sound based on the determination that the at least one baby is about to wake up, or
• (iii) projecting by the projection device, a relaxing image to by viewed by the at least one baby based on the determination that the at least one baby is agitated.

A method, comprising:

• receiving, by a processor, from at least one electronic resource comprising a database;
• wherein the database comprises:
• (i) baby-specific stimuli data for a plurality of baby-specific stimuli provided to each baby in a plurality of babies,
• (ii) baby-specific response data for a plurality of baby-specific responses for each baby acquired in response to the baby-specific stimuli data provided to each baby, and
• (iii) baby personal data for each baby in the plurality of babies;
• determining, by the processor, from the baby-specific stimuli data and the baby-specific response data for each baby, sensor data from a plurality of sensors, image data from an image device, and audio data from a microphone acquired while monitoring each baby in the plurality of babies;
• determining, by the processor, baby-specific physiological data for each baby based on the image data, the sensor data, and audio signal data;
• wherein the baby-specific physiological data comprises:
• (i) breathing rate signal data for each baby,
• (ii) spatial body temperature distribution data for each baby,
• (iii) heartbeat signal data for each baby,
• (iv) baby motion data for each baby, and
• (v) baby voice classification data for each baby;
• executing, by the processor, a physics algorithm module that generates baby-specific features and environmental-specific features for each baby in the plurality of babies unique to a baby soothing use case from the baby-specific physiological data, the image data, the audio signal data, the sensor data, and the baby personal data for each baby;
• executing, by the processor, a time driven pipeline software module configured to calculate from the baby-specific features and the environmental-specific features for each baby unique to the baby soothing use case, baby-specific time-dependent features and environmental-specific time-dependent features for each baby based at least in part on a time series generated from the baby-specific features and the environmental-specific features that characterize feature progression over time;
• executing, by the processor, a behavioral model configured to generate a digital twin model for each baby in the plurality of babies based at least in part on:
• (1) a correlation between baby-specific behavioral features from the baby-specific features and the environmental-specific features for each baby, and
• (2) the baby-specific stimuli data, the baby-specific response data, and the baby personal data for each baby:
• wherein the digital twin model comprises a set of parameters representing different responses to different stimuli for each baby in the plurality of babies when each baby is in different behavioral states during different time intervals;
• executing, by the processor, a feedback environmental generator using the digital twin model of each baby to output at least one recommendation, at least one action, or both to change a behavioral state of each baby in accordance with the baby soothing use case for each baby;
• and
• generating, by the processor, a training dataset for the baby soothing use case based at least in part on:
• (1) input features for each baby from the plurality of babies comprising:
• (A) the baby-specific features for each baby,
• (B) the environmental-specific features for each baby,
• (C) the baby-specific time-dependent features for each baby, and
• (D) the environmental-specific time-dependent features for each baby, and
• (2) output features for each baby from the plurality of babies comprising:
• (A) the at least one recommendation for each baby,
• (B) the at least one action for each baby, or
• (C) both.