Ai-powered smart bassinet with multi-sensor integration and real-time personalized infant care recommendations

Abstract

An infant developmental platform includes a bassinet configured to support an infant, a plurality of sensors integrated into the bassinet and a processor operatively connected to the plurality of sensors configured to receive sensor data from the plurality of sensors and utilize the sensor data in analyzing neurodevelopmental milestones to generate real-time infant care strategies and developmental insights for neurodevelopment of the infant. The platform may include a graphical user interface operatively connected to the processor, wherein the graphical user interface is configured to display the real-time infant care strategies and developmental insights and receive caregiver feedback data. The platform may include a feedback module operatively connected to the processor and configured to adjust processor inference models based on the caregiver feedback data received via the graphical user interface or on changes in sensor data corresponding to infant physiological or behavioral patterns indicative of neurodevelopmental milestones.

Description

TITLE OF THE INVENTIONAl-Powered Smart Bassinet with Multi-Sensor Integration and Real-Time Personalized Infant Care RecommendationsRELATED APPLICATION(S)

[0001] This PCT application claims priority to U.S. Non- Provisional Application No. 18 / 793,719 filed August 2, 2024, The disclosure of the prior application is incorporated herein by reference in its entirety.BACKGROUND OF THE DISCLOSURE1. Field of the Disclosure

[0002] The present disclosure relates to the field of infant care devices. More specifically, it relates to a smart bassinet system that leverages multiple sensors, deep learning methodologies, predictive modeling, and APIs ( Application Programing Interfaces) to gather and analyze data about an infant's physiological and environmental state. The bassinet is further distinguished by its innovative use of multi-source learning techniques, including federated learning and crowd-sourced data analysis, as well as loT (Internet of Things) control capabilities, to draw' insights from a broad range of infant data beyond the individual user and enable third-party application development.2. Description of the Related Art

[0003] Parental management of an infant's sleep routine poses significant challenges and concerns, with a common goal of establishing a sleep routine that ensures the infant's health, safety, and comfort. Issues such as Sudden Infant Death Syndrome (SIDS), sleep disturbances, and the resultant stress and anxiety experienced by parents underscore the need for more sophisticated solutions. While traditional infant monitoring devices provide some level of insight, they are often limited by their inability to integrate comprehensive data and provide real-time, actionable recommendations.

[0004] Recent technological advancements, particularly in Artificial Intelligence (Al), machine learning, and sensor technology, offer a transformative opportunity to address these issues. Despite these advancements, existing solutions for infant care fail to utilize the potential of federated learning and crowd- sourcing data from a large user base to enhance the accuracy and effectiveness of recommendations. Federated learning, which allows for decentralized data processing, ensures the privacy and security of sensitivedata while still enabling robust analysis across a vast number of devices. Yet, federated learning has yet to be applied to infant care.

[0005] Several notable patents exist in this domain. U.S. Pat. No. 4,640,034 (Zisholtz, Feb. 3, 1987) and U.S. Pat. No. 4,777,938 (Sirota, Oct. 18, 1988) describe various infant sleep devices that use sensory stimuli to induce sleep in children. U.S. Pat. No.7,127,074 (Landa, Oct. 24, 2006) introduces a baby monitor device that assists a caregiver in training a child to sleep by muting the sound of the child for a fixed period of time. However, these devices do not address the root causes of sleep problems in children. Rather, these devices function to mitigate sleep disruption.

[0006] U.S. Patent Application No. 20150094830 Al (Rest Devices Inc., Apr. 2, 2015) discloses a computerized health and sleep monitor that captures biometric data of an infant and transmits the data via a network to an event server for evaluation. While this system provides a foundation for health monitoring, it fails to leverage federated learning and APIs to significantly enhance data processing and ensure robust privacy.

[0007] World Patent Document WO 02017196695 (‘“695”) (Udisense Inc., Nov. 16, 2017) describes a video monitoring system with a fixed camera above a crib. The camera is only capable of watching the baby and c annot measure physiological parameters of the baby. Without these parameters, ‘695 fails to recognize the needs of the baby and therefore, cannot provide recommendations.

[0008] U.S. Pat. No. 9,694,156 (Eight Sleep, Jul. 4, 2017) describes a sleep system with sensors and machine learning for sleep improvement, primarily designed for adults and lacking the comprehensive approach needed for infants. It does not utilize federated learning or APIs for third-party application development.

[0009] U.S. Published Application No. 20170043118 (Happiest Baby Inc., Feb. 16, 2017) (“’ 118”) discloses a sleep-aid device that moves and generates sound to calm a baby. Additionally, U.S. Published Application No. 20170055898 (Awardables Inc., Mar. 2, 2017) describes systems for determining sleep stages and sleep events using sensor data. However, the systems and devices of (‘ 118”) and (898) fail to incorporate a broad range of sensors and / or utilize federated learning to enhance data privacy and accuracy and offer more comprehensive insights and recommendations.

[0010] U.S. Patent No. 8,562,511 (Koninklijke Philips N.V., Oct. 22, 2013) describes a system for inducing sleep using a breathing rate measuring unit and light pattern generator. Unlike this sy stem, ours not only induces sleep but also monitors and analyzes a wide range of physiological data, offering personalized recommendations andsupporting third-party application development. Additionally, U.S. Patent No. 8,532,737 (Cervantes, Sep. 10, 2013) discloses an apparatus for automatically monitoring sleep with a video recorder and real-time image transmission. Our system integrates multiple types of sensors beyond video, utilizes Al for data analysis, and provides an open API for third-party developers.

[0011] U.S. Patent No. 9,530,080 (Joan and Irwin Jacobs Technion-Comell Institute, Dec. 27, 2016) discloses systems for monitoring babies with cameras and centralized computation. However, it does not disclose physiological sensors integrated into a bassinet nor does it employ federated learning for privacy-preserving data analysis. Additionally, U.S. Patent No. 9,572,376 (Nested Bean Inc., Feb. 21, 2017) describes a wearable accessory that provides gentle pressure to mimic a human hold. By integrating sensors into the bassinet, our proposed solution avoids reliance on wearables, offering a more comprehensive monitoring solution

[0012] U.S. Published Application No. 2011 / 0015467 (‘“467”) (Dothie et al.) describes a base unit with sensors for sleep-relevant characteristics and environmental conditions. However, ‘467 does not address how to monitor an infant in a bassinet as well as optimal placement of the sensors for the infant. Additionally, U.S. Patent No. 9,694,156 (Eight Sleep, Jul. 4, 2017) details a bed device system for gathering and analyzing human biological signals. This system also does not address the optimal sensor placement and sleeping device for an infant.

[0013] U.S. Patent Application 20070279234 (Walsh, Dec. 6, 2007) and European Patent 1810710 (July 25, 2007) present sleep improvement systems that leverage individual information to control the sleeping environment and monitor the quality of sleep, respectively. For example, sensors monitor physiological signals such as snoring, breathing, body movement, or body temperature, providing a comprehensive view of the user's sleep state. However, these systems focus primarily on adult sleep patterns and are not fully adaptable for infant care because infants have significantly different physiological patterns compared to adults. The sensitivity and placement of sensors might need adjustment for infants, and safety concerns are paramount, as devices designed for adults may not meet the stringent safety standards required for infants. Additionally, the mechanisms used to control the sleeping environment for adults might not be suitable or necessary for infants, who require different thermal environments and monitoring for additional parameters like oxygen saturation levels to prevent conditions like sudden infant death syndrome (SIDS).

[0014] U.S. Published Application No. 20160293042 (‘”042”) (Smilables Inc., Oct. 6, 2016) discloses mechanisms for monitoring an infant's emotional state using an infant monitoring hub. While ‘042 emphasizes emotional state monitoring, the system of ‘042 does not incorporate a wide array of physiological sensors nor does take advantage of federated learning for more comprehensive data analysis and personalized care recommendations .

[0015] Moreover, U.S. Patent Application 20070191692 (Hsu, Aug. 16, 2007) outlines a method for using sensor data to select recommendations for behavioral programs or actions to improve sleep behavior. While the approach is comprehensive, it will lack the ability to customize recommendations based on the unique patterns and needs of an individual infant.

[0016] World Patent Document WO 2005089649 (Sep. 29, 2005) proposes an implantable medical device to determine a patient's sleep quality. This approach, however, may not be suitable or desired for infants due to the invasiveness of die device.

[0017] Therefore, there is a need for an improved infant sleep system and bassinet with that employs advanced sensor technology, including crying recognition, machine learning algorithms, deep learning techniques, and multi-source data for a comprehensive approach to infant sleep and care routines. Further there is a need, for an improved infant sleep system that integrates multiple sensors within a bassinet, employs advanced machine learning algorithms for real-time recommendations, and facilitates third-party development through open APIs, thereby addressing several limitations of the prior art.BRIEF SUMMARY OF THE DISCLOSURE

[0018] The infant sleep system, according to this disclosure, may be incorporated into infant care devices such as a bassinet, crib or incubator and enables comprehensive monitoring and improved management of an infant's sleep routine, providing real-time, personalized recommendations for infant care. Such recommendations are the result of extensive data analysis of the infant's sleep patterns, environmental and physiological factors, feeding times, diaper changes, mood, and other crucial factors affecting sleep and overall health. This system may identify and understand individual patterns, using a collaborative learning model to offer personalized and proactive recommendations for infant care. Furthermore, the system may include a graphical user interface (“GUI”) topresent its findings in an intuitive and user-friendly manner to effectively assist caregivers in implementing optimal care practices.

[0019] In addition, the system may incorporate an advanced Electromechanical Film (EMFi) sensor technology that detects changes in pressure, vibration, and deformation, offering a more detailed understanding of the baby's respiration rate, heart rate, movement, and sleep patterns. Furthermore, parent-reported inputs and environmental factors that may not be captured by the bassinet's sensors are integrated into the system's analysis, creating a holistic view of the baby's health and wellness.

[0020] By incorporating all these elements, the system provides a unique, holistic, and personalized approach to infant care that not only enhances a caregiver's ability to monitor and manage an infant's sleep routine but also educates them on evidence-based care practices, thereby contributing to the overall wellbeing of tire infant.

[0021] Further the infant sleep system, according to this disclosure, introduces an intelligent bassinet designed to significantly enhance the way infant care is managed. This bassinet represents a comprehensive integration of various state-of-the-art technologies within a single unit of infant care equipment. The embedded sensors monitor a broad array of parameters, including the infant's body temperature, movement, breathing patterns, heart rate, and sleep schedules, thereby gaining a comprehensive understanding of the infant's unique routine.

[0022] Further, the system may include seamless integration of advanced machine learning technologies, federated learning algorithms, and predictive modeling techniques, all contained within a single bassinet. Also, the system multi-source data collected not only from the individual infant using the bassinet but also from a large and growing network of similar smart bassinets forming a user base. This data, processed through federated learning, enables the detection of patterns across the user base while ensuring the privacy and security of individual data.

[0023] Also, the system may include a dual- API architecture to enhance functionality and data security. The first API is designed for on-device data processing, where machine learning algorithms directly interact with sensor data to generate real-time insights and recommendations without transmitting raw data externally. This API supports local computations and adaptations, ensuring swift response times and reducing dependency on external data processing.

[0024] The second API manages the secure transmission of processed data to external applications and third-party developers. It is crafted to ensure that data, while beingaccessible for further innovation and application development, remains encrypted and compliant with privacy standards. This second API facilitates the expansion of the ecosystem, allowing third-party developers to create bespoke applications that utilize the insights generated by the bassinet’s sensors, thus contributing to an integrated care solution platform.

[0025] By analyzing this multi-source data, the bassinet system may generate highly personalized, precise, and age-appropriate recommendations based on developmental milestones for individual infants. These recommendations span from optimal feeding and sleep times to room temperature adjustments and alerts about potential health concerns. Also, these recommendations may be provided to the care giver on the GUI.

[0026] The bassinet system gathers extensive physiological and environmental data, processes this data using Al, and provides real-time insights and recommendations. The incorporation of a broad range of sensors combined with federated learning enhances data privacy and accuracy and offers beneficial comprehensive insights and recommendations to bassinet system users (e.g. infant care givers) in caring for an infant.

[0027] Some aspects of this intelligent infant sleep system is its ability to cross- reference data across users, identify patterns among infants with favorable sleep and care routines, and applying these insights to provide evidence-based recommendations to caregivers. Additionally, providing safe data for third-party developers and research institutions brings significant benefits, including fostering innovation, customizability', and rapid development of new applications. This comprehensive approach ensures that the platform not only meets the immediate needs of parents but also contributes to the broader field of infant health and development through data-driven insights and enhanced research capabilities.

[0028] Another aspect of the system includes providing real-time feedback and forward-looking predictions, empowering caregivers to adjust their infant care practices promptly based on the insights provided. In essence, this intelligent bassinet presents a data-driven, scientifically backed system that enhances the effectiveness of infant care. The integrated bassinet, with its wealth of sensor data, Al capabilities, and federated learning, forms part of a learning network of bassinets, each contributing to and benefiting from the collective intelligence of the system. A further beneficial aspect of the system includes providing safe data for third-party developers is the potential for rapid innovation and diverse application development, entrancing die overall ecosystem. This not only benefits consumers by providing a wide range of tailored applications andservices but also offers valuable data for research institutions. Hie rich, anonymized dataset can be used to conduct large-scale studies on infant health and development, leading to breakthroughs in pediatric care and early childhood development research. This setup transforms infant care into a more proactive, informed, and effective process, thereby reducing stress and enhancing the quality-of-care practices.

[0029] In some aspects, the techniques described herein relate to an infant developmental platform including: a bassinet configured to support an infant; a plurality of sensors integrated into the bassinet, the plurality of sensors including at least one physiological sensor, at least one environmental sensor, and at least one audio sensor, the plurality of sensors configured to generate sensor data; a processor operatively connected to the plurality of sensors, the processor configured to receive sensor data from the plurality of sensors and utilize the sensor data in analyzing neurodeve I opmental milestones to generate real-time infant care strategies and developmental insights for neurodevelopment of the infant; a graphical user interface operatively connected to the processor, the graphical user interface configured to display the real-time infant care strategies and developmental insights and receive caregiver feedback data; and a feedback module operatively connected to the processor and configured to adjust processor inference models based on the caregiver feedback data received via the graphical user interface or on changes in sensor data corresponding to infant physiological or behavioral patterns indicative of neurodevelopmental milestones.

[0030] In some aspects, the techniques described herein relate to a platform, wherein the at least one physiological sensor includes at least one of a heart rate sensor, a respiratory' sensor, or a motion sensor.

[0031] In some aspects, the techniques described herein relate to a platform, wherein the at least one environmental sensor is configured to sense at least one of temperature, humidity, or ambient noise.

[0032] In some aspects, the techniques described herein relate to a platform, wherein the processor includes an edge Al accelerator configured for low-latency neurodevelopmental signal interpretation.

[0033] In some aspects, the techniques described herein relate to a platform, wherein the processor is configured to apply reinforcement learning algorithms to sensor data and caregiver feedback data to adapt sleep-related recommendations over time.

[0034] In some aspects, the techniques described herein relate to a platform, further including: a timer operatively connected to the processor, wherein the processor isconfigured to map sensor data over time and display baby behavior predictions and recommendations on the graphical user interface.

[0035] In some aspects, the techniques described herein relate to a platform, wherein the graphical user interface is configured to receives caregiver feedback data including override instructions of at least one recommendation generated by the processor, and the processor is configured to record override instructions and use adaptive planning to refine subsequent recommendations.

[0036] In some aspects, the techniques described herein relate to a platform, wherein the processor is configured to adapt infant care strategies using reinforcement learning based on the infant behavioral patterns and developmental milestones.

[0037] In some aspects, the techniques described herein relate to a platform, further including a data interface configured to: synchronize with third-party data sources including at least one of electronic health records, pediatric wellness platforms, or environmental monitoring systems; and adjust the processor neurodevelopmental milestone generation based on external signals corresponding to infant developmental milestones.

[0038] In some aspects, the techniques described herein relate to a platform, wherein third-party data includes pediatric diagnostic flags or therapy schedules.

[0039] In some aspects, the techniques described herein relate to a system including: a plurality of early development systems, each system of the plurality of systems including: a smart bassinet configured to support an infant, a plurality of sensors operatively connected to the bassinet, the plurality of sensors configured to monitor physiological, environmental, and behavioral parameters of the infant and generate sensor data corresponding to at least one parameter, a local processor operatively connected to the sensors and configured to train an infant-specific developmental model based on sensor data, a communication module operatively connected to the local processor, the communication module configured to transmit encrypted model updates to a federated learning server, and a federated learning module in the local processor, the federated learning module configured to receive and integrate global model improvements derived from aggregated anonymized data from other systems of the plurality of systems; and tire federated learning server in communication with at least two systems of the plurality of systems.

[0040] In some aspects, the techniques described herein relate to a system, wherein the communication module configured is configured to transmit sensor data to thefederated learning server without transmitting any identifiable data of the bassinet or infant.

[0041] In some aspects, the techniques described herein relate to a system, wherein the federated learning server is configured to identify population-level patterns in early- stage neurodevelopment based on communication with the federated learning model of each system of the plurality of systems.

[0042] In some aspects, the techniques described herein relate to a sleep system for a baby including: a surface configured to support the baby; a physiological sensor positioned beneath the surface; such that the physiological sensor is configured to sense from beneath the baby; and at least one lateral sensor connected to the surface and configured to sense the baby from a side of the baby, wherein the at least one lateral sensor and the physiological sensor are configured to sense at least one at least one physiological parameter of the baby and generate corresponding sensor data.

[0043] In some aspects, the techniques described herein relate to a system, wherein the least one lateral sensor extends above the surface.

[0044] In some aspects, the techniques described herein relate to a system, further including: a perimeter edge surrounding the surface; at least one lateral sensory guard extending vertically from the surface, the lateral sensory guard between the perimeter edge and a central portion of the surface; and the at least one lateral sensor on tine lateral sensory guard.

[0045] In some aspects, the techniques described herein relate to a system, further including: a perimeter edge surrounding the surface; a side wall connected to the perimeter edge, the side wall extending vertically, the side wall surrounding the surface and forming an internal space for the baby to rest therein; and an environmental sensor connected to the side wall, the environmental sensor configured to sense environmental parameters including noise and temperature.

[0046] In some aspects, the techniques described herein relate to a system, further including: a processor operatively connected to tire sensors, the processor configured to generate sleep recommendations and alerts based on the sensor data; and a graphical user interface to displ ay the sleep recommendations and alerts to a care giver of the baby.

[0047] In some aspects, the techniques described herein relate to a system, wherein the graphical user interface is configured to receive input from the care giver.

[0048] In some aspects, the techniques described herein relate to a system, wherein the system is a bassinet, crib or incubator.

[0049] In some aspects, the techniques described herein relate to a computer- implemented method of generating infant sleep recommendations using a neural network, including: collecting sensor data, via sensors, from a bassinet or crib, the sensor data corresponding to at least physiological parameters, behavioral events , and environmental conditions ; forming a unified infant dataset by integrating said sensor data with standardized health and developmental reference data from sources external to the bassinet; performing signal processing to extract temporal features and physiological trends, including respiration rate, cry amplitude profiles, and movement variance; training a neural network, in one or more processors, including a convolutional layer and a recurrent layer on the extracted temporal features and physiological trends to associate local behavioral signals with sleep stages and neurodevelopmental indicators; transmitting encrypted local model parameters to a federated learning server, and receiving aggregated global model updates from a plurality of similar infant systems; refining the local model with said global updates and generating adaptive sleep recommendations; and providing said recommendations via a bi-directional graphical user interface, wherein caregiver feedback is recorded and used to adjust future recommendation parameters.

[0050] In some aspects, the techniques described herein relate to a method, wherein collecting infant further includes: using a local infant sleep system including integrated sensors to collect physiological data of the infant and environmental data of an infant's sleeping environment.

[0051] In some aspects, the techniques described herein relate to a method, wherein receiving, at a federated learning module in a server, a plurality of historical datasets further includes: using a plurality of other infant sleep systems including integrated sensors to collect physiological data of the plurality of other infants and environmental data of a plurality of other infant's sleeping environments.

[0052] In some aspects, the techniques described herein relate to a method, wherein collecting standard data further includes: receiving data from computer applications related to infant care.

[0053] In some aspects, the techniques described herein relate to a computer- implemented method of training a neural network to recognize for infant sleep care parameters, the method including: collecting infant data while the infant is resting in a bassinet, crib or incubator, the bassinet, crib or incubator including a sensor configured to sense the infant from beneath the infant, the sensor configured to sense physiologicalparameters or the infant; storing standard data including infant health or infant development parameters; integrating the infant data and the standard data into a local dataset; applying one or more transformations to the local dataset to create a preprocessed dataset, the one or more transformation including handling missing values, normalization, standardization, or noise reduction; applying signal processing to the preprocessed dataset to generate a local training set including physiological signals, physiological trends, health or sleep-related parameters; and training the neural network using the local training set to recognize local historical health and sleep dataset of the local infant.

[0054] In some aspects, the techniques described herein relate to a sleep system for a baby including: a flat surface configured to support the baby; a physiological sensor positioned beneath the flat surface; a mattress over the flat surface and the physiological sensor such that the physiological sensor is configured to sense from beneath the baby; and at least one lateral sensor connected to the flat surface and configured to sense the baby from a side of the baby, wherein the at least one lateral sensor and the physiological sensor are configured to sense at least one physiological parameter of the baby and generate corresponding sensor data.

[0055] In some aspects, the techniques described herein relate to a system, wherein the at least one lateral sensor extends above the mattress.

[0056] In some aspects, the techniques described herein relate to a system, further including: a perimeter edge surrounding the flat surface; at least one lateral sensory guard extending vertically from the flat surface, the lateral sensory guard between the perimeter edge and a central portion of the mattress; and the at least one lateral sensor on the lateral sensory guard.

[0057] In some aspects, the techniques described herein relate to a system, further including: a perimeter edge surrounding the flat surface; a side wall connected to the perimeter edge, the side wall extending vertically, the side wall surrounding the flat surface and forming an internal space for the baby to rest therein; and an environmental sensor connected to the side wall, the environmental sensor configured to sense environmental parameters such as ambient noise and temperature.

[0058] In some aspects, the techniques described herein relate to a system, further including: a processor connected to the sensors, the processor configured to generate sleep recommendations and alerts based on the sensor data; and a graphical user interface to display the sleep recommendations and alert to a care giver of the baby.

[0059] In some aspects, the techniques described herein relate to a system, wherein the graphical user interface is configured to receive input from the care giver.

[0060] In some aspects, the techniques described herein relate to a system, wherein the system is a bassinet, crib or incubator.

[0061] In some aspects, the techniques described herein relate to a computer- implemented method of training a neural network to provide sleep recommendations, information and alerts of an infant, the method including: collecting infant data including physiological parameters, life events and surrounding environment factors; collecting standard data including one or more of health data or educational data; integrating the infant data and standard data into a local dataset; applying one or more transformations to the local dataset to create a preprocessed dataset, the one or more transformation including handling missing values, normalization, standardization, or noise reduction; applying signal processing to the preprocessed dataset to generate a local training set including physiological signals, physiological trends, health or sleep-related parameters; training the neural network using the local training set to recognize local historical health and sleep dataset of the infant; receiving, at a federated learning module in a server, a plurality of historical datasets including the local historical health and sleep dataset and a plurality of other historical datasets corresponding to a plurality of other infants; aggregating the plurality of historical datasets into a federated dataset; training the federated learning module on the federated dataset to identify global patterns and trends and develop infant sleep insights; providing the infant health and sleep insights to the neural network; and using the infant health and sleep insights and local historical data to provide infant health sleep recommendations, via a graphical user interface, to the local infant.

[0062] In some aspects, the techniques described herein relate to a method, wherein collecting infant further includes: using a local infant sleep system including integrated sensors to collect physiological data of the infant and environmental data of the infant's sleeping environment.

[0063] In some aspects, the techniques described herein relate to a method, wherein receiving, at a federated learn ing module in a server, a plurality of historical datasets further includes: using a plurality of other infant sleep systems including integrated sensors to collect physiological data of the plurality of other infants and environmental data of the plurality' of other infant's sleeping environments.

[0064] In some aspects, the techniques described herein relate to a method, wherein collecting standard data further includes: receiving data from computer applications related to infant care.

[0065] In some aspects, the techniques described herein relate to a computer- implemented method of training a neural network to recognize for infant sleep care parameters, the method including: collecting infant data while the infant is resting in a bassinet, crib or incubator, the bassinet, crib or incubator including a sensor configured to sense the infant from beneath the infant, the sensor configured to sense physiological parameters or the infant; storing standard data including infant health or infant development parameters; integrating the infant data and the standard data into a local dataset; applying one or more transformations to the local dataset to create a preprocessed dataset, the one or more transformation including handling missing values, normalization, standardization, or noise reduction; applying signal processing to the preprocessed dataset to generate a local training set including physiological signals, physiological trends, health or sleep-related parameters; and training the neural network using the local training set to recognize local historical health and sleep dataset of the local infant.BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The foregoing summary, as well as the detailed description of the preferred embodiments of the present invention, will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there is shown in the drawings, which are diagrammatic, embodiments that are presently preferred. It should be understood, however, that the present invention is not limited to the precise arrangements and instrumentalities shown. In the drawings:FIG. 1 depicts a front view of a sleep system according to this disclosure;FIG. 2A is prospective of the sleep system of FIG. 1 including torso sensors according to this disclosure;FIG. 2B is a side view of the sleep system depicted in FIG. 2A;FIG. 3 A is a prospective view of the sleep system of FIGS. 1, 2A, and 2B with the upper surface removed;FIG. 3B is a prospective view of the sleep system of FIG. 3 A with the upper, flat surface and sensor in place;FIG. 3C is a top view of the system of FIGS. 2A and 2B ;FIG. 4A is a prospective view of the system of FIGS. 2A and 2B showing the environmental sensor according to this disclosure;FIG. 4B is an exploded view of section A of FIG. 4A;FIG. 5 A and 5B depict side views, according to this disclosure, of the system of FIGS 1 , 2A and 2B including the processor within the support;FIG 6 is a schematic of a bassinet system according to this disclosure;FIG. 7 is a flow diagram of a method of training the neural network within the processor of FIGS 5 A and 5B; andFIG, 8 depicts federated learning among multiple smart infant sleeping systems.DETAILED DESCRIPTION OF THE DISCLOSURE

[0067] Certain terminology is used in the following description for convenience only and is not limiting. As used herein, the words “connected” or “coupled” are each intended to include integrally formed members, direct connections between two distinct members without any other members interposed therebetween and indirect connections between members in which one or more other members are interposed therebetween. As used herein “bassinet” refers to a sleeping apparatus for an infant until 4-6 months as well as a crib utilized by a baby or infant. The terminology includes the words specifically mentioned above, derivatives thereof, and words of similar import.

[0068] An artificial intelligence powered smart infant sleep system, according to this disclosure, may provide a comprehensive solution for managing infant care routines while alleviating stress on caregivers. The system integrates multi-source data collection, multiple sensor technologies, machine learning models, and a user-friendly interface into a single, holistic system.

[0069] The infant sleep system 100 is depicted, for example, in the form of a smart bassinet 5 which may include bassinet insert with lateral sensory guard 18, 19 or as described as “bumpers” in US Patent Nos. 11,357,336 and 11,825,961 both entitled, “Newborn Sleep Insert for Bassinette and Crib”. These two publications mentioned herein are incorporated by reference in their entirety and disclose and describe the methods and / or materials in connection with which the publications are cited. The lateral sensory guard refers to a soft, contoured structure positioned along one or both sides of the infant’s body, configured to gently contact and partially surround tire infant’s torso and / or limbs. The guard may be embedded with one or more sensors to detect pressure, contact, stretch, temperature, or movement, enabling the system to monitor containmentlevels and infant physiological signals. In certain embodiments, the guard is designed to simulate a “hugging” effect, supporting infant co-regulation, calming, and neurodevelopmental benefits.

[0070] Infant sleep system 100 may also include other forms to accommodate the age and size of an infant, or baby that may rest therein. For example, system 100 may be in the form of an incubator to accommodate a pre-mature infant or a crib for an infant older than 6-8 weeks. Also, system 100 may be an early childhood development system for different ages of babies. Further, system 100 may or may not include the lateral sensory guards 18, 19. The smart bassinet 5 may include an array of sensors 20, 21, 24 50, 55 positioned in various locations such as the interior and exterior walls as well as and include lateral sensor guards 18, 19 which may contact portions of the infant such as the torso or arms of the infant, etc. In these positions, the sensors monitor the infant's physiological parameters, such as heart rate, body temperature, movement, and breathing patterns. Moreover, environmental factors that might influence the infant's comfort and sleep, like room temperature, noise levels, and light intensity, are also tracked continuously. These data points, coupled with information inputted manually by parents and integrated from other partner apps are continuously collected and transmitted to an onboard processor for analysis. It is noted that system 100 may include at least one lateral sensory guard 18, 19 or both lateral sensory guards 18, 19.

[0071] System 100 includes integration of multiple sensors to monitor a wide range of physiological and environmental parameters. By leveraging federated learning, the system processes this data to provide personalized, evidence-based recommendations aimed at improving infant sleep and health outcomes. The inclusion of an open API ecosystem further enhances the system’s capabilities by allowing third-party developers to create innovative applications that utilize the insights generated by the bassinet’s sensors.

[0072] Moreover, the data crowd-sourcing approach not only improves the system’s 100 recommendations by learning from a large cohort of infants but also creates invaluable datasets for research and medical institutions. This vast data pool can be used to advance our understanding of infant health and develop new strategies for preventing conditions such as SIDS. The collaboration with research and medical institutions ensures that the system remains at the forefront of scientific and medical advancements, ultimately contributing to the well-being of infants and reducing parental stress and anxiety.

[0073] In essence, this invention provides a holistic solution that combines cutting- edge technology with a collaborative approach to revolutionize infant care, ensuring safer and more effective sleep management for infants and peace of mind for parents.

[0074] This integration of features provides a dynamic, personalized system capable of learning and adapting to the unique needs of each infant and their caregivers. By leveraging advanced technology, machine learning, and a vast dataset, the Al-Powered Smart Bassinet offers a scientifically backed, holistic approach to managing infant care routines. It effectively reduces stress for caregivers while promoting the health, safety, and comfort of infants.

[0075] Referring now to the drawings in detail, wherein like numbers are used to indicate like elements throughout, FIGS. 1 and 2 A- 5B depict a smart infant neurodevelopment platform or infant sleep system 100 including a bassinet 5 including environmental sensor 20, physiological sensor 50, lateral sensory guards 18, 19 and torso or lateral sensors 21, 24, graphical user interface (“GUI”) 70, processor 80 and global federated learning module 200 in remote server 300. Although FIGS. 1 and 2A- 5B depict on environmental sensor 20 and one physiological sensor 50, other configurations are contemplated with additional sensors 20 and / or 50.

[0076] Bassinet 5 includes sidewall 15, sleep surface or mattress 30, upper or flat surface 26, and support 60. Sidewall 15 extends between top rail 40 and upper surface 26, and sidewall 15 is connected to upper surface 26 at the perimeter edge 27 of upper surface 26. Further, sidewall 15 may be formed of mesh and may entirely surround upper surface 26.

[0077] Surface 26 extends horizontally and provides a flat surface to support a resting infant or baby 10. Preferably, mattress 30 rests on upper surface 26 and physiological sensor 50 is therebetween or otherwise incorporated into the bassinet 5 in an operationally suitable manner. As such, mattress 30 may abut upper surface 26 and sensor 50. Also, sensor 50 may be held in place on upper surface 26 with an adhesive or clips, etc. As shown in FIG. 3B-3C, it is preferable that sensor 50 is centrally positioned on upper surface 26 such that sensor 50 is configured to be beneath a baby 10 resting on mattress 30.

[0078] Physiologic sensor 50 is positioned between the bottom surface of the mattress 30 and upper surface 26. Sensor 50 may be an electromagnetic film sensor such as an EMFIT or photoplethysmogram, Respiratory Inductive Plethysmography (RIP) Sensor, Piezoelectric Respiratory Sensor, or Accelerometer. Sensor 50 may be electricallypowered and communicatively linked via a connection interface routed through column 60, a structural housing module located on the bassinet frame. Column 60 may contain processing circuitry, power distribution hardware, and connectivity modules configured to both supply power to sensor 50 and transmit physiological data to processor 80. Sensor 50 may monitor one or more physiological parameters of the infant including heart rate, body temperature, movement, and breathing patterns. For each of the parameters monitored, sensor 50 communicates physiological data 52 to processor 80.

[0079] In addition to sensor 50 located under the mattress 30, system 100 also includes sensors 21, 24 positioned on the lower la teral sides of the bassinet 5 and contained in or on lateral sensory' guards 19, 18, respectively. As lateral sensory guards 19, 18 extend vertically above mattress 30, sensors 21, 24 are configured to sense the torso or side of baby 10. Lateral sensors 21, 24 may provide additional readings from the lateral torso of the infant, capturing a more comprehensive set of physiological data. The integration of sensors 21, 24 allows for enhanced monitoring of the infant's breathing patterns, movements, and other vital signs, contributing to a more accurate and holistic understanding of the infant’s well-being.

[0080] Environmental sensor 20 may be connected to top rail 40. This position allows sensor 20 to measure one or multiple environmental factors including room temperature, humidity noise levels, and light intensity, etc. surrounding bassinet 5. Sensor 20 generates environmental data 22 corresponding to each measured factor and communicates data 22 to processor 80. Alternatively, sensor 20 may be connected to an outer or external surface of sidewall 15 or top rail 40 or spaced apart from the basinet 5 and communicate 'wirelessly with processor 80. When sensor 20 is connected to bassinet 5, sensor 20 may communicate with processor 80 via a wired 23 or wireless connection. Although one environmental sensor 20 is discussed herein, multiple sensors may be utilized and positioned exterior to bassinet 5 and / or interior to bassinet 5.

[0081] Regarding noise or audio sensing, sensor 20 may include or comprise a microphone or a microphone array configured to monitor environmental sounds and vocalizations of the infant 10. For example, sensor 20 may recognize and record crying events, including metadata such as start time, duration, amplitude (volume), frequency profile, and pattern dynamics. Sensor 20 generates audio data corresponding to environmental and infant-originated sounds, such as cry data 57, and communicates such data to processor 80 for further analysis and classification.

[0082] Processor 80 is in communication with sensors 20, 21, 24, 50 and may be embedded within bassinet 5, and processor 80 includes various systems, as shown in FIG. 6, operating within processor 80. For example, processor 80 may be within support 60 as shown in FIGS. 5A and 5B. Internally, processor 80, as shown in FIG. 6, may include machine learning module 110, at least one convolutional neural network (“CNN”) module 120, at least one recurrent neural network module 130 (“RNN”), predictive model module 140, timer 150, sensor database 155, local federated learning module 160, caregiver feedback module 170, communication module 180, sleep database 190, and developmental database 195. Modules 110, 120, 130, 140 may receive and process sensor data22, 52 from sensors 20, 21, 24, 50. Machine learning module 110 applies advanced machine learning algorithms and predictive modeling technologies to data 22, 52. Further, module 110 includes CNN module 120 and RNN module 130.CNN module 120 extracts patterns from data 22, 52 and identifies correlations between different variables (e.g. physiological, environmental, health and sleep related). RNN module 130 uses data 52 to understand and / or map the baby’s sleep behavior, based on sensor data and / or care giver data, over time with reference to timer 150. Predictive model module 140 employs historical sleep pattern data and caregiver feedback to anticipate potential sleep disruptions.

[0083] Processor 80 is positioned within bassinet 5, preferably integrated into structural column 60 or other protected housing. Processor 80 is configured as an on- device Al accelerator or edge processing module optimized for low-latency, real-time interpretation of infant physiological and environmental signals. In some embodiments, processor 80 is capable of running trained machine learning models directly on-device, thereby enabling personalized care interventions and adaptive responses without reliance on external servers.

[0084] As an edge Al processor, processor 80 enables rapid inference model execution, reduced latency, and improved data privacy by avoiding cloud-based roundtrips and retaining sensitive infant data locally. Local processing may also reduce bandwidth consumption by performing signal filtering, preprocessing, or classification on-device before optionally transmitting aggregated or anonymized data externally. In some embodiments, processor 80 may also coordinate multi-sensor fusion and contribute to reinforcement learning loops or federated model updates. Also, the edge Al processor allows system 100 to operate in real time. That is, sy stem 100 may receive sensor data 22, 52 and provide recommendations to care givers via GUI 70 in real time.

[0085] Sensor database 155 may store any data 22, 52, 57 received from sensors 20, 21, 24, 50.

[0086] Parent or caregiver feedback module 170 receives feedback, via a bidirectional GUI 70, from infant caregivers (e.g. parent(s), guardian, and / or nurse) as whether or not recommendations made by processor 80 are effective in assisting infant / baby 10 to improve sleep habits. Timer 150 may include a chronological tinier, or other types of timers needed to monitor the infant 10 and / or environment. Module 170 is also configured to display on the GUI 70 any recommendations generated by modules 110, 140, 160, and 185 for the caregivers to improve the infant sleep habits. Communications module 180, as shown in Figure 6, enables wired and / or wireless communication protocols (e.g., UHF radio, Wi-Fi, Bluetooth), allowing data exchange between processor 80 and external systems including caregiver devices (e.g., GUI 70), cloud servers, or local edge nodes.

[0087] Cry module 185 receives audio data 57 from environmental sensor 20. Module 185 includes advanced cry recognition capabilities, utilizing audio data 57 and machine learning algorithms to distinguish between different types of crying and alert caregivers to potential needs or issues via GUI 70.

[0088] GUI 70 may also display visualizations of neurodevelopmental progress over time, derived from sensor data 22, 52, 57 and developmental milestone models. Caregiver interactions such as acceptance, rejection, or override of recommendations may be logged and analyzed over time, by processor 80, to refine adaptive recommendation strategies. Feedback latency — defined as the delay between a recommendation and caregiver response — may be used as an additional input for tuning the responsiveness of the system and adjusting future recommendations.

[0089] Sleep database 190 may include the historical sleep pattern data and care giver feedback data. Developmental database 195 includes infant developmental milestones data for the various stages of an infant’s life. Each database 190, 195 may be referenced by modules 110, 120, 130, 140 for use in system 100 algorithms, thereby offering age- appropriate recommendations and insights.

[0090] Local federated learning module 160, housed within bassinet 5, is configured to preprocess and transmit anonymized infant-specific training data derived from realtime physiological and behavioral signals. This data is sent to a global federated learning module 200 located on a remote server 300, as shown in Figure 6.

[0091] Global federated learning module 200, as shown in FIG. 8, aggregates local model updates from a distributed network of similarly equipped smart bassinets 5. It maintains a continuously evolving dataset 220 of anonymized infant sleep, environment, and health-related information, which supports training of a shared Al model across the population without transmitting raw infant data.

[0092] By analyzing this growing dataset 220, system 100, via local module 160, identifies common neurodeve lopmental sleep patterns, stress responses, and effective caregiver interventions. This enables real-time adaptation of the infant care model to the individual baby.

[0093] Based on these insights, processor 80 may generate personalized recommendations, which are presented via GUI 70 to caregivers. These may include adjustments to routines, sleep timing, environmental conditions, or soothing strategies, ultimately aimed at improving sleep quality, supporting infant neurodevelopment, and enhancing overall well-being.

[0094] GUI 70 may be attached to base 60, as shown in FIG. 2 and / or GUI 70 may be incorporated into a stand-alone, portable, or remote device such as smart phone, laptop, desktop and / or tablet, etc. GUI 70 receives and displays personalized recommendations, corresponding infant 10 and generated by processor 80. Additionally, GUI 70 receives caregiver feedback, enabling the processor 80 of system 100 to continuously refine its understanding and adaptation to the infant's sleep patterns and routines. Caregiver feedback may be both positive and negative regarding the adequacy if system 100 recommendations. The intuitive design of the interface including a display screen, employing user-friendly graphics and straightforward language, makes the system's recommendations easily understood and implemented by caregivers.

[0095] Additionally, system 100 may include a dual-Application Programming Interface (“API”) architecture to enhance functionality and data security. The first API 90 is designed for on-device data processing, where machine learning algorithms directly interact with data stored in sensor database 155 to generate real-time insights and recommendations without transmitting raw data externally. This API supports local computations and adaptations, ensuring swift response times and reducing dependency on external data processing.

[0096] The second API 95 manages the secure transmission of processed data to external applications and third-party developers. It is crafted to ensure that data, while being accessible for further innovation and application development, remains encryptedand compliant with privacy standards. This second API 95 facilitates the expansion of the ecosystem, allowing third-party developers to create bespoke applications that utilize the insights generated by the bassinet’s sensors, thus contributing to an integrated care solution platform and recommendations provided to care givers.

[0097] FIG. 7 is a schematic of a method 400, according to this disclosure, of promoting infant sleep and development using smart bassinet infant sleep system 100. Additionally, method 400 is a computer-implemented method of training a neural network to provide sleep recommendations of a local infant.

[0098] Step 410 includes data collection. The embedded sensors or remote sensors 20, 50 in bassinet 5 continuously collect data 22, 52, 57 relating to the infant's physiological parameters such as body temperature, movement, crying patterns, breathing patterns, and heart rate. Additional parameters such as environmental factors (room temperature, noise, light levels) are also recorded. Moreover, caregivers can manually input data, via GUI 70, such as feeding times, diaper changes, or notable behaviors. Standar d data such as data from other partner apps, such as nutrition or healthcare apps, and educational health or developmental data may also be integrated. Data 22, 52, 57 may be collected with sensors 20, 50, 21, 24.

[0099] Next, step 420 includes data integration. All data collected in step 410 — including data 22, 52, 57 from sensors 20, 50, inputs from caregivers via GUI 70, and third-party partner app data — are synchronized and formatted into a local temporal profile associated with infant 10. This locally constructed data structure forms the basis for subsequent real-time analysis, recommendation generation, and optionally, local model training within processor 80 or local federated learning module 160.[000100] Further, in step 430, local data preprocessing is performed by processor 80 and / or local federated learning module 160. The integrated temporal profile associated with infant 10 is subjected to a series of preprocessing operations to ensure data quality and consistency. These operations may include detection and handling of missing or outlier values, normalization and standardization of sensor signals (e.g., heart rate, movement, audio levels), synchronization of multimodal data streams, and reduction of noise or artifacts.[000101] Next, in step 440, real-time physiological signal processing is performed by processor 80. The system extracts key features from multimodal data streams — such as respiratory rate from chest movement signals, heart rate variability from acoustic or plethysmographic signals, and motion artifacts from accelerometers. Signal enhancementtechniques, such as filtering and smoothing, are applied to improve data fidelity. Processor 80 then analyzes these features to identify trends, anomalies, and biomarkers indicative of infant sleep stages, stress levels, and neurodevelopmental state.[000102] Next, in step 450, local machine learning analysis is performed by processor 80 in coordination with machine learning module 110, convolutional neural network (CNN) module 120, and recurrent neural network (RNN) module 130. The preprocessed physiological and behavioral data are input into trained models configured to detect individual patterns in infant sleep, stress recovery, and neurodevelopmental state transitions.[000103] These models may include supervised and deep learning architectures, such as CNNs for spatial pattern recognition (e.g., breathing variability) and RNNs for temporal sequence prediction (e.g., transitions between sleep stages or crying bouts). System 100 utilizes a combination of historical data from infant 10 and population- level model updates (e.g., from federated learning module 160) to continuously adapt to the unique needs, routines, and developmental trajectory of the local infant.[000104] In step 460, federated learning is initiated to enhance system-wide model performance while preserving individual data privacy. System 100 participates in a distributed network 310 of peer smart bassinets and shares model parameter updates — such as learned weights or gradient differentials — from the local machine learning analysis. These updates are anonymized and aggregated locally before being securely transmitted to global federated learning module 200 via communications module 180. No raw infant data is transmitted. FIG. 8 is a schematic of federated learning among a plurality of bassinet systems (eg., Bassinet A, B, C) similar or the same as system 100 which form network 310 of which system 100 is a component.[000105] Global module 200 integrates the updates from multiple bassinets (e.g., Bassinet A, B, C), refines a shared global model, and periodically distributes improved model weights back to participating systems. This allows system 100 to benefit from broader population-level learning while continuously personalizing to infant 10. System 100 may thus operate as both a node and a beneficiary within network of smart bassinets 310.[000106] In step 470, dataset 220 is developed, curated, and continuously refined within global federated learning module 200. This dataset comprises anonymized and aggregated outputs from local machine learning modules across a distributed network of smart bassinets 310. Data types may include derived sleep-state transitions, heart ratevariability metrics, crying patterns, environmental responses, and caregiver feedback metadata.[000107] Module 200 analyzes this aggregated dataset to detect global trends, classify developmental archetypes, and benchmark individual infants against normative developmental patterns. Over time, dataset 220 evolves through continuous input, statistical validation, and feedback from population-level model performance, thus forming the basis for population-wide neurodevelopmental insights and predictive care logic.[000108] In step 480, personalized recommendation generation is performed by processor 80 using both local insights (from step 450) and global patterns (from steps 460 and 470). These recommendations are tailored to the specific age, physiological profile, and behavioral patterns of infant 10. Processor 80 integrates real-time sensor signals with historical and comparative data to determine optimal care parameters such as individualized sleep windows, feeding intervals, environmental settings (e.g., temperature and light), and deviations from expected neurodevelopmental patterns.[000109] Recommendations are communicated to the caregiver through GUI 70 as timesensitive alerts, visual cues, or care summaries. The system may also highlight potential concerns, such as irregular sleep-wake cycles or prolonged stress recovery periods, prompting further observation or medical consultation.[000110] Step 490 includes feedback provision and caregiver loop integration. System 100 delivers real-time, context-aware feedback to caregivers via GUI 70, which may be integrated into the smart bassinet 5 or accessed remotely through a secure mobile application. This feedback includes visualizations of infant 10's sleep patterns, stress recovery metrics, and recommended care adjustments — presented in a caregiver-friendly, actionable format.[000111 ] Caregiver responses, such as confirming actions taken, rejecting suggestions, or reporting observed behaviors, are captured through GUI 70 and processed by parent feedback module 170. These responses are used to fine-tune both local learning (via module 110) and global model refinement (via federated learning module 200), completing the adaptive, closed feedback loop.[000112] Step 500 includes caregiver action and outcome recording. Based on the personalized recommendations provided in step 480 and real-time feedback from step 490, the caregiver may take specific actions such as modifying the infant’s sleep environment, adjusting feeding or nap schedules, or initiating soothing interventions.[000113] GUI 70 enables the caregiver to record these actions and report observed outcomes, such as improved sleep onset, reduced crying duration, or behavioral changes, using structured or free-form inputs. These inputs are processed by feedback module 170 and linked to corresponding recommendation sets, enabling processor 80 and modules 110-130 to evaluate effectiveness and update predictive logic.[000114] This caregiver- system interaction loop forms the basis of a dynamic, behavior- aware feedback engine that continuously adapts to the infant’s developmental trajectory and caregiver style.[000115] Step 510 includes continuous machine learning adaptation and system refinement. System 100 continuously incorporates new data streams — including updated sensor data, caregiver feedback, and behavioral outcomes — into its machine learning modules 110, 120, and 130. This enables the system to re-tune model weights, update prediction thresholds, and refine recommendation logic in response to the infant’s evolving physiological and behavioral profile.[000116] As the infant matures, system 100 adjusts its guidance to align with age- appropriate neurodevelopmental expectations, ensuring recommendations remain developmentally relevant. This continuous adaptation process strengthens both local predictive accuracy and the robustness of global patterns identified through federated learning.[000117] Method 400 illustrates the closed-loop, continuously adaptive nature of the smart bassinet system 100. By integrating multi-source data collection (sensor signals, environmental metrics, caregiver input), real-time signal processing, edge-based machine learning, and federated model sharing, the system achieves a uniquely comprehensive and dynamic approach to infant neurodevelopmental care.[000118] Unlike traditional infant monitoring systems, method 400 does not merely display metrics, it actively interprets them, personalizes care insights, and incorporates caregiver validation to evolve its logic. This end-to-end, evidence-based framework positions system 100 as a next-generation infant care platform, capable of delivering timely, personalized interventions and continuously optimizing its guidance as the infant grows.[000119] It will be appreciated by those skilled in the art that modifications, substitutions, and enhancements may be made to the embodiments described herein without departing from the broad inventive concept. For example, variations in sensor technologies, machine learning model architectures, or user interface modalities may beadopted to meet specific clinical, regulatory, or regional requirements while remaining within the scope of the invention. Accordingly, this invention is not limited to the particular embodiments disclosed but is intended to cover all functional equivalents and evolutions thereof, consistent with the spirit and scope of the invention as defined in the appended claims.

Claims

Claims:

1. An infant developmental platform comprising: a bassinet configured to support an infant; a plurality of sensors integrated into the bassinet, the plurality of sensors including at least one physiological sensor, at least one environmental sensor, and at least one audio sensor, the plurality of sensors configured to generate sensor data; a processor operatively connected to the plurality of sensors, the processor configured to receive sensor data from the plurality of sensors and utilize the sensor data in analyzing neurodevelopmental milestones to generate real-time infant care strategies and developmental insights for neurodevelopment of the infant; a graphical user interface operatively connected to the processor, the graphical user interface configured to display the real-time infant care strategies and developmental insights and receive caregiver feedback data; and a feedback module operatively connected to the processor and configured to adjust processor inference models based on the caregiver feedback data received via the graphical user interface or on changes in sensor data corresponding to infant physiological or behavioral patterns indicative of neurodevelopmental milestones.

2. The platform of claim 1, wherein the at least one physiological sensor comprises at least one of a heart rate sensor, a respiratory sensor, or a motion sensor.

3. The platform of claim 1, wherein the at least one environmental sensor is configured to sense at least one of temperature, humidity, or ambient noise.

4. The platform of claim 1, wherein the processor comprises an edge Al accelerator configured for low-latency neurodevelopmental signal interpretation.

5. The platform of claim 1, wherein the processor is configured to apply reinforcement learning algorithms to sensor data and caregiver feedback data to adapt sleep-related recommendations over time.

6. The platform of claim 1, further comprising: a timer operatively connected to the processor, wherein the processor is configured to map sensor data over time and display baby behavior predictions and recommendations on the graphical user interface.

7. The platform of claim 1, wherein the graphical user interface is configured to receives caregiver feedback data including override instructions of at least one recommendation generated by the processor, and the processor is configured to record override instructions and use adaptive planning to refine subsequent recommendations.

8. The platform of claim 1, wherein the processor is configured to adapt infant care strategies using reinforcement learning based on the infant behavioral patterns and developmental milestones.

9. The platform of claim 1, further comprising a data interface configured to: synchronize with third-party data sources including at least one of electronic health records, pediatric wellness platforms, or environmental monitoring systems; and adjust the processor neurodevelopmental milestone generation based on external signals corresponding to infant developmental milestones.

10. The platform of claim 8, wherein third-party data includes pediatric diagnostic flags or therapy schedules.

11. A system comprising: a plurality of early development systems, each sy stem of the plurality of systems including: a smart bassinet configured to support an infant, a plurality of sensors operatively connected to the bassinet, the plurality of sensors configured to monitor physiological, environmental, and behavioral parameters of the infant and generate sensor data corresponding to at least one parameter, a local processor operatively connected to the sensors and configured to train an infant-specific developmental model based on sensor data, a communication module operatively connected to tire local processor, the communication module configured to transmit encrypted model updates to a federated learning server, and a federated learning module in the local processor, the federated learning module configured to receive and integrate globalmodel improvements derived from aggregated anonymized data from other systems of the plurality of systems; and the federated learning server in communication with at least two systems of the plurality of systems.

12. The system of claim 11, wherein the communication module configured is configured to transmit sensor data to the federated learning server without transmitting any identifiable data of the bassinet or infant.

13. The system of claim 11 , wherein the federated learning server is configured to identify population-level patterns in early-stage neurodevelopment based on communication with the federated learning model of each system of the plurality of systems.

14. A sleep system for a baby comprising: a surface configured to support the baby; a physiological sensor positioned beneath the surface; such that the physiological sensor is configured to sense from beneath the baby; and at least one lateral sensor connected to the surface and configured to sense the baby from a side of the baby, wherein the at least one lateral sensor and the physiological sensor are configured to sense at least one at least one physiological parameter of the baby and generate corresponding sensor data.

15. The system of claim 14, wherein the least one lateral sensor extends above the surface.

16. The system of claim 14, further comprising: a perimeter edge surrounding the surface; at least one lateral sensory guard extending vertically from the surface, the lateral sensory guard between the perimeter edge and a central portion of the surface; and the at least one lateral sensor on the lateral sensory guard.

17. The system of claim 14, further comprising: a perimeter edge surrounding the surface; a side wall connected to the perimeter edge, the side wall extending vertically, the side wall surrounding the surface and forming an internal space for the baby to rest therein; andan environmental sensor connected to the side wall, the environmental sensor configured to sense environmental parameters including noise and temperature.

18. The system of claim 14, further comprising: a processor operatively connected to the sensors, the processor configured to generate sleep recommendations and alerts based on the sensor data; and a graphical user interface to display the sleep recommendations and alerts to a care giver of the baby.

19. The system of claim 18, wherein the graphical user interface is configured to receive input from the care giver.

20. The system of claim 14, wherein the system is a bassinet, crib or incubator.

21. A computer-implemented method of generating infant sleep recommendations using a neural network, comprising: collecting sensor data, via sensors, from a bassinet or crib, the sensor data corresponding to at least physiological parameters, behavioral events , and environmental conditions ; forming a unified infant dataset by integrating said sensor data with standardized health and developmental reference data from sources external to the bassinet; performing signal processing to extract temporal features and physiological trends, including respiration rate, cry amplitude profiles, and movement variance; training a neural network, in one or more processors, comprising a convolutional layer and a recurrent layer on the extracted temporal features and physiological trends to associate local behavioral signals with sleep stages and neurode velopmental indi c ators ; transmitting encrypted local model parameters to a federated learning server, and receiving aggregated global model updates from a plurality of similar infant systems; refining the local model with said global updates and generating adaptive sleep recommendations: and providing said recommendations via a bi-directional graphical user interface, wherein caregiver feedback is recorded and used to adjust future recommendation parameters.

22. The method of claim 21, wherein collecting infant further comprises:using a local infant sleep system including integrated sensors to collect physiological data of the infant and environmental data of an infant’s sleeping environment.

23. The method of claim 22, wherein receiving, at a federated learning module in a server, a plurality of historical datasets further comprises: using a plurality of other infant sleep systems including integrated sensors to collect physiological data of the plurality of other infants and environmental data of a plurality of other infant’s sleeping environments.

24. The method of claim of claim 21, wherein collecting standard data further comprises: receiving data from computer applications related to infant care.

25. A computer-implemented method of training a neural network to recognize for infant sleep care parameters, the method comprising: collecting infant data while the infant is resting in a bassinet, crib or incubator, the bassinet, crib or incubator including a sensor configured to sense the infant from beneath the infant, the sensor configured to sense physiological parameters or the infant; storing standard data including infant health or infant development parameters; integrating the infant data and the standard data into a local dataset; applying one or more transformations to the local dataset to create a preprocessed dataset, the one or more transformation including handling missing values, normalization, standardization, or noise reduction; applying signal processing to the preprocessed dataset to generate a local training set including physiological signals, physiological trends, health or sleep- related parameters; and training the neural network using the local training set to recognize local historical health and sleep dataset of the local infant.

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