JP2018514814A - High-function infant monitoring system, infant monitoring hub and infant learning acceptability detection system - Google Patents

Abstract

It provides mechanisms and techniques for monitoring infants more effectively to improve care content and infant development. The system can include a wearable infant monitoring device, a transmission interface and a monitoring hub. The wearable infant monitoring device includes sensors for detecting infant activity and posture. The transmission interface transmits measurement data related to infant activity and posture. The monitoring hub includes a processor configured to receive measurement data and to determine acceptability for infant learning. Next, based on the child's acceptability for learning, the child is provided with learning materials through the display interface. [Selection] Figure 3

Description

The present disclosure relates to an infant monitoring apparatus.
In one example, the invention relates to a mechanism for providing a high performance infant monitoring system.
In one example, the present invention relates to a mechanism for providing a wearable infant monitoring device.

  Conventional infant monitoring systems are audio or visual monitors that collect auditory or visual information by remote control and communicate this information to other devices so that parents or other caregivers can view or hear that information. including. As an example, a microphone may be placed near an infant, such as a bedroom lamp or table, and a remote speaker may be placed near a caregiver in another location, such as another room. Thereby, a caregiver can hear a child's crying voice. Some surveillance systems include a video camera arranged to record the infant's movement and posture. The caregiver can watch the infant's video from a remote device, such as a dedicated surveillance device or smart phone.

<Cross-reference of related applications>
This patent application was filed on April 5, 2015 by Pradeep under 35 USC 35, 120, but abandoned US Patent Application No. 14 / 679,006 (SMLBP002) "INTELLIGENT INFANT MONITORING SYSTEM" US patent application No. 14 / 681,902 (SMLBP002C1) “INTELLIGENT INFANT MONITORING SYSTEM” filed on Apr. 8, 2015, which is a continuation application of the US patent application. This patent application was filed on April 5, 2015 by Pradeep under 35 USC 35, 120, but is abandoned in US Patent Application No. 14 / 679,008 (SMLBP003) "INFANT MONITORING HUB" Claims priority to continuation, US patent application Ser. No. 14 / 681,904 (SMLBP003C1) “INFANT MONITORING HUB” filed Apr. 8, 2015. This patent application was filed on April 5, 2015 by Pradeep under 35 USC 35, 120, but was abandoned US patent application No. 14 / 679,010 (SMLBP004) "INFANT LEARNING RECEPTIVITY DETECTION SYSTEM. Claiming the priority of US patent application No. 14 / 681,906 (SMLBP004C1) “INFANT LEARNING RECEPTIVITY DETECTION SYSTEM,” filed April 8, 2015, which is a continuation application of “ Incorporated herein by reference for all purposes.

  Conventional systems allow infants' voice and video to be monitored by a caregiver from a remote device, but these monitoring systems are limited to providing only basic monitoring of infants. In fact, this surveillance system allows the caregiver to see and hear the infant at different locations, such as another room in the house. The caregiver must imagine the infant's demands, mood, health and happiness from the sounds and images transmitted through the surveillance system. Some wearable devices provide the child with basic heartbeat and temperature information. However, today's surveillance systems are in fact very limited. Caregivers can benefit greatly from a more robust surveillance system to improve infant care and development.

  The present invention provides a mechanism and technique for more effectively monitoring an infant to improve care content and infant development. The system can include a wearable infant monitoring device, a transmission interface and a monitoring hub. The wearable infant monitoring device includes sensors for detecting infant activity and posture. The transmission interface transmits measurement data related to infant activity and posture. The monitoring hub includes a processor configured to receive measurement data and to determine acceptability for infant learning. Next, based on the child's acceptability for learning, the child is provided with learning materials through the display interface.

  The present invention provides a mechanism and technique for more effectively monitoring an infant using an infant monitoring hub to improve care content and infant development. The infant monitoring hub can include an infant monitoring device interface, a processor, persistent storage, and a platform interface. The infant monitoring apparatus receives measurement data wirelessly transmitted from the infant monitoring apparatus including sensors for tracking the activity and posture of the infant. The processor processes the measurement data to select and customize the infant learning material associated with the infant monitoring device. The persistent storage stores learning material provided from a remote platform that receives information from multiple infant monitoring hubs. The platform interface facilitates information exchange with a remote platform that analyzes measurement data to identify multiple windows and corresponding learning materials.

  The present invention provides mechanisms and techniques for more effectively monitoring infants to improve care content and infant development. The system can include an infant monitoring device and a monitoring hub. The infant monitoring device includes sensors that collect measurement data. The monitoring hub receives the measurement data and analyzes the measurement data associated with the learning acceptability model received from the remote platform. The measurement data is analyzed to predict the time and duration that the infant corresponding to the infant monitoring device can accept learning.

(Aspect 1) A wearable infant monitoring device including a plurality of sensors configured to detect infant activity and posture;
A transmission interface configured to transmit measurement data related to the activity and position of the infant;
A system comprising a processor configured to determine acceptability of the infant for learning and a monitoring hub configured to receive measurement data,
Each learning material is provided to a caregiver through a display interface based on the child's acceptability for learning.

(Aspect 2) The system according to aspect 1, wherein the processor determines acceptability of the infant for learning based on a duration of smile and gaze.

(Aspect 3) The system according to aspect 1, wherein the processor determines acceptability of the infant for learning based on smile and gaze intensity.

(Aspect 4) The system of aspect 1, wherein the processor processes the data measurements to customize learning content based on a developmental age associated with the infant.

(Aspect 5) The system according to aspect 1, wherein the monitoring hub further comprises a persistent storage device configured to store learning content and each schedule.

(Aspect 6) The system of aspect 1, wherein the monitoring hub further comprises a permanent storage device configured to store each infant model and each table.

(Aspect 7) In aspect 1, the monitoring hub further comprises a platform interface configured to facilitate communication between the monitoring hub and the platform, wherein the platform aggregates data from a plurality of infant monitoring devices. The described system.

(Aspect 8) The system according to aspect 1, further comprising an external device.

(Aspect 9) The system according to aspect 8, wherein the external device is a camera.

(Aspect 10) The system according to aspect 9, wherein the external device is an audio sensor.

(Aspect 11) The system according to aspect 9, wherein the external device is a projector.

(Aspect 12) The system according to aspect 9, wherein the external device is a tablet device.

(Aspect 13) The system according to aspect 1, wherein the remote infant monitoring hub is a mobile device.

(Aspect 14) The system according to aspect 1, wherein the plurality of sensors includes a triaxial accelerometer, an electrical skin reaction (GSR) detector, a gyroscope, and a body temperature sensor.

(Aspect 15) The system according to aspect 1, wherein the transmission interface is a Bluetooth Low Energy (BLE) interface.

(Aspect 16) A wearable infant monitoring device including a plurality of sensors configured to detect infant activity and posture;
A transmission interface configured to transmit measurement data related to the activity and position of the infant;
A device comprising a charging interface,
A monitoring hub is configured to receive measurement data from the wearable infant monitoring device and includes a processor configured to determine acceptability of the infant for learning, each learning material being the child's learning A device provided to caregivers through a display interface based on their acceptability for.

(Aspect 17) The apparatus according to Aspect 16, wherein the processor determines acceptability of the infant for learning based on a duration of smile and gaze.

(Aspect 18) The apparatus according to Aspect 16, wherein the processor determines acceptability of the infant for learning based on smile and gaze intensity.

(Aspect 19) The apparatus according to aspect 16, wherein the processor processes the data measurements to customize learning content based on a developmental age associated with the infant.

(Aspect 20) The apparatus of aspect 16, wherein the monitoring hub further comprises a persistent storage device configured to store learning content and each schedule.

(Aspect 21) An infant monitoring device interface configured to receive measurement data wirelessly transmitted from an infant monitoring device including a plurality of sensors configured to track the activity and posture of the infant;
A processor configured to process the measurement data to select and customize learning material for an infant associated with the infant monitoring device;
Persistent storage configured to store learning material provided by a remote platform configured to receive information from a plurality of infant monitoring hubs associated with the plurality of infants;
An infant monitoring hub comprising a platform interface configured to exchange information with the remote platform,
An infant monitoring hub, wherein the remote platform is configured to analyze measurement data to identify a plurality of receptive windows associated with the plurality of infants and learning material suitable for the plurality of receptive windows.

(Aspect 22) The infant monitoring hub according to aspect 21, further comprising a display configured to allow a caregiver to interact with the device.

(Aspect 23) The infant monitoring hub according to Aspect 21, wherein the measurement data includes movement, body temperature, and body position.

Aspect 24. The infant monitoring of aspect 21, wherein the platform identifies multiple receptive windows using data from one or more of a directional detector, a gaze detector, a shared attention detector, and a cognitive detector. Hub.

(Aspect 25) The infant monitoring hub according to aspect 21, wherein the measurement data includes whether the infant is in a prone position or a supine position.

(Aspect 26) The infant monitoring hub according to aspect 21, wherein the measurement data includes an electric skin reaction.

(Aspect 27) The infant monitoring hub according to aspect 21, wherein the processor is configured to process the measurement data into observations about the infant.

(Aspect 28) The infant monitoring hub according to aspect 27, wherein the observation includes a state of sleep, mobility, stress, posture, comfort, health, vigilance or intelligibility of the infant.

(Aspect 29) The infant monitoring hub according to aspect 21, wherein the processor is configured to process the measurement data into a guess about the infant.

(Aspect 30) The infant monitoring hub according to aspect 29, wherein the inference includes acceptability of the infant for learning, infant happiness, caregiver presence, environmental factors, infant safety or infant emotional state.

(Aspect 31) The infant monitoring hub according to aspect 21, wherein the processor is further configured to customize the learning material for the particular infant.

(Aspect 32) The infant monitoring hub according to aspect 21, wherein the persistent storage device is further configured to store schedules, models and tables received from the platform.

(Aspect 33) The infant monitoring hub according to aspect 21, further comprising a peripheral device.

(Aspect 34) The infant monitoring hub according to aspect 21, wherein the peripheral device is a camera, a microphone, or a projector.

(Aspect 35) An interface configured to receive measurement data transmitted wirelessly from an infant monitoring device including a plurality of sensors configured to track infant activity and posture;
A processor configured to process the measurement data;
A persistent storage configured to store learning material provided from a remote platform configured to receive information from a plurality of infant monitoring hubs associated with the plurality of infants;
A remote platform interface configured to exchange information with the remote platform, the system comprising:
The remote platform is configured to analyze measurement data to identify a plurality of receptive windows associated with the plurality of infants and learning material suitable for the plurality of receptive windows.

(Aspect 36) The system according to Aspect 35, wherein the measurement data includes whether the infant is lying down or lying on its back.

Aspect 37. The infant monitoring hub according to aspect 35, wherein the processor is configured to process the measurement data into observations about the infant.

(Aspect 38) The infant monitoring hub according to aspect 37, wherein the observation includes the infant's sleep, mobility, stress, posture, comfort, health, alertness, or clarity state.

Aspect 39. The infant monitoring hub according to aspect 35, wherein the processor is configured to process the measurement data into a guess about the infant.

(Aspect 40) The infant monitoring hub according to aspect 39, wherein the inference includes acceptability of the infant for learning, infant happiness, caregiver presence, environmental factors, infant safety or infant emotional state.

(Aspect 41) An infant monitoring apparatus associated with a plurality of sensors configured to obtain measurement data;
The measurement in connection with a learning acceptability model obtained from a remote platform configured to receive measurement data from the plurality of sensors and receive information from a plurality of monitoring hubs associated with a plurality of infant monitoring devices. A monitoring hub configured to analyze the data, the system comprising:
A system that analyzes the measurement data to predict when and for how long an infant associated with an infant monitoring device can accept learning.

(Aspect 42) The system according to aspect 41, wherein the measurement data includes an infant's gaze intensity and duration.

(Aspect 43) The system according to aspect 41, wherein the measurement data includes an infant's posture and movement.

(Aspect 44) The system according to aspect 41, wherein the learning content is customized based on a developmental age related to the infant.

(Aspect 45) The system according to aspect 41, wherein the developmental age associated with the infant is determined based on the measurement data.

(Aspect 46) The method according to aspect 41, wherein the measurement data includes movement, body temperature, body posture, and electrodermal reaction.

(Aspect 47) Receiving measurement data acquired from a plurality of sensors associated with an infant monitoring apparatus at a monitoring hub;
Analyzing the measurement data in relation to a learning acceptability model obtained from a remote platform configured to receive information from a plurality of monitoring hubs associated with a plurality of infant monitoring devices;
Predicting when and for how long an infant associated with the infant monitoring device may accept learning.

(Aspect 48) The method according to aspect 47, wherein the measurement data includes an infant's gaze intensity and duration.

(Aspect 49) The method according to aspect 47, wherein the measurement data includes an infant's posture and movement.

(Aspect 50) The method of aspect 47, further comprising presenting customized learning content based on the developmental age associated with the infant.

(Aspect 51) The method according to aspect 47, further comprising determining a developmental age associated with the infant based on the measurement data.

(Aspect 52) The method of aspect 51, wherein predicting the time and duration is based on the developmental age associated with the infant.

(Aspect 53) The method according to aspect 47, wherein the measurement data includes movement, body temperature, body posture, and electric skin reaction.

(Aspect 54) The method of aspect 47, wherein analyzing the measurement data comprises processing the data measurements into observations about the infant and comparing the observations with the learning acceptability model.

(Aspect 55) The method according to aspect 47, wherein the observation includes one of sleep, mobility, stress, posture, comfort, health, vigilance, or intelligibility.

(Aspect 56) The method of aspect 47, wherein analyzing the measurement comprises processing the data measurement into a guess about the infant and comparing the guess with the learning acceptability model.

(Aspect 57) The method according to aspect 56, wherein the inference includes one of acceptability for learning, happiness of an infant, presence of caregivers, environmental factors, infant safety, or infant emotional state.

(Aspect 58) The method according to aspect 57, wherein customizing learning content for the infant includes preparing materials for a caregiver associated with the infant.

(Aspect 59) The method according to aspect 58, wherein customizing the learning content for the infant includes preparing a suggestion for a caregiver associated with the infant.

(Aspect 60) The method of aspect 47, further comprising presenting customized learning content based on previous learning associated with the infant.

  These and other embodiments are described below with reference to the drawings.

FIG. 1 is a schematic diagram illustrating an example of an infant monitoring system. FIG. 2A is a schematic diagram illustrating an example of a data aggregation system for collecting information about an infant from a user community that is monitoring the activity of the infant. FIG. 2B is a schematic diagram illustrating an example of a graph indicating the degree of smile that may contribute to the meaning of smile. FIG. 3 is a schematic diagram illustrating an example of an infant monitoring data aggregation and processing system. FIG. 4 is a schematic diagram illustrating an example of a wearable infant monitoring apparatus. FIG. 5A is a schematic diagram illustrating an example of an infant monitoring apparatus and a wearable infant monitoring apparatus. FIG. 5B is a schematic diagram illustrating an example of the infant monitoring apparatus docked on the charging stand. FIG. 5C is a schematic diagram illustrating another example of the infant monitoring apparatus docked on the charging stand. FIG. 6 is a flowchart showing an example of a process for providing measurement data corresponding to an infant activity. FIG. 7A is a schematic diagram illustrating an example of a monitoring hub. FIG. 7B is a schematic diagram illustrating another example of the monitoring hub. FIG. 8 is a flow diagram illustrating an example of a process for determining the infant's developmental age relative to the infant's biological age. FIG. 9A is a flowchart showing an example of processing for presenting customized learning content to an infant based on the infant's developmental age. FIG. 9B is a flowchart showing an example of processing for presenting customized learning content to an infant based on the past performance of the infant. FIG. 10 is a flowchart showing an example of processing for providing learning content customized based on parental preferences. FIG. 11 is a flowchart illustrating an example of a process for creating a playlist of customized learning materials. FIG. 12 is a flow diagram illustrating an example of providing social media recognition for completion of infant learning content. FIG. 13 is a flowchart illustrating an example of a process for detecting the performance of an infant.

  Reference will now be made in detail to several embodiments of the invention in order to provide a thorough understanding of the ideas presented. These specific examples are illustrated in the accompanying drawings. While the invention has been described in conjunction with these specific embodiments, it should be understood that it is not intended to limit the invention to the described embodiments. Rather, as described in the appended claims, the described embodiments that may be included within the spirit and scope of the present invention are intended to cover alternatives, modifications, and equivalents. Some of the ideas presented may be practiced without using some or all of these specific details. In other instances, well-known processing operations have not been described in detail in order to avoid unnecessarily obscuring the described concepts. Although some ideas have been described in conjunction with specific examples, it should be understood that they are not intended to be limited to these examples.

  Various techniques and mechanisms of the invention may be described in the singular for ease of understanding. However, it should be noted that the technique repeats the technique multiple times and includes examples of the mechanism unless otherwise noted. In addition, these techniques and mechanisms of the present invention are sometimes described as two elements connected. Note that the connection between two elements does not necessarily imply a direct, unhindered connection, and various other elements may exist between these two elements. That is, unless otherwise specified, the connection does not necessarily mean a direct and unhindered connection.

  Conventional infant monitoring systems generally allow a caregiver to monitor an infant's voice and / or video provided by a remote device such as a speaker or portable device. However, these monitoring systems are limited to providing only basic monitoring of infants. In principle, this surveillance system allows the caregiver to see and hear the infant from different locations, such as another room in the house. The caregiver must imagine the infant's demands, mood, health and happiness from the sounds and images transmitted from the surveillance system. Once the caregiver goes to the infant, the surveillance system is no longer useful.

  Some wearable devices provide the child with basic heartbeat and temperature information. However, all these monitoring systems today are very limited in nature. Caregivers can benefit greatly from a more robust surveillance system to improve infant care and development.

  Various embodiments of the present disclosure relate to providing an infant monitoring device that an infant can wear. As an example, the wearable infant monitoring device can collect various measurements related to the infant, such as movement, body temperature, position, awakening, and the like. These measurements can be sent to a monitoring hub that can process the data into useful information that can be provided to one or more caregivers. In some examples, the environmental sensor can collect other measurement data, such as audio level and video data, that can also be sent to the surveillance hub. In some embodiments, the monitoring hub may interact with a remote server that is configured to aggregate information from multiple wearable infant monitoring devices at different locations. Further, as used herein, the term “infant” includes infants.

  According to various examples, surveillance hubs are sleep, mobility, stress, posture, comfort, health, vigilance, clarity, acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety Processes measurement data to provide information about the infant, such as the emotional state of the infant, sensitivity, acceptability for learning, etc. In some examples, this information can be provided to the caregiver either directly through the hub or through a client device such as a mobile device. According to various examples, other suggestions for caring for infants can also be provided to the carer from the surveillance hub.

  In various examples in particular embodiments, measurement data and / or processed measurement data can be transmitted to a remote platform. This remote platform can collect measurement data and / or processed measurement data from multiple infant monitoring devices in a community. According to various embodiments, the remote platform is a remote infant development analysis platform. The remote infant development analysis platform can also use this aggregated data to identify various patterns and phenomena, and use this data to create other suggestions such as care, education, etc. As an example, aggregated data can be used to create a graph of infant growth and development. These graphs are then sent to each monitoring hub so that each caregiver can understand, for example, what each infant is in comparison to the graph. As an example, the developmental age of an infant can be compared to the biological age of the infant based on the graph. In another example, the measurement data can be used to create a model such as when an infant becomes receptive to learning. Information from these models can be provided to the respective monitoring hub and further to the caregiver at the appropriate time. In yet another example, a behavior model or the like can be used, for example, to provide feedback to caregivers on how their infants can be made more comfortable.

  In some embodiments, the measurement data can be used to determine an infant's developmental age, and learning content customized for that infant can be selected based on that developmental age. According to various examples, learning content customized for an infant can be selected based on parental preferences. In addition, selected learning content can be organized into customized educational playlists that can be presented through a monitoring hub or portal associated with a remote platform. In some examples, a surveillance hub or remote platform can also interact with social media. By way of example, a surveillance hub or remote platform can provide this performance social media recognition upon completing a specific module of learning content, for example as a write to a social media platform.

  FIG. 1 is a schematic diagram showing an example of an infant monitoring system. According to various embodiments, the infant monitoring system is designed to be safe, secure and simple to use. As shown, the system includes a local monitoring system 101 and a remote system 105. The local monitoring system includes a wearable infant monitoring device 111 and a monitoring hub 113. The remote system 105 includes a platform 115 that is designed to collect data from the user community. In various embodiments, information about the infant 107 is collected by the wearable infant monitoring device 111 and this information is processed by the monitoring hub 113 to create each model on the platform 115.

  According to various embodiments, the wearable infant monitoring device 111 collects data and issues a notification. The wearable infant monitoring device 111 is an infant-friendly wearable device that monitors infant activity and other infant-related biometrics. In one embodiment, the wearable infant monitoring device 111 collects data about activity, emotional state data, and acceptability for learning, fitted to the infant's ankle. As an example, the wearable infant monitoring device 111 can collect data regarding infant movement, orientation, and physiology. In some examples, the age of the target infant is between about 0-24 months. In some examples, the wearable infant monitoring device 111 may notify the user. As an example, the LED of the wearable infant monitoring device 111 can notify the caregiver 109 that the charge amount of the battery is low or the device is being charged.

  In this example, measurement data associated with an infant is input 117 to the wearable infant monitoring apparatus 111. Next, this measurement data is transmitted 119 to the monitoring hub 113. The monitoring hub 113 can execute various functions according to a desired application such as data reprocessing, surrounding detection, content storage in a cache, and infant status evaluation. In some examples, the monitoring hub includes learning content and a schedule. As an example, the learning content can include caregiver information about the content to be educated to the infant, and the schedule can indicate an appropriate presentation time based on the content, for example, age or development level. In some embodiments, this learning content is available from platform 115. More specifically, the platform 115 may store various libraries of data, models, schedules, etc. that are accessible to the monitoring hub 113. Platforms include, for example, environmental suitability models (predicted environmental state distributions and expected infant characteristics corresponding to these environmental conditions), infant orientation models (predicted infant positions based on data such as movement and geographical location), Health issues such as learning acceptability models (predicting when and how long an infant can accept learning), health models (eg, epileptic seizures, increased risk of SIDS (sudden infant death syndrome) associated with lying in prone position Predictions) and developmental models (measurements, observations, guesses or other criteria associated with infants of a particular developmental age) can be stored. These models may include various decision thresholds that can cause caregiver notification. For example, an environmental suitability model can include thresholds for noise pollution, visual pollution, and / or over-brightness illumination, and exceeding any of these thresholds can cause a determination that an environmental condition is not suitable for an infant There is also. The monitoring hub 113 can select and customize content from the library in response to the needs and development of the particular infant 107 being monitored. According to various embodiments, the monitoring hub 113 can also provide digital signal processing, human interface and data security. In some examples, the development model can be evaluated at the monitoring hub 113. Depending on the application, the monitoring hub 113 can provide content adaptation based on the model. Further, the monitoring hub 113 may provide notifications or suggestions to caregivers based on decisions made at the monitoring hub 113 or platform 115. As an example, if it is determined that the environmental condition is not suitable for an infant, the surveillance hub can make suggestions including methods for reducing noise, lighting brightness, visual pollution, and the like. In particular, suggestions may include closing windows, turning off lights, and reducing the number of toys or items in the room.

  Although not explicitly shown in FIG. 1, the local monitoring system 101 may also include a mobile device. In some embodiments, the mobile device can communicate with the monitoring hub 113 and / or the wearable infant monitoring device 111. Furthermore, the mobile device can provide an interface to the local monitoring system 101 for the caregiver 109. As an example, the caregiver 109 can view data including information such as biometric data, video, audio, and the like regarding the infant through the mobile device. In some examples, the mobile device can serve as the monitoring hub 113 itself. According to various embodiments, the mobile device can provide data preprocessing, early warning, and remote observation. Mobile devices can also include social and environmental content. In some cases, carer 109 can input information regarding social and environmental conditions, and / or the mobile device can detect various situations using a microphone, camera, or other input. In some examples, a mobile device includes caregiver content for a proposed social dialogue or environment expansion or adjustment of music, lighting, and the like.

  According to various embodiments, a caregiver 109, such as a mother, father, nanny, babysitter, or other primary caretaker, is the primary user of data from the wearable infant monitoring device 111. The caregiver 109 can also provide information to the system, such as developmental assessments, nominal infant habits, etc., via the mobile device and / or the monitoring hub 113, for example. Information can be provided to the caregiver 109 via a mobile device associated with the monitoring hub 113 and / or the local monitoring system 101. By way of example, compliant content, infant monitoring, and social engagement are provided through monitoring hub 113 and / or mobile devices.

  In this example, data from the monitoring hub 113 is transmitted 123 to the platform 115. As an example, raw data including biometric data and the like is transmitted to the platform 115. Information from the platform 115 can also be sent 123 to the monitoring hub 113. Transmissions 123 to and from the platform can also include encryption and / or compression. Encryption can be used to protect sensitive personal information, and compression can assist in the smooth and efficient transmission of data.

  According to various embodiments, platform 115 includes software that promotes mechanisms such as parental portals, social interfaces, preschool learning platforms, and content distribution platforms. Although not explicitly shown in FIG. 1, the caregiver 109 may interact directly with the platform 115, for example through these portals or platforms. The platform 115 includes contents such as an infant profile, infant unspecification data, learning material, evaluation material, and infant tendency. According to various embodiments, the information transmitted to the platform 115 includes data such as the development criteria of each infant. In addition, the platform 115 performs machine learning on aggregate measurement data, sensor data, and any other development criteria to create a model that predicts the next behavior, development, activity, etc., according to various examples. As an example, measurement data can be used to create models based on activity patterns, and these models can be used to predict the next activity in a particular infant monitoring system. Specifically, each pattern of activity may include aspects such as physical activity, emotional signal, sleep pattern, attitude. The following activities include illness, sleep, mobility, stress, posture, comfort, health, alertness, clarity, acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety and / or It can include aspects such as the emotional state of the infant.

  In one example illustrating the use of the system shown in FIG. 1, the wearable infant monitoring device 111 continuously monitors the infant's body temperature, and the caregiver 109 inputs information about changing diapers. The system detects noise in the room with, for example, a microphone that provides data to the monitoring hub 113. The wearable infant monitoring device 111 then detects measurement data corresponding to the surprise response from the infant. The monitoring hub 113 determines that there are too many surprising responses experienced by the infant 107. In response, the monitoring hub 113 may request a more calm environment (eg, using a projector, music, white noise, etc.) or provide a calmer environment for the caregiver.

  In some instances, the caregiver may also have a wearable device (not shown). The caregiver wearable device can be used to infer when the caregiver 109 is interacting with the infant 107. This information can be used by the monitoring hub 113 and / or platform 115 to assess the effectiveness of a particular interaction or the like. In addition, monitoring the position of the infant 107 and the caregiver 109 can be used to alert the infant about wandering or stealing in some applications.

  According to various embodiments, the system can be used with one infant or more than one infant. As an example, it can be used to direct the system to two infants, such as twins, or when the caregiver 109 takes care of multiple infants. This allows the caregiver 109 to interact with a single surveillance hub 113 and / or mobile device, thereby making it easier and more efficient to care for multiple babies. In such instances, additional (each) wearable infant monitoring devices can also communicate with the monitoring hub 113.

  FIG. 2A shows a schematic diagram of an example of a data aggregation system for collecting information about infants from a user community monitoring infant activity. As shown, a number of monitoring systems such as monitoring systems 203, 205, 207, 209 and 211 form part of the infant monitoring community. Any number of monitoring systems may be included, as indicated by the dotted line following the figure. In some examples, the infant monitoring community 201 includes millions of infants, each associated with an independent monitoring system. In these examples, development criteria from this millions of infants can be collected on a platform 225, such as a remote infant development analysis platform. As described herein, aggregated measurement data and sensor data includes development criteria such as measurement data from monitoring devices collected from the infant monitoring community 201 and sensor data from peripheral devices. Similarly, the aggregated observation contents and guesses refer to the data aggregated from the infant monitoring community 201.

  In this example, the monitoring systems 203, 205, 207, 209 and 211 are the same as the local monitoring system 101 in FIG. Thus, each monitoring system 203, 205, 207, 209 and 211 is associated with a different infant. Each of the monitoring systems 203, 205, 207, 209 and 211 can communicate with the platform 225. According to various embodiments, the information transmitted from the monitoring systems 203, 205, 207, 209 and 211 to the platform 225 includes development criteria and / or other data collected by each of each monitoring system. These development criteria (and / or other data) can be used on the platform 225 for machine learning back-end input.

  According to various embodiments, content such as content libraries and parameterized infant development models can be stored on the platform 225. This content can be shared with the monitoring systems 203, 205, 207, 209 and 211. For example, information can be transmitted to the monitoring system 203 in response to a request from the monitoring system 203. In other examples, information can be sent to the monitoring system 205 at a specific developmental time associated with the infant being monitored by the monitoring system 205. In yet another example, information can be transmitted upon receipt of a development criterion from a particular monitoring system 207. As described above in connection with FIG. 1, platform 225 includes mechanisms such as a parent portal, social interface, infant learning platform, and content distribution platform. Each monitoring system 203, 205, 207, 209, and 211 can access these mechanisms on platform 225. In some embodiments, the parent portal allows the caregiver to communicate directly with the platform 225, for example through a mobile device or computer, without communicating through a local monitoring hub. In addition, the platform 225 can be accessed by the monitoring systems 203, 205, 207, 209, and 211 in various embodiments, such as infant profiles, infant de-specification data, learning materials, evaluation materials, and infant propensity. Contains content.

  According to various embodiments, machine learning can be used on platform 225 to create models such as development models, health models, exercise models, and dynamic models. These models can be created using information gathered from the monitoring systems 203, 205, 207, 209 and 211 of the infant monitoring community 201. Specifically, the collected data can be used for research on the platform. The collected data can be used, for example, to find new criteria, create demographics, and capture trends. As an example, a model can be created with various predictions by applying unstructured machine learning to a large amount of collected measurement data such as weight, age, sex, and position related to a large number of infants. For example, a model relating to how to give knowledge or interact with society can be created. Other examples include finding trends or landmarks, such as features that indicate that the infant may soon become ill based on the infant's sleep / wake pattern.

  Various aspects can be observed and studied on the platform 225 with the aid of machine learning. Some examples include awakening / sleep prediction, walking detection, detection of a paused window, determining when the infant is absent, determining attention status and predicting the child's learning acceptability.

  In one example, wake / sleep prediction can be studied on platform 225. Specifically, activity monitoring can be used to identify arousal / sleep transitions. The next transition can be predicted based on the awakening / sleep transition of the previous week. This type of prediction is based on the end of the pulse train. Time-series awakening / sleeping results in a pulse train that should have a regular pulse width and interval (for a healthy sleep pattern). By estimating those parameters, one can predict the beginning of the next awakening / sleep transition and the duration of the subsequent state (whether it is awake or sleeping). As the infant grows, the characteristic pulse interval and width change (in healthy infants, the sleep at night gradually continues longer and converges to a shorter nap during the day). These changes usually occur on a monthly time scale, so sleep predictions observe a time frame on the order of the previous week. By observing the pattern on this time scale, a change in the sleep pattern can be predicted on a time scale that is earlier than the time scale on which the sleep pattern develops.

  Collecting awake / sleep patterns from countless babies and analyzing this data will help to model health patterns at different developmental levels and ages. In general, infants require different amounts of sleep in different cycles, depending on the age of the infant. As an example, a newborn may require about 16-20 hours of sleep per day, a 3-week-old child may require about 16-18 hours of sleep per day, and a 6-week-old child may need one sleep. May require 15 to 16 hours of sleep per day, and a 4-month-old child may require about 2 to 3 hours of nap twice in addition to about 9 to 12 hours of sleep per day In addition to about 11 hours of sleep per day for 6-month-old children, each may require two naps of about 1.5-2.5 hours each, and about 11-12 hours per day for 9-month-old children In addition to sleep, each child may need two naps of about 1 to 2 hours each. In addition to about 10 to 11 hours of sleep per day, a 1 year old child takes two naps of about 1 to 2 hours each. May need, per day for 18 months old In addition to 13 hours of sleep, each requires about 1 to 2 hours of nap twice, and for 2-year-old children, about 11 to 12 hours of sleep each night requires about 2 hours of nap There are things to do.

  A sleep schedule can be predicted using various factors such as electrical skin reaction (GSR) activity (ie, awakening), the latest known sleep cycle, and voice detection with a sensor. In some examples, each model is created from data for an infant and / or data aggregated from multiple infants to predict an expected sleep schedule. According to various embodiments, each sensor includes a mechanism for determining whether the infant is prone, lying on its back or otherwise. Each sensor can include an accelerometer, a magnetic sensor, a gyroscope, a motion sensor, a pedometer, a rotation vector sensor, a gravity sensor, an orientation sensor, and a linear acceleration sensor. In various embodiments, with regard to the situation of infants, it has been recognized that it is particularly useful to determine the position of the infant, such as whether the infant is lying on its back, lying down or sitting down.

  Infants can wear the sensor wearable case in a special way so that the directionality is known. For example, the wearable case may be an anklet, a bracelet, socks, shoes, a diaper, or may be included in an infant upper and lower integrated garment. An indicator that indicates to the caregiver about the proper placement or orientation of the wearable case may be included in the wearable case. In addition, the infant's sleep pattern and sleep state may be observed, and in some examples, the infant's fatigue level can be estimated. As an example, if an infant's sleep schedule indicates that the infant should normally be sleeping at that time, but is not actually sleeping, it can be imagined that the infant is probably tired. Specifically, an infant can usually take a nap at that time, but if it is awake, it can be imagined that the infant may be angry. Depending on the application, it can be suggested to caregivers about encouraging infants to sleep or avoiding stimuli and learning to create a quiet environment. According to various embodiments, each model created on platform 225 can also be used to predict the development of a particular infant when that particular infant is compared to these models.

  In another example, platform 225 can consider gait detection. Specifically, it is possible to determine that the infant is walking or moving in various ways using the activity data from the infant monitoring community 201. As an example, before walking, smooth acceleration can be included, but walking can include acceleration that increases rapidly when the foot is lowered at an appropriate period. Also, for models that include torso rebound and ground reaction forces, joint angles and bone positions can also indicate whether the infant is walking or otherwise moving. By analyzing the movements of infants, it is possible to predict walking detection. In some examples, measurement data associated with an infant can be combined with information provided by a caregiver, such as when the infant walked. By comparing the walking of a specific infant with each model, it can be used to predict the developmental age of the infant. A mechanism for creating a model related to walking or the like can also be applied to data sets other than the infant category. As an example, the system can be used for patients under treatment at any age.

  In another example, each mechanism can be used on the platform 225 to establish “each stationary window” when the infant is inactive, quiet and stationary. Hygiene can be improved by creating models for predicting these “stationary windows” and using them in each monitoring system, such as the health of infants and the increase in the number of diapers used.

  In yet another example, the absence of an infant can be detected based on each model created on the platform 225. You can predict when the infant is not moving on its own. As an example, it is possible to determine when an abnormality is detected by examining the operation pattern and position. In some examples, a geographic location may be included to indicate that the infant is moving with someone other than a legal carer. In some applications, the caregiver can be notified to check the infant and determine where the infant is. This is particularly useful not only for kidnapping infants, but also for inadvertently leaving infants in cars or other places. In addition, this technique can be used by older children to determine if they have wandered.

  In another example, the platform 225 can consider the attention of an infant. Specifically, measurement data can be examined to detect when an infant is paying attention alone and when the infant is paying attention alone. Although it can be detected based on factors such as background speech analysis, the fact that an infant is alone is actually complicated in situations where the infant is not alone but simply ignored. It may also include input from caregivers. Each model can be used to predict when infants will benefit from a dialogue or learning experience.

  In another example, platform 225 can consider acceptability for learning. Identifying the appropriate window of time for acceptability of early childhood learning can help to know when carers present or interact with teaching materials in a more successful way. A number of factors can be considered to identify these appropriate windows. Specifically, data such as sleep / wake cycle, articulation, body temperature, age, sex, weight, and other biometrics collected from the infant monitoring community 201 can be considered. In addition, data from one or more of orientation detectors, gaze detectors, shared attention detectors, and cognitive detectors can be used to determine acceptability of the infant for learning. In addition, data on the infant environment such as voice level, time of day, location, ethnicity, etc. can be considered. Additional data can also be considered, such as changing diapers from one or more caregivers, self-reports and material feedback. This data can be analyzed to help determine what materials are appropriate to present at specific times and when children are most receptive to learning. A model can be created that shows an acceptability window for learning and appropriate teaching materials / learning materials. These models can be used in individual monitoring systems to apply to each infant. As an example, the presence or absence of specific stimuli such as hearing, perception, touch, etc., as shown by system or caregiver input, is weighted by age from a model created on platform 225, and learning program weighted by progress Can be used for Specifically, knowing the infant's age can help determine whether to present physical, cognitive or language learning material. For example, an infant between about 0 and 3 months may be receptive to learn gross motor skills, and an infant between about 3 to 9 months may be receptive to learn gross motor skills and language, Infants between about 9-18 months may have the ability to learn fine motor skills, language and social skills, and infants between about 18-24 months will have fine motor skills, language, social skills And may be receptive to learning discriminatory ability. There may be an order of learning at a particular age when the infant is receptive to many skills, but these skills can be presented in an order based on the infant's level of development. In various embodiments, certain infant monitoring systems can predict an acceptability window when an infant is receptive to learning. In these embodiments, the infant monitoring system processes the measurement data to select and customize learning materials suitable for the infant. The learning material can be customized based on factors such as the infant's developmental age, preparation, learning experience, caregiver feedback, etc.

  Various mechanisms can be used to assess infant acceptability, such as orientation detectors, gaze detectors, shared attention detectors and cognitive detectors. In one example, the emotional intensity hypothesis can be used to determine an infant's acceptability for learning. Specifically, an infant's smile width can be measured based on data from a camera or other input device in the surveillance system, and the infant's acceptability correlation can be correlated. FIG. 2B shows a graph showing various facial expressions for various smile widths. These facial expressions can indicate the amount of joy the infant is experiencing at a particular time. The information in this table can be used in conjunction with data from the infant monitoring system, such as camera feed, audio level, etc., to determine a good condition for the infant to learn. The familiarity and retreat of the graph shown in FIG. 2B with the gaze and movement pattern during the game as an index gives a smile (Vogel et al., 2000). For example, when playing, if not, infants tend to stare at their parents with any smiles, suggesting positive visual attention. Contrary to this, at the top of tickling play, toddlers who open their mouths and squint at the same time narrow their eyes show a mixed pattern of staring at their parents and looking away from their parents . Such patterns may correspond to the feelings of excitement and joy of active participation and escape pleasure. These findings suggest that the same smiling behavior can reflect different positive emotions depending on the behavior of the infant and the social development process occurring simultaneously.

  According to various embodiments, the link between smile and stare changes and more specifically patterns as age progresses. Simulation studies suggest that in a 3-month-old infant, taking a look while smiling is actually less than expected and happens by chance. Simulation studies show that 3-month-old infants tend to start and end smiling while staring at their parents' faces. In other words, early expression of positive emotions depends on whether you are conscious of keeping an eye on your parents. At 6 months, after sharing a positive feeling with parents, pay attention to others. Toddlers stare at the mother's face, smile, look away, and finish the smile. Such disgusting gaze tends to occur during a stronger smile and a longer-lasting smile in a five-month-old infant who plays at least if he / she plays. Therefore, the information collected about the infant's smile and gaze is useful for judging the infant's age and the like. This in turn helps to determine what learning material or activity the infant should present during the receptive window.

  According to various embodiments, analysis at platform 225 is an ongoing process. Various observations, patterns and models can be continuously discovered and improved. As a result, these models are changed over time based on input from the infant monitoring community 201. In some examples, an expert model can be used first and frequently replaced with an improved model.

  FIG. 3 shows a schematic diagram of an example of an infant monitoring data aggregation and processing system. The system includes an infant monitoring device, environmental sensor (s) and a monitoring hub. Measurement data collected by the wearable infant monitoring device and each environmental sensor is transmitted to the monitoring hub for processing. As shown in the figure, the wearable infant monitoring device data 301 collected by the infant monitoring device includes a movement 303 (that is, an activity), a body temperature 305, a body position 307, and an awakening 309. In some examples, the position 307 can include the infant's geographic location. The environmental sensor data (311) collected from each device such as a microphone or a camera includes an audio level 313 and a moving image 315. However, depending on the example, each environmental sensor may be omitted when a simple system is employed. As an example, when using the system when going out, the camera, peripheral devices, etc. may be removed and only the input from the wearable infant monitoring device may be used.

  In this example, the monitoring hub receives data from the wearable infant monitoring device and the environmental sensor (s). According to various embodiments, data is collected continuously for 24 hours. Some examples may mean periodic but consistent monitoring, such as specified time intervals. Various observations 351 and guesses 353 can also be obtained by subjecting the received data to hub processing 321. Depending on the observation 351 at the monitoring hub based on data measurements, sleep 323, mobility 325, stress 327, body position 329, comfort 331, health 333, alert (eg, infant attention, cognitive response) 335 and clarity ( In other words, the speech intelligibility) 337 is included. Depending on speculation 353 at the monitoring hub based on measurement data, it may include acceptability 339 for learning, infant happiness 341, caregiver presence 343, environmental factors 345, infant safety 347 and infant emotional state 349. Although the observation 351 and the guess 353 are shown as different classifications, the various items may be classified into any set without departing from the scope of this example.

  Many combinations of measurement data from the wearable infant monitoring device and / or environmental sensor (s) can be used for observation or inference. According to various embodiments, data about an infant is first collected, the data is scaled, and then a model or prediction is applied to the infant. Specifically, as described with reference to FIG. 2, aggregated data can be collected on the platform to create models, predictions, and the like. These models, etc. can then be accessed from the platform at individual monitoring hubs. A particular infant monitoring system can then implement a hub process 321 that can use these models and the like to analyze measurement data for a particular infant.

  Observations and / or inferences about specific infants can be made available to caregivers. This information will help caregivers to better care for their infants. In some examples, this information can be used for caregiver guidance or advice through a surveillance hub and / or mobile device. As an example, the hub processor 321 determines that the infant is currently in a particular position 329 (also called orientation) that may be correlated with respiratory problems (related to SIDS, etc.) or unfavorable / unsafe positions. May be. This observation 351 can guide this discovery with notification to the caregiver. In some examples, this notification may include a recommendation on how to change the infant's position. In another example, an infant's growth can be monitored, for example, with a caregiver input 355 or a sensor such as a ruler (not shown) connected to the system as a peripheral device. This development can be used to estimate the infant's developmental age and can be scheduled from this information outlining the hub when it is time to teach the infant. In yet another example, for example, movement 303 such as an infant hand gesture can be monitored to measure motor development, blood flow can be monitored to correlate with brain development, and epileptic seizures, etc. Skin potential can be monitored to predict the occurrence of a health 333 problem. In another example, data from accelerometers and GSR, described in more detail with respect to FIG. 4, can be used to predict infant activity. Based on this data, it can be predicted whether the infant is awake / sleep, eating, walking / walking / running, etc. Various inputs can be monitored to obtain observations and expectations about the infant.

  Various observations 351 about the infant can be made based on measurement data associated with the infant. As an example, observation of sleep 323 can be used to predict the infant's next sleep pattern, and can alert the caregiver when the sleep pattern is disturbed. For example, a disturbed sleep pattern may indicate that an infant is getting sick. Observations on mobility 325 can help determine infant movement relative to the infant's developmental age and advise the caregiver on how to educate or help the infant at the appropriate level of development. Can be used for. Observation of the stress 327 can be useful in determining whether there are conditions that can be changed to reduce the stress of the infant. As described above, position 329 can be observed to see if the current position is associated with an unfavorable or unsafe position and can notify the caregiver. The posture 329 can also inquire about the orientation of the infant, for example, whether the infant is lying, standing, leaning or walking. Further, the infant's orientation can include whether the infant is lying down or lying on its back. These observations can be made based on data such as the movement 303 and the body position 307. Observations about comfort 331 can be made to provide knowledge. You can also observe health 333, such as whether the infant's body temperature is considered fever. Observations about vigilance 335 include whether the infant is alert and awake. In addition, the observation about intelligibility 337 can also include speech intelligibility detection using environmental sensor data 311 such as voice input. While specific observation examples have been shown and described, it should be understood that further observations are possible within the scope of this disclosure. Similarly, any combination of observations (such as the limited set shown so far) can be used depending on the desired system operation.

  Based on the measurement data associated with the infant, various inferences 353 about the infant can be made. As an example, the acceptability 339 for infant learning can be inferred. As described above with respect to FIG. 2, various factors such as age of development can be used to assess acceptability 339 for learning. These inferences can be used to determine when and / or content that an infant should learn. Providing the right learning materials at the right time (such as teaching the caregiver about what to teach or how to interact with the infant) helps the infant's brain development. Inferences about infant happiness 341 can be made in some examples. As an example, an infant's overall happiness can be shown by considering factors such as the infant's health and emotional state. In some examples, these inferences can help determine how effectively a particular caregiver is responding to an infant's needs and the like. A guess about the existence of a caregiver 343 can also be made. As an example, measurement data from an infant monitoring device and / or a caregiver device can indicate whether a carer is present at a particular time. The environmental factor 345 can also be estimated. As an example, environmental sensor data 311 such as audio level 313 can be used to evaluate what is good for an infant versus what is good for an infant. In some examples, a predictive model can be used to determine whether an environment may be based on empirical perception for infants using factors such as visual pollution, noise pollution, over-brightness lighting, lack of dialogue, and the like. Specifically, an environmental suitability model that reflects the relationship between the range of environmental conditions and the expected infant characteristics corresponding to the range of environmental conditions can be used. For example, visual pollution can be associated with stronger stress, noise pollution can be associated with less (or less quality) sleep, etc. In addition, the child's safety 347 can be inferred. In some examples, safety may include an infant's posture (eg, “sleep again”) and other physical safety factors. In other examples, safety may include whether the infant is “absent”, such as whether the infant was wandering, falling or taken by an unsupported caregiver. It can also be guessed about the emotional state 349 of the infant, such as whether the infant is under stress. In some examples, these inferences can help determine how effective a particular caregiver or dialogue is to reduce infant stress. In another example, these estimates can be used to determine the type of activity, environment, schedule, etc. most appropriate for this particular infant. While specific speculative examples have been shown and described, it should be understood that further speculations are possible within the scope of this disclosure. Similarly, any speculative combination (such as the limited set shown so far) can be used depending on the desired system operation.

  FIG. 4 shows a schematic diagram of an example of a wearable infant monitoring apparatus. The wearable infant monitoring device 401 is a wearable device that is easy to use for infants, and monitors infant activities and other biometrics related to infants. As shown in this example, the wearable infant monitoring apparatus 401 includes a wearable case 403 and an infant monitoring apparatus 405. According to various embodiments, the infant monitoring device 405 can be removed from the wearable case 403, examples of which are described with reference to FIGS. 5A-5C.

  In one embodiment, the wearable infant monitoring device 401 can place the infant monitoring device 405 on the infant's ankle. The infant monitoring device collects activity and emotional state data. In this example, this data is collected continuously for 24 hours. Specifically, the infant monitoring device 405 collects data and provides a notification. In various examples, the infant monitoring device 405 can be used for data logging. According to various embodiments, the device is required to store data from multiple sensors and also to process data from each sensor appropriately. This process can include filtering, dimensionality reduction and raw data cleanup. Since this device is also intended for use as an infant monitor, a low-delay process of some sensors, such as body position sensors, may be required. However, in some examples, the infant monitoring device 405 may not store content. By including less content and / or other mechanisms, a smaller infant monitoring device 405 can be designed and the infant can have a more comfortable experience. In addition, by including fewer mechanisms, the complexity of the apparatus can be reduced, thereby reducing possible malfunctions and the like.

  In this example, the infant monitoring apparatus 405 includes a triaxial accelerometer 407, a body temperature sensor 409, a gyroscope 411, an electric skin reaction (GSR) sensor 413, a processor 415, a memory 417, a light emitting diode (LED) 421, a transmission interface 423, Various components such as a charging interface 425 and a battery 427 are included. The triaxial accelerometer 407 measures infant activity, such as movements that record values above about 50 Hz in some examples. Accelerometer data is used to measure infant movement. The body temperature sensor 409 measures the body temperature of the infant. According to various examples, the infant's body temperature is continuously monitored. The gyroscope 411 measures the direction of the infant. The GSR sensor 413 measures an electric skin reaction (GSR). As an example, the GSR sensor 413 can measure the amount of sweat or moisture detected by the body. GSR is a low-latency wakefulness measurement that can be used to measure an infant's stress level.

  In this example, the processor 415 may be ARMortex M0-M3 or similar depending on the application. Depending on the example, the processor 415 may perform a limited amount of signal processing (DSP) or no signal processing (DSP). The memory 417 may be any size depending on the application. In some examples, the memory 417 can have a size of 384 kb. The transmission interface 423 can be used for communication with the monitoring hub 429. Specifically, measurement data can be transmitted from the infant monitoring apparatus to the monitoring hub 429. According to various examples, the transmission interface 423 can use a transmission protocol such as Bluetooth LE (BLE 4.0), but other suitable protocols can be used.

  In this embodiment, the infant monitoring device 405 includes an LED 421 that can communicate status information to a caregiver. As an example, LED 421 can indicate that the device is charging when the LED is lit. In some examples, the LED may be a single neopixel LED.

  According to various embodiments, the battery 427 stores charge for operation of the infant monitoring device. One type of battery that can be used is a LiPo battery (110 mAh (milliampere hour)), which is sufficient for one-day operation. However, any battery may be used depending on the application and intended use. In some examples, the battery can be recharged via a charging interface 425 that can be periodically placed in contact with the charging base 431. As an example, the device can be charged using contact and / or wireless inductive charging. In this example, if the battery life can be expected to have at least 24 hours, the device is charged once a day. The battery 427 and / or the charging interface 425 includes a charging circuit in some examples.

  According to various embodiments, the wearable infant monitoring device must be safe, secure and easy to use. In this example, the infant monitoring device 405 is waterproofed and is hypoallergenic. In addition, the wearable infant monitoring device does not contain any repairable parts and the electronic parts in this example are completely sealed.

  In some examples, infants between about 0-24 months are targeted. Of course, this age range can be expanded or reduced depending on the particular application or corresponding needs. In addition, although the wearable infant monitoring device may be used indoors depending on the application, the infant monitoring device can be used outdoors according to various embodiments. As an example, the infant monitoring device can be used when outing or traveling. If the infant monitoring device includes one or more peripheral devices such as a camera or microphone placed in a fixed position such as an infant's room, certain mechanisms may not be available when the device is used outdoors. is there. However, in some examples, continuous monitoring of the infant can be continued for measurements of body temperature, activity, GSR, position, etc.

  5A-5C show examples of infant monitoring devices used in different situations. FIG. 5A shows a schematic diagram of an example of an infant monitoring apparatus and a wearable infant monitoring apparatus. Specifically, the infant monitoring apparatus 501 is shown together with a base 507, a main body 505, and an LED window 503. When the infant monitoring device 501 is engaged 509 with the wearable case 515, the wearable infant monitoring device 511 is ready to be worn by the infant. As an example, the wearable infant monitoring device can be fitted around the infant's ankle and secured at its ends with snaps or other fasteners. In some examples, the infant monitoring device 501 can be engaged to fit in the wearable case 515, with the body 505 overlapping one side of the wearable case 515 and the base overlapping the opposite surface. In such an example, the main body 505 and the base 507 may be connected by a bar having a smaller cross section than the main body 505 or the base 507. Further, in these examples, the wearable case may be made of an elastic material that can be stretched to some extent and attached to the infant monitoring apparatus 501 to be fixed. In other examples, the base 507 can also slide into a wearable case pocket or sleeve.

  Although a particular infant monitoring device 501 and wearable case 515 are illustrated, any design and configuration is possible within the scope of this disclosure. Specifically, the infant monitoring device may be made in any shape. By way of example, the body 505 may be square instead of circular, the base 507 may be circular instead of square, and so on. Further, the wearable case 515 may be any shape and design. As an example, the wearable case may be alternatively designed as a continuous loop that is radially adjustable or impossible. In another example, different fasteners, such as buckles (watch type), mating sides that snap together, can be used to secure the wearable case 515 at both ends.

  FIG. 5B shows a schematic diagram of an example of the infant monitoring apparatus docked on the charging stand. As shown, the charging stand 519 is part of an infant station. According to various embodiments, the infant station includes various mechanisms such as a charging station (in this example, the infant monitoring device 501 is shown docked), peripheral devices, and the like. Each peripheral device includes components such as a projector 517, a camera, a microphone, a speaker, a screen, and an input device. By way of example, the infant station includes software that enables data pre-processing, ambient detection, content storage in cache, and infant status assessment. In addition, the infant station includes content such as learning content and schedule (s) in some examples. In addition, the infant station can operate as a surveillance hub in some instances.

  In this example, the charging station may be induction based. The projector 517 may be used as lighting in an infant's room or the like or for displaying an image. Although not shown, the infant station may include a power cord that can be plugged into an outlet or the like, thereby providing power to the various components of the infant station. In some examples, the peripheral device (s) are removable from the infant station.

  FIG. 5C shows a schematic diagram of another example of the infant monitoring apparatus docked on the charging stand. Specifically, the charging stand 521 includes a plug 523 that can be used for charging via a USB port, a micro USB port, or the like. As shown in the figure, the infant monitoring apparatus 501 is docked on the charging stand 521. In this embodiment, the charging stand is an induction base. However, other connection methods may be implemented within the scope of this disclosure. This kind of charging stand is convenient when you are using the infant monitoring device 501 remotely, such as when traveling or going out, especially when the caregiver is using a mobile device to view the monitoring information Sometimes it is. Since the charging stand is small, portable, and easy to store and use, the charging stand can be used together with the mobile device to charge the infant monitoring apparatus 501 on the go.

  FIG. 6 shows a flowchart of an example of a process for providing measurement data corresponding to an infant activity. In this example, the activity of the infant is detected at 601. As described above with respect to the various embodiments, this activity is detected by an infant monitoring device. Detection may be performed based on changes in measured values, such as movement or changes in body temperature in some examples. On the other hand, the detection may correspond to a detection act periodically performed based on a schedule, a set time, and the like. Next, at 603, the infant monitoring apparatus collects measurement data corresponding to the activity. As also described above with respect to the various embodiments, this measurement data includes information such as movement (ie activity), body temperature, body position, and awakening. Next, at 605, the measurement data is transmitted to the monitoring hub. As described above, the monitoring hub can then process the data and provide the caregiver with information about the infant's activities. According to various embodiments, the monitoring hub can also provide this data to the platform for further analysis.

  In this example, the infant monitoring device can include an inspector at 607 to confirm that the battery is fully charged. When the amount of charge of the battery is small, it is possible to notify the carer 609 to charge the infant monitoring device by turning on the optical signal. For example, you may light the LED arrange | positioned at the infant monitoring apparatus. Alternatively or additionally, the caregiver may be notified via the monitoring hub and / or mobile device to charge the infant monitoring device. If no charge is detected, no notification is sent. As shown in the present embodiment, this battery charge confirmation is performed after providing measurement data. By including battery checking as part of this process, the battery is checked frequently. However, it should be understood that in some examples, the battery check and notification 609 at 607 may be omitted from this process, and the battery check may be performed at other times, such as at regular intervals or set times.

  7A-7B show examples of monitoring hubs. Various configurations can be used as the monitoring hub within the scope of the present disclosure. FIG. 7A shows an example of a monitoring hub. As described above with respect to various examples, the monitoring hub 701 can receive measurement data from the infant monitoring device 727 and process the measurement data with the monitoring hub 701.

  According to various embodiments, the monitoring hub 701 can perform data pre-processing, ambient detection (local environmental detection, vibration detection, audio sensor, camera), cache content storage and / or infant status assessment. The monitoring hub 701 can also include learning content and schedule (s). In addition, the surveillance hub can also provide digital signal processing, human interface and data security. In addition, model-based content adaptation can also be provided by the monitoring hub 701. Therefore, each model and library content acquired from the platform 731 such as a remote infant development analysis platform can be adjusted according to the infant's developmental age and requirements. Specifically, each development model can be evaluated by the monitoring hub 701, and contents from the library can be selected and customized. An example of content adaptation to apply to activities involving dialogue involves selecting an activity involving a series of dialogues that do not tire the infant suitable for infant development. Specifically, it is possible to determine the development age of a certain infant and the duration of the dialogue window suitable for that age. Using this information, content from the content library stored on the platform 731 is selected and adapted to be appropriate for the infant. This adapted content can then be provided to the infant during use of the appropriate dialog window.

  In this example, the monitoring hub 701 includes a processor 703, a memory 705, a persistent storage device 707, a display or display interface 709, a projector 711, each sensor 721 (including a camera 723 and an audio sensor 725), an infant monitoring device interface 713, a charging stand. 715, a client device interface 717, and a platform interface 719. Although particular parts are shown, it should be understood that some of these parts may be omitted without departing from the scope of the present disclosure. In one example, the projector 711 can be omitted. Additional components may also be included depending on the desired operation of the monitoring hub 701.

  According to various embodiments, the monitoring hub 701 may serve as an infant station, as described with respect to FIG. 5B. In these embodiments, the infant station includes software capable of data preprocessing, ambient detection, cache content storage, and infant condition assessment. Inclusive content includes learning content and schedule (s).

  In this embodiment, the processor 703 and the memory 705 can be used to process data measurements received from the infant monitoring device 727. Specifically, this data can be processed for observation and / or inference, as described above with respect to FIG. In addition, the processor 703 and the memory 705 can be used to customize content for infants so as to be age appropriate learning material. The persistent storage 707 can store any model, graph, etc. received from the platform 731 along with the content and schedule (s). Furthermore, the persistent storage device 707 can store information specific to the infant.

  In this example, the display or display interface 709 allows a caregiver to view and / or interact with the monitoring hub 701. As an example, notifications, warnings, advice, etc. can be displayed to caregivers through a display or display interface 709. In some examples, the display may be a screen or a monitor. In addition, particularly when the screen is not a touch sensor type, an input device such as a keyboard may be included. In other examples, the display interface may include a port that allows a monitor to be connected as a peripheral device. In addition, the monitoring hub 701 can be connected to a computer such as a laptop or desktop.

  In some examples, a projector 711 may be included as part of the surveillance hub 701. As an example, a projector 711 can be included as part of an infant station and can be used to display light or video viewed by the infant. This mechanism can help improve the atmosphere of the environment with subdued light, color or video. In some examples, this may be used to provide learning content to an infant.

  In this example, each sensor 721 includes a camera 723 and an audio sensor 725. The camera 723 can be used to transmit the video viewed by the caregiver on the monitor through the mobile device 729 or the like. The camera 723 can also be used to collect data measurements related to the infant, such as body posture. The voice sensor 725 can use the voice heard by the caregiver for transmission through the mobile device 729 or the like. The audio sensor 725 can also be used to collect data measurements related to the infant's surroundings and the environment. In addition, the voice sensor 725 can be used to collect data measurements related to voice from infants, such as crying, word intelligibility. In particular, each sensor 721 can be removed from the monitoring hub 701 in order to improve the positional relationship between the infant and these devices. Other parts of the monitoring hub 701 may be removable as well, so that the monitoring hub 701 is modular.

  In this embodiment, the infant monitoring device interface 713 facilitates wireless communication with the infant monitoring device 727. In addition, the infant monitoring device 727 can be charged with a charging base 715 associated with the monitoring hub 701. The charging stand 715 may be an induction base so that the infant monitoring device 727 can be placed in contact with the charging stand 715 during charging. An example of a charging stand included in an infant station is described above with respect to FIG. 5B.

  According to various embodiments, the monitoring hub 701 includes a client device interface 717 that enables the monitoring hub 701 to communicate wirelessly with a mobile device 729 such as a smartphone, tablet or the like. Mobile device 729 includes software that facilitates mechanisms such as data preprocessing, early warning and remote observation. In addition, content that can be included in the mobile device 729 includes learning, social and environmental information. A typical user of the mobile device 729 is a carer and can view various data from the infant monitoring device 727. In some examples, raw data measurements from an infant monitoring device can be viewed. However, processed information from the monitoring hub 701, such as health measurements and the optimal time and method of sending learning information to the infant, may provide more useful information for carers. In addition, as described above, information from each sensor 721 may be accessible from the mobile device 729. In various embodiments, an API interface can be provided to a third party so that more applications can run on the mobile device 729.

  According to various embodiments, the infant monitoring device 727 and / or the monitoring hub 701 can communicate with IOS and / or Android devices. Specifically, BLE is a communication stack that can be used for data exchange and firmware upgrades. In this embodiment, the API includes access in debug mode from each sensor to raw data. A storage device API may be provided for each sensor and allows the mobile device 729 to download and process data upon request.

  Although not shown, the tablet device can also communicate with the monitoring hub 701 through the client device interface 717. The tablet device can serve as an accessory that delivers to the caregiver a dialogue for structured learning for use with an infant. The tablet, in some cases, has additional sensors that help evaluate infant growth parameters. However, according to various embodiments, the first 24 months do not expect the infant to interact with the tablet.

  In this example, the platform interface 719 is used for communication with the platform 731. As described above with respect to various examples, the monitoring hub 701 can send data to the platform 731 and receive information from the platform 731. As an example, the monitoring hub 701 can send raw data measurements to the platform 731 to receive each model and learning material from the platform 731.

  FIG. 7B shows a schematic diagram of another example of the monitoring hub. In this example, the monitoring hub 735 may be a mobile device such as a smartphone or a tablet. The monitoring hub 735 can perform data preprocessing, content storage in a cache, and / or infant status assessment. The monitoring hub 735 can also include learning content and schedule (s). In addition, the monitoring hub 735 can also provide digital signal processing, human interface and data security. Furthermore, model-based content adaptation can be provided at the monitoring hub 735. For this reason, each model acquired from the platform 757 can be adjusted according to the infant's developmental age and requirements. Specifically, each development model can be evaluated at the monitoring hub 735 and the content from the library can be selected and customized. One example of content adaptation to apply to activities involving dialogue involves selecting an activity involving a series of dialogues that does not tire the infant suitable for infant development. Specifically, it is possible to determine the development age of a certain infant and the duration of the dialogue window suitable for that age. Using this information, the content from the content library stored in the platform 757 is selected and adapted to be appropriate for the infant. This adapted content can then be provided to the infant during use of the appropriate dialog window.

  In this example, the monitoring hub 735 includes a processor 737, memory 739, persistent storage 741, display 743, device interface 751, infant monitoring device interface 745, USB / micro USB port 747 and platform interface 749. . Although particular parts are shown, it should be understood that some of these parts may be omitted without departing from the scope of the present disclosure. Additional components may also be included depending on the desired operation of the monitoring hub 735 and infant monitoring system.

  In this embodiment, processor 737 and memory 739 can be used to process data measurements received from infant monitoring device 753. Specifically, this data can be processed for observation and / or inference, as described above with respect to FIG. In addition, the processor 737 and the memory 739 can be used to customize content for infants so as to be age appropriate learning materials. Persistent storage 741 can store any model, graph, etc. received from platform 757 along with content and schedule (s). Further, the persistent storage 757 can store information specific to the infant.

  In this example, display 743 allows carers to view and / or interact with surveillance hub 735. By way of example, the caregiver can view through the display 743 the observations or guesses made about the infant, watch video images, listen to audio from the infant's room, and enter data. In addition, notifications, warnings, advice, etc. can be displayed through a display or display 743.

  In this embodiment, the device interface (s) 751 facilitate the operation of each peripheral device with the infant monitoring system. As an example, ambient detection, such as local detection of the environment, vibration detection, audio detection, and visual monitoring may be desirable. As such, various external devices 759 may be included as part of the infant monitoring system. Specifically, the camera 761 is used to transmit the video so that the carer can view it on the monitor through the display 743 or the like. The camera 763 can also be used to collect data measurements related to the infant, such as body posture. The audio sensor 765 is used to transmit audio and allow the caregiver to hear through a speaker or the like included in the mobile device. The audio sensor 765 can also be used to collect data measurements related to the infant's surroundings and environment. In addition, the voice sensor 765 can be used to collect data measurements related to the infant's voice, such as crying, wording. In some examples, a projector 763 can be included as part of the surveillance hub 735. The projector 763 can be used to display light or video that an infant sees. This mechanism can help improve the atmosphere of the environment with subdued light, color or video. In some examples, this may be used to provide learning content to an infant. According to various embodiments, external device 759 communicates wirelessly with monitoring hub 735 via device interface (s) 751. Since each device is physically separated from the monitoring hub 735, these devices can be conveniently located with respect to the infant.

  In this example, tablet device 759 (or other mobile device) can communicate with monitoring hub 735 through device interface (s) 751. The tablet device 759 can serve as an accessory that delivers to the caregiver a dialogue for structured learning that is used with an infant. The tablet may in some examples have additional sensors that help evaluate infant growth parameters. By way of example, the tablet device 759 can be used to monitor voice or video from the infant's surroundings, particularly when the tablet device 759 is installed near an infant and the mobile device is installed near a carer. According to various embodiments, the infant does not expect to interact with the tablet device 759 for the first 24 months.

  In this example, the monitoring hub 735 includes a number of interfaces. As an example, the infant monitoring device interface 745 facilitates wireless communication with the infant monitoring device 753. The USB / micro USB port 747 can be used as a plug-in for the charging stand 755 as shown in FIG. 5C. The charging stand 755 may be an induction base so that the infant monitoring device 753 can be placed in contact with the charging stand 755 during charging. In this example, the platform interface 749 is used for communication with the platform 757. As described above with respect to various examples, the monitoring hub 735 can send data to the platform 757 and receive information from the platform 757. As an example, the monitoring hub 735 can send raw data measurements to the platform 757 to receive each model and learning material from the platform 757.

  In this example, the monitoring hub 735 may be IOS, Android or similar device. BLE is a communication stack that can be used for data exchange and firmware upgrades. In this embodiment, the API includes access in debug mode from each sensor to raw data. A storage device API may be provided for each sensor and allows the mobile device to download and process data on demand.

  According to various embodiments, when a mobile device is used as the monitoring hub 735, the infant monitoring system may be portable. In this way, the monitoring system can be used outdoors, in remote locations outside the house, and the like. This system allows continuous monitoring to be uninterrupted when the infant is taken outdoors or elsewhere. In these embodiments, the infant monitoring device 753 can continuously transmit data to the mobile device. If there are other peripheral devices used for home surveillance, such as cameras 761, audio sensors 765 or the like, which are very difficult or inconvenient to carry outdoors or when traveling, these The device can be turned off. For example, by setting the monitoring system to the remote monitoring mode, peripheral devices such as the external devices 759 and the tablet device 759 can be set in the sleep mode or the energy saving mode, and transmission of information can be stopped when going out.

  The infant monitoring device can be used in a variety of ways, as described in the various embodiments herein. By way of example, an infant monitoring system can be used to assess infant development and health, present learning materials, advise carers associated with infants or the like. Several examples of processes that can be performed by the infant monitoring system are described below in conjunction with FIGS. 8-13. Some examples can be processed using computer code and computer readable media.

  FIG. 8 shows a flowchart of an example of a process for obtaining the infant's developmental age relative to the infant's biological age. In this example, at 801, measurement data related to an infant is received. Specifically, the measurement data is transmitted from each sensor associated with the infant monitoring apparatus and received by the monitoring hub. As described above with respect to various embodiments, the measurement data may include aspects such as infant posture and movement, movement, body temperature, posture and electrical skin response. Other criteria can be used depending on the application.

  In this example, the measurement data is then analyzed in relation to a development model obtained at 803 from a remote platform. According to various embodiments, the remote platform is configured to receive information from a number of monitoring hubs associated with a corresponding infant monitoring device, and the developmental model is received and aggregated from the number of monitoring hubs. Based on information. Specifically, the development model is assembled using machine learning to identify patterns and characteristics of information received from multiple monitoring hubs, according to various examples. The development model may be assembled and updated on the platform as new information is received. In a specific example, the development model can include measurement data, observations, inferences or other criteria corresponding to infants of any age.

  In some examples, the developmental model includes a set of model measurement data corresponding to infants of different ages. This model measurement data is an aggregation of information received from multiple monitoring hubs associated with infants of different ages. More specifically, model measurement data and other desired criteria that are selected based on aggregation of information from a number of monitoring hubs are provided for each developmental age. The model measurement data may be based on an average of measurement data associated with multiple monitoring hubs in some examples. In addition, for abnormal data such as data far from other data, in some examples, data that is the basis of an error or that distorts model measurement data incorrectly may be omitted. According to various embodiments, the development model is updated as additional information is received from multiple monitoring hubs or periodically to incorporate new information received from multiple monitoring hubs.

  In other examples, analyzing the measurement data may include processing the measurement data into observations about the infant and comparing the observations with a developmental model. Here, the observation includes one of sleep, mobility, stress, posture, comfort, health, vigilance or clarity. In such an example, the development model includes model observations associated with infants of different ages. These model observations are based on aggregate information received from multiple surveillance hubs associated with infants of different ages. More specifically, there is a set of other desired criteria selected based on model observations, measurement data, and aggregate information from multiple monitoring hubs for each developmental age. Model observations can also be based on an average of observations associated with multiple monitoring hubs in some examples. In addition, for abnormal data such as data far from other data, in some examples, data that is the basis of an error or that distorts model observation incorrectly may be omitted. According to various embodiments, the development model is updated as additional information is received from multiple monitoring hubs or periodically to incorporate new information received from multiple monitoring hubs.

  In yet another example, analyzing the measurement data may include processing the measurement data into a guess about the infant and comparing the guess to a developmental model. Here, the guess includes one of acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety or infant emotional state. In such an example, the development model includes model guesses associated with infants of different ages. These model inferences are based on aggregate information received from multiple surveillance hubs associated with infants of different ages. More specifically, there is a set of other desired criteria selected for each developmental age based on model guesses, measurement data, and aggregate information from multiple monitoring hubs. The model guess can also be based on an average of guesses associated with multiple monitoring hubs in some embodiments. In addition, for abnormal data such as data far from other data, in some examples, data that is the basis of the error or that distorts the model guess incorrectly may be omitted. According to various embodiments, the development model is updated as new information is received from multiple monitoring hubs or periodically to incorporate new information received from multiple monitoring hubs.

  In this example, the developmental age of the infant is determined based on a comparison between the measured data at 805 and the development model. Specifically, according to various embodiments, measurement data, observations, guesses, or other criteria associated with an infant can be compared to model data contained in a development model. Specifically, for any developmental age, an average infant model measurement data is estimated based on aggregated information from multiple monitoring hubs. The infant measurement data to be evaluated is then compared with the model measurement data associated with the developmental model. The developmental age associated with the model measurement data that best matches the measurement data of the infant being evaluated is selected as representing the developmental age of the infant. In some examples, the age of development in the development model may be discontinuous or continuous, such as when the development model is created by data interpolation.

  Next, in this example, at 807, the infant's developmental age is compared with the biological age. Here, the biological age is obtained at the monitoring hub by manual input. In some examples, the infant's living age can be entered by the parent, carer or other user with whom the infant is associated when the user first uses the infant monitoring system or anytime thereafter. This biological age can be stored for reference. By comparing the infant's developmental age with the biological age, the caregiver, parent or other user involved with the infant can be provided with a prospect as to whether the infant is usually developing earlier or later than expected. This information can help determine if further intervention or evaluation is recommended. As an example, if a noticeable delay is found, the child may be encouraged to have a doctor diagnose the disease.

  According to various embodiments, the process described in this example can be performed using various mechanisms associated with the infant monitoring system. As an example, as described in each of the previous examples, the infant monitoring device can be used to acquire infant measurement data. In addition, as also described in the previous examples, the monitoring hub receives the measurement data, analyzes the measurement data associated with the development model, receives the user's manual input, It can be used for various actions, such as determining the infant's developmental age based on a comparison with the development model, and providing a comparison between the infant's developmental age and the biological age. In a particular embodiment, the development model is created on a remote platform configured to receive information from multiple monitoring hubs and their corresponding infant monitoring devices. In some examples, receiving the measurement data, analyzing the measurement data in relation to the development model, receiving the user's manual input, and determining the developmental age of the infant based on the comparison of the measurement data with the development model Various operations can be performed on the remote platform, such as providing a comparison between the infant's developmental age and living age. In such an example, the user can enter data and view the data and results through a portal provided by the remote platform.

  FIG. 9A shows a flowchart of an example of processing for presenting customized learning content to an infant based on the infant's developmental age. In this example, the measurement data of the corresponding infant is specified at 901 together with the first module of the learning content previously presented to the infant. Specifically, the measurement data corresponds to data acquired when the first module of learning content is presented. As described above with respect to various examples, measurement data can be acquired with each sensor associated with the infant monitoring device, such as infant gaze intensity and duration, infant posture and movement, movement, body temperature, body position and electrodermal response, etc. Items can be included. Other criteria can be used depending on the application. The first module of learning content can include any learning content. As an example, the learning content can include material related to a particular theme. Some examples of each theme include language, sounds, words, numbers, colors, motor skills and cognitive skills. As mentioned in this example, the learning content module is learning content that is to be presented in one session with varying amounts.

  Next, in this example, at 903, the measurement data is analyzed in relation to the development model obtained from the remote platform to determine if the first module of learning content was appropriate for the infant. As described in previous examples, the remote platform is configured to receive information from a number of monitoring hubs associated with each infant monitoring device. The development model is built with aggregated information from a number of monitoring hubs on a remote platform. The development model may include criteria such as measurement data, observations, guesses, etc. corresponding to the infant's response to the first module of learning content or similar learning content.

  In some examples, analysis of the measurement data involves processing the measurement data into observations about the infant and comparing the observations with the developmental model to determine if the first module of learning content was inappropriate for the infant. Including. As described in the various examples above, observations can include aspects such as sleep, mobility, stress, posture, comfort, health, vigilance, and / or clarity. Therefore, if an undesirable level of stress is detected, the first module of learning content is considered inappropriate for the infant. Similarly, if other observations at an undesirable level are detected, the first module of learning content is considered inappropriate for the infant. Conversely, if a healthy level of observation is detected, the first module of learning content may be considered appropriate for an infant.

  In another example, the analysis of the measurement data includes processing the measurement data into an inference about the infant and comparing the inference to the developmental model to determine if the first module of learning content was inappropriate for the infant. Including. As explained in the various examples above, speculation may include aspects such as acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety and / or emotional state of the infant. it can. Therefore, if an undesired level of emotional state is detected, the first module of learning content is considered inappropriate for the infant. Similarly, upon detecting other guesses at an unfavorable level, the first module of learning content is considered inappropriate for the infant. Conversely, if a healthy level guess is detected, the first module of learning content may be considered appropriate for an infant.

  In this example, then, at 905, the infant's developmental age is determined based on whether the first module of learning content was appropriate for the infant, as reflected in the measurement data analysis. As already explained, the developmental model can include criteria such as measurement data, observations, inferences, etc. corresponding to infants' responses of any developmental age to the first module of learning content or similar learning content. The developmental age of the infant can be determined based on comparison of the measured data or other criteria with the development model. In addition, the comparison of measured data or other criteria to the development model can also indicate whether the first module of content is too difficult or difficult for the infant based on stress, discomfort, and the like. If the content is too difficult, the subsequent learning content module can be adjusted appropriately.

  In this example, the second module of learning content is then selected at 907 based on the infant's developmental age and presented at 909. In some examples, as reflected in the analysis at 903, the second module of learning content is selected based on whether the first module of learning content is appropriate or inappropriate for the infant. Specifically, when an appropriate level of stress is detected from an infant when the first module of learning content is presented, the second module of learning content equivalent to or more difficult than the first set of learning content is selected. Similarly, when an appropriate level of comfort is detected from an infant when the first module of learning content is presented, the second module of learning content that is equivalent to or more difficult than the first module of learning content is selected. In another example, a second module of learning content equal to or more difficult than the first module of learning content is detected when the first module of learning content is presented and an acceptability for an appropriate level of learning is detected from an infant. Select. Similarly, if an appropriate level of measurement data, observations, guesses or other criteria is found when presenting the first module of learning content, an equivalent level or more difficult material can be selected as the second module of learning content. . Conversely, if an inappropriate level of measurement data, observations, guesses or other criteria is found when presenting the first module of learning content, an easier material can be selected as the second module of learning content. According to various embodiments, the second module of learning content may include materials that are related or not related to the first module of learning content. In some examples, the second module of learning content includes materials or suggestions for caregivers who are involved with the infant.

  According to various embodiments, the process described in this example can be performed using various mechanisms associated with the infant monitoring device. For example, as described in the previous examples, the infant monitoring apparatus can be used to acquire measurement data when the first module of learning content is presented to the infant. In addition, as also described in the previous examples, the monitoring hub receives the measurement data, analyzes the measurement data with respect to the development model obtained from the remote platform, and thereby the first module of learning content Determine the child's developmental age based on whether the first module of the learning content was appropriate for the child, as reflected in the analysis of the measurement data, to determine whether the child was appropriate Can be used to perform various operations such as presenting a second module of customized learning content.

  FIG. 9B shows a flowchart of an example of a process for presenting customized learning content to an infant based on the past performance of the infant. This process is similar to the process described with respect to FIG. 9A, but differs to select customized learning content based on the infant's response to previous learning content without having to evaluate the infant's developmental age. This process can be repeated so that the selection of learning content is further improved each time it is repeated so that it is more appropriate for an infant.

  In this example, the first module of the learning content previously presented to the infant is identified 901 together with the corresponding infant measurement data. Specifically, the measurement data corresponds to data acquired when the first module of learning content is presented. As described above with respect to various examples, measurement data can be acquired with each sensor associated with the infant monitoring device, such as infant gaze intensity and duration, infant posture and movement, movement, body temperature, body position and electrodermal response, etc. Items can be included. Other criteria can be used depending on the application. The first module of learning content can include any learning content. As an example, the learning content can include material related to a particular theme. Some examples of each theme include language, sounds, words, numbers, colors, motor skills and cognitive skills. As mentioned in this example, learning content modules vary in the amount of learning content to be presented in one session.

  Next, in this example, at 903, the measurement data is analyzed in relation to the development model obtained from the remote platform to determine if the first module of learning content was appropriate for the infant. As described in the previous examples, the remote platform is configured to receive information from a number of monitoring hubs associated with each corresponding infant monitoring device. The development model is built on the remote platform based on the information gathered from many monitoring hubs. The development model may include criteria such as measurement data, observations, guesses, etc. corresponding to the infant's response to the first module of learning content or similar learning content.

  In some examples, the analysis of the measurement data processes the measurement data into observations or guesses about the infant and compares the observations or guesses with the developmental model to determine whether the first module of learning content was appropriate for the infant. . As described in the various examples above, observations can include aspects such as sleep, mobility, stress, posture, comfort, health, vigilance, and / or clarity. Also, as explained in the various examples above, the guess includes aspects such as acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety and / or infant emotional state. Can do.

  In this example, it is next determined in 911 whether the first module of the learning content is too difficult for the infant. Specifically, if an unfavorable or unhealthy level is detected in measurement data, observations, guesses or other criteria, it can be determined that the learning content of the first module was too difficult. As an example, if an undesirable level of stress is detected, the first module of learning content is considered too difficult. Similarly, when detecting an unfavorable level of infant emotional state, the first module of learning content is considered too difficult for the infant. Conversely, upon detecting a healthy or favorable level of measurement data, observations, guesses or other criteria, the first module of learning content is considered too difficult for the infant.

  In this example, if the first module of the learning content is not too difficult for the infant, a material that is less likely to be tackled is selected as the second module of the learning content in 913. However, if the first module of learning content is too difficult for an infant, at 915 a material that is easy to work on as the second module of learning content is selected. Depending on the system, the second module of learning content may or may not be associated with the first module of learning content. As an example, the first module and the second module may include material from the same theme, or may include material from different themes as a whole. Once the second module of learning content is selected, it is presented to the infant at 909.

  FIG. 10 shows a flowchart of an example of processing for providing learning content customized based on parental preferences. More specifically, the customized learning content can be selected based on preferences input by a user who is related to an infant such as a parent or a caregiver. In this example, a user input as a first preference associated with learning content for infants is received at 1001. This input can be received at the surveillance hub associated with the infant, for example, through a keyboard, touch screen, etc. associated with the surveillance hub. Numerous preferences can be prepared for the user so that they can be selected as the first preference. By way of example, preferences can include themes such as sounds, words, numbers or colors. In another example, a preference may include infant physical activity, such as physical activity related to motor skills or cognitive skills. In some examples, preferences can include a language that is preferred for infants. In some examples, the user can select additional preferences. Specifically, the learning content customized based on both the first and second preferences can be selected by selecting the second preference. You can choose as many additional preferences as you like.

  Next, at 1003, the developmental age of the infant is determined based on the measurement data received from the infant monitoring apparatus. As described in various examples, the infant monitoring device includes each sensor configured to collect measurement data, which are then transmitted to the monitoring hub for analysis. Also, as described in various examples, the measurement data can include criteria such as the infant's posture, movement, movement, body temperature and electrical skin reaction. Other criteria can be used depending on the application.

  In certain embodiments, determining the infant's developmental age includes analyzing measurement data associated with a development model obtained from a remote platform. As described in various examples above, the remote platform receives information from multiple monitoring hubs associated with multiple infant monitoring devices. According to various examples, the developmental model includes a set of model measurement data corresponding to infants of different ages. This model measurement data is based on aggregate information received from multiple surveillance hubs associated with infants of different ages. The model measurement data is based on an average of information received from multiple monitoring hubs that are associated with infants of different ages, for example. In addition, if information that is far away in the information distorts the model inappropriately, it may be removed. In this example, determining the infant's developmental age is based on a comparison of measurement data associated with the infant and model measurement data representing infants of different ages. Specifically, model measurement data that most closely matches infant measurement data is used to estimate the infant's developmental age. Specifically, the development age corresponding to the model measurement data that most closely matches the infant measurement data is selected as an approximate value of the infant development age.

  According to various embodiments, the development model includes model observations associated with infants of different ages. Similar to model measurement data, model observations are based on aggregate information received from multiple surveillance hubs associated with infants at different ages. In addition, analysis of the measurement data includes processing the measurement data into observations about the infant and comparing the observations with a developmental model. Examples of observations include sleep, mobility, stress, posture, comfort, health, alertness, and / or intelligibility. The model observation that best matches the infant observation is used to estimate the infant's developmental age. Specifically, the developmental age corresponding to the (each) model observation that most closely matches the (each) observation of the infant is selected as an approximate value of the infant's developmental age.

  By example, the development model includes model guesses associated with infants of different ages. Similar to model measurement data, model inference is based on aggregate information received from multiple surveillance hubs associated with infants at different ages. In addition, analysis of the measurement data includes processing the measurement data into inferences about the infant and comparing the inference to the development model. Examples of speculation include acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety and infant emotional state. The model guess that best matches the infant's guess is used to estimate the infant's developmental age. Specifically, the development age corresponding to the (each) model guess that best matches the (each) guess of the infant is selected as an approximate value of the infant's development age.

  In this example, the first module of learning content is selected at 1005 based on the developmental age corresponding to the infant and the first preference entered by the user. As previously mentioned, if a user with an infant has selected additional preferences, these preferences are taken into account when selecting the first module of learning content. Depending on the application, learning content can be stored on a monitoring hub or remote platform. Once the first module of learning content is selected, it is displayed at 1009 on the monitoring hub.

  According to various embodiments, the process described in this example can be performed using various mechanisms associated with the infant monitoring system. As an example, as described in the previous examples, an infant monitoring device using each sensor can be used to obtain measurement data. In addition, and as described in the previous examples, the monitoring hub receives measurement data from multiple sensors, analyzes the measurement data to determine the infant's developmental age, learns the infant To perform various operations such as receiving user input for a first preference associated with the content and selecting a first module of learning content based on the developmental age and first preference associated with the infant Can be used. The monitoring hub may also include a display configured to present the first module of learning content.

  FIG. 11 shows a flowchart of an example of processing for creating a customized educational material playlist. In this example, first, at 1101, the monitoring data of the infant is received from each sensor associated with the infant monitoring apparatus. As described above in various examples, the measurement data can include criteria such as movement, body temperature, body position, and electric skin reaction. Other criteria can be used depending on the application.

  In this example, the measurement data is then analyzed in relation to the development model obtained from the remote platform at 1103. As described above in various embodiments, the remote platform receives information from multiple monitoring hubs and their corresponding infant monitoring devices. The development model is assembled with aggregate information from multiple monitoring hubs. According to various embodiments, the developmental model includes model measurement data sets corresponding to infants of different ages. Here, the model measurement data set is based on aggregate information received from multiple monitoring hubs associated with infants of different ages. In some examples, the model measurement data is based on an average of information received from multiple monitoring hubs associated with infants of different ages. In addition, if information that is far away in the information distorts the model inappropriately, it may be removed.

  In some examples, the development model includes model observations associated with infants of different ages. Similar to model measurement data, model observations are based on aggregate information received from multiple surveillance hubs associated with infants of different ages. Further, the analysis of the measurement data includes processing the measurement data into observations about the infant and comparing the observations with a developmental model. Examples of observations include sleep, mobility, stress, posture, comfort, health, alertness, and / or intelligibility.

  In a specific example, the development model includes model guesses associated with infants of different ages. Similar to model measurement data, model inference is based on aggregate information received from multiple surveillance hubs associated with infants of different ages. Further, the analysis of the measurement data includes processing the measurement data into a guess about the infant and comparing the guess to a development model. Examples of speculation include acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety and infant emotional state.

  Next, at 1105, the developmental age of the infant is determined based on the comparison between the measurement data and the development model. In this example, the determination of the infant's developmental age is based on a comparison of measurement data associated with the infant and model measurement data representing infants of different ages. Specifically, the development age of the infant is estimated using model measurement data from the development model that most closely matches the infant measurement data. Specifically, the development age corresponding to the model measurement data that most closely matches the infant measurement data is selected as an approximate value of the infant development age.

  In some examples, observations derived from infant measurement data can be used to determine the infant's developmental age. In particular, the developmental age of the infant is approximated using the model observation that best matches the infant's observation. More specifically, the development age corresponding to the (each) model observation that most closely matches the (each) observation of the infant is selected as an approximate value of the infant's development age.

  In some examples, inferences derived from infant measurement data can be used to determine the infant's developmental age. In particular, the infant's developmental age is approximated using the model guess that best matches the infant's guess. More specifically, the development age corresponding to the (each) model guess that most closely matches the (each) guess of the infant is selected as an approximate value of the infant's development age.

  In this example, once the infant's developmental age is determined, 1107 selects a number of learning content modules appropriate for the infant's developmental age. According to various embodiments, these learning content modules are obtained from a remote platform. The learning content module can include any learning content. As an example, the learning content can include material related to a particular theme. Some examples of each theme include language, sounds, words, numbers, colors, motor skills and cognitive skills. As mentioned in this example, the learning content module is learning content that is to be presented in one session with varying amounts.

  Once the learning content modules are selected, they are organized into play lists at 1109. In some examples, the learning content module selected from the playlist is played when the infant is receptive to learning. As described in the previous examples, the measurement data can be used to determine when the infant is receptive to learning. Once this is determined, the learning content to be presented can be selected from the playlist. In some examples, if desired, the user can pause the playlist at the first position and play it again from the first position. In a specific embodiment, once selection from the playlist has begun, the playlist continues to be played until the user selects an instruction to pause or stop the playlist. However, in other embodiments, the playlist continues to be played until it is determined that the infant is not sufficiently receptive to learning. Such a determination can be made based on the analysis of the measurement data acquired after the presentation of the playlist related to the learning acceptability model acquired from the remote platform. In yet another example, the user can access the playlist and play the learning content module according to the user's will.

  According to various embodiments, the process described in this example can be performed using various mechanisms associated with the infant monitoring system. As an example, as described in previous examples, measurement data associated with an infant can be obtained using the infant monitoring device and each sensor associated therewith. In addition, as described in the previous examples, receiving measurement data from each sensor associated with the infant monitoring device and analyzing measurement data associated with the developmental model obtained from the remote platform Determining the infant's developmental age based on comparison of the measured data and the development model, obtaining learning content modules from a remote platform, selecting and organizing a number of learning content modules appropriate for the infant's developmental age The monitoring hub can be used to perform various operations such as creating a playlist and playing a learning content module of the playlist. The monitoring hub receives input from the user and plays, pauses or otherwise moves through the playlist. Further, in some cases, it can be determined when the infant is receptive to learning so that the monitoring hub can replay the selection from the playlist at these times.

  FIG. 12 shows a flow diagram of an example of a process for providing social media recognition for completion of infant learning content. In this example, at 1201, learning content appropriate for an infant is selected based on the developmental age associated with the infant. According to various embodiments, the developmental age of the infant is determined by analyzing measurement data received from the infant monitoring device for a developmental model obtained from a remote platform. As described above with respect to various examples, the measurement data can include criteria such as movement, body temperature, posture, and electrodermal response. Other criteria can be used depending on the application. In addition, as described in the various examples above, the development model can be based on aggregate information received from multiple infant monitoring hubs. In addition, the developmental model can include model measurement data representing infants of different ages.

  In some examples, analyzing the measurement data in relation to the development model includes processing the measurement data into observations about the infant and comparing the observations to the development model. Observation can include aspects such as sleep, mobility, stress, posture, comfort, health, vigilance, and / or intelligibility. In these examples, the developmental model can include model observations that represent infants of different ages.

  In some examples, analyzing the measurement data in relation to the development model includes processing the measurement data into inferences about the infant and comparing the inference to the development model. Inferences can include aspects such as acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety and / or infant emotional state. In these examples, the developmental model can include model guesses that represent infants of different ages.

  Once the developmental age is selected based on the comparison of the infant measurement data with the development model, the learning content appropriate for the infant is selected. The learning content can be selected from various materials. As an example, the learning content can include material related to a particular theme. Some examples of each theme include sound, language, numbers, colors and / or physical activity. In some examples, the learning content is selected based on the learning content presented to the infant so far. In other examples, the learning content includes materials or suggestions for carers who are involved with the infant. Once the learning content is selected, it is presented at 1203 through a monitoring hub associated with the infant.

  According to various embodiments, after presenting the learning content, it is confirmed at 1205 that the presentation of the learning content is complete. In this example, confirming that the presentation of the learning content has been completed includes detecting that the learning content has been played to the end. Therefore, when the learning content is interrupted during reproduction by stopping or temporarily stopping the learning content, it is not confirmed that the learning content is completed.

  After confirming that the presentation of the learning content is completed, 1207 provides social media recognition for the completion of the presentation of the learning content. In some examples, social media recognition is posted to a social media feed associated with an infant caregiver, parent or guardian. Specifically, a monitoring hub associated with an infant can provide an option to post or post this social media recognition. On the other hand, in some examples, the remote platform can provide a post or an option to post. In this example, social media recognition includes information about completed learning content. Specifically, social media recognition may include a level of performance associated with completed learning content. As an example, a different node or level can be specified for each group of learning contents. In another example, each learning content is associated with the achievement itself. Social media recognition may also include information such as themes included in the finished learning content. In one example, a social media recognition post may include a diagram with a message. Similarly, various achievements and learning situations can be posted on social media.

  According to various embodiments, the process described in this example can be performed using various mechanisms associated with the infant monitoring device. As an example, both the infant monitoring device and each sensor associated therewith can be used to obtain measurement data associated with the infant, as described in previous examples. In addition, as described in the previous examples, receiving measurement data from each sensor associated with the infant monitoring device and analyzing measurement data associated with the developmental model obtained from the remote platform Social media recognition indicating that the infant's developmental age is determined based on a comparison between the measured data and the development model, presenting the learning content appropriate for the infant's developmental age, and completing the presentation of the learning content A monitoring hub can be used to perform various operations, such as providing data.

  FIG. 13 shows a flowchart of an example of a process for detecting the performance of an infant. In this example, infant measurement data is received 1301 from each sensor associated with the infant monitoring device. As described in connection with the various examples above, the measurement data can include aspects such as movement, body temperature, body position, and electrodermal response. Other criteria can be used depending on the application.

  In this example, the measurement data is then analyzed at 1303 in relation to the infant's past measurement data set. The past measurement data set includes the measurement data collected so far and the corresponding data when the data such as the related date and / or number of times are collected. According to various embodiments, historical measurement data sets are stored in a monitoring hub associated with the infant and the infant monitoring device. In some examples, analyzing the measurement data in relation to the infant's past measurement data set includes processing the measurement data into observations about the infant and comparing the observations to the infant's past observation set. Yes. As described in various embodiments, observations can include aspects such as sleep, mobility, stress, posture, comfort, health, alertness, clarity. In another example, analyzing the measurement data in relation to the infant's past measurement data set includes processing the measurement data into a guess about the infant and comparing the guess to the infant's past guess set. It is out. As also described in the various embodiments, the guess may include aspects such as acceptability for learning, infant happiness, caregiver presence, environmental factors, infant safety, infant emotional state, etc. it can.

  In this example, at 1305, based on the comparison between the measurement data and the past measurement data set, a determination is made as to whether the current measurement data exceeds the previously detected (each) level. For example, when physical growth such as height / length increase is detected, it is determined that the measured data exceeds past physical growth. In another example, a kind of operation such as turning over, which has not been detected before, may be detected. In some examples, determining that the measured data exceeds the previously detected (each) level includes determining that the measured data exceeds a previously detected (each) level by a certain amount There is also. This amount can be incorporated into the system and can prevent insignificant data or errors in the measurements being detected. As an example, it may be determined only when the growth exceeds a certain amount (eg, 3 mm). Similarly, other types of measurements, observations, guesses or other criteria can be compared.

  If the measurement data matches the past measurement data set, the measurement data does not exceed the level detected so far, and in this example, no track record is detected. In this situation, the measurement data is then stored at 1307. This measurement data can be used in subsequent analysis in addition to past measurement data sets. Similarly, if observations, guesses and / or other criteria are found to match past data, these observations, guesses and / or other criteria can also be stored with the past measurement data set.

  However, if the measurement data exceeds the level detected so far in the past measurement data set, in this example, the performance of the infant is detected at 1309. Various types of achievements can be detected. As an example, performance can include physical growth or an increase in developmental age. Specifically, in the case of physical growth, each sensor can detect body measurements of height or weight that constitute the growth performance. Increased developmental age is proven when the infant shows physical, verbal or other developmental improvements, as described in this document, comparing and detecting measured data with developmental models. May be discovered. In some examples, performance includes reaching milestones that have not been reached so far based on past measurement data. Specifically, milestones may include events such as walking for the first time, talking for the first time, linking words to idioms or sentences. In some embodiments, these nodes may be included in the development model. As described in various embodiments, the development model is based on aggregate information received from a number of monitoring hubs associated with the infant monitoring device. In addition, the development model can be assembled on a remote platform that receives information from multiple monitoring hubs and aggregates the information.

  In this example, once a record is detected, a notification is sent to the caregiver about this record at 1311. According to various embodiments, the carer may include a person related to an infant, such as a parent, a guardian, a babysitter, a nanny, and a relative. The notification can be sent through a monitoring hub in some examples. Notifications can also be sent through any other medium depending on the application. As an example, the notification can be sent by e-mail or mobile phone mail by the monitoring hub.

  According to various embodiments, at 1313, an option to post social media recognition of performance content can be provided. Specifically, social media recognition may include posting to a social media feed associated with a user such as an infant caregiver, parent or guardian. In some examples, if the user chooses to post to social media, the monitoring hub can provide a post message to social media. Alternatively, in some examples, the remote platform can provide social media posting messages. In the present embodiment, the post option allows filtering the post to a caregiver, parent or other person who is associated with the infant because of confidentiality. However, if confidentiality is not a problem, in some embodiments, a post can be automatically created without having to confirm the option for the user to post information. According to various embodiments, social media recognition includes information about achievements achieved. Specifically, social media recognition may include a description, title or message associated with the performance. By way of example, social media recognition may include a message such as “Emily, you walked for the first time today, congratulations!”. In some examples, the social media recognition post message may include a figure with a message about the achievement.

  According to various embodiments, the process described in this example can be performed using various mechanisms associated with the infant monitoring device. As an example, as described in previous examples, measurement data associated with an infant can be obtained using the infant monitoring device and each sensor associated therewith. In addition, as described in the previous examples, receiving measurement data from each sensor associated with the infant monitoring device and analyzing the measurement data in relation to the infant's past measurement data set , Storing past measurement data sets, detecting results based on comparisons of measurement data with infants' past measurement data sets, and notifying caregivers who are involved with the infants about this performance A monitoring hub can be used to perform various operations such as. In some examples, the monitoring hub is configured to provide an option to post and / or post performance social media recognition.

Although the foregoing concepts have been described in detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. Note that there are many alternative ways of executing each process, each system, and each device. Accordingly, the embodiments are to be regarded as illustrative rather than restrictive, respectively.

Claims (60)

A wearable infant monitoring device including a plurality of sensors configured to detect infant activity and posture;
A transmission interface configured to transmit measurement data related to the activity and position of the infant;
A system comprising a processor configured to determine acceptability of the infant for learning and a monitoring hub configured to receive measurement data,
Each learning material is provided to a caregiver through a display interface based on the child's acceptability for learning.   The system of claim 1, wherein the processor determines acceptability of the infant for learning based on a duration of smiles and gaze.   The system of claim 1, wherein the processor determines acceptability of the infant for learning based on smile and gaze intensity.   The system of claim 1, wherein the processor processes the data measurements to customize learning content based on a developmental age associated with the infant.   The system of claim 1, wherein the monitoring hub further comprises a persistent storage device configured to store learning content and each schedule.   The system of claim 1, wherein the monitoring hub further comprises a persistent storage device configured to store each infant model and each table.   The system of claim 1, wherein the monitoring hub further comprises a platform interface configured to facilitate communication between the monitoring hub and a platform, wherein the platform aggregates data from a plurality of infant monitoring devices. .   The system of claim 1, further comprising an external device.   The system of claim 8, wherein the external device is a camera.   The system of claim 9, wherein the external device is a voice sensor.   The system of claim 9, wherein the external device is a projector.   The system of claim 9, wherein the external device is a tablet device.   The system of claim 1, wherein the remote infant monitoring hub is a mobile device.   The system of claim 1, wherein the plurality of sensors includes a triaxial accelerometer, an electrical skin reaction (GSR) detector, a gyroscope, and a body temperature sensor.   The system of claim 1, wherein the transmission interface is a Bluetooth Low Energy (BLE) interface. A wearable infant monitoring device including a plurality of sensors configured to detect infant activity and posture;
A transmission interface configured to transmit measurement data related to the activity and position of the infant;
A device comprising a charging interface,
A monitoring hub is configured to receive measurement data from the wearable infant monitoring device and includes a processor configured to determine acceptability of the infant for learning, each learning material being the child's learning A device provided to caregivers through a display interface based on their acceptability for.   The apparatus of claim 16, wherein the processor determines acceptability of the infant for learning based on durations of smiles and gaze.   The apparatus of claim 16, wherein the processor determines acceptability of the infant for learning based on smile and gaze intensity.   The apparatus of claim 16, wherein the processor processes the data measurements to customize learning content based on a developmental age associated with the infant.   The apparatus of claim 16, wherein the monitoring hub further comprises persistent storage configured to store learning content and each schedule. An infant monitoring device interface configured to receive measurement data wirelessly transmitted from an infant monitoring device including a plurality of sensors configured to track infant activity and posture;
A processor configured to process the measurement data to select and customize learning material for an infant associated with the infant monitoring device;
Persistent storage configured to store learning material provided by a remote platform configured to receive information from a plurality of infant monitoring hubs associated with the plurality of infants;
An infant monitoring hub comprising a platform interface configured to exchange information with the remote platform,
An infant monitoring hub, wherein the remote platform is configured to analyze measurement data to identify a plurality of receptive windows associated with the plurality of infants and learning material suitable for the plurality of receptive windows.   The infant monitoring hub of claim 21, further comprising a display configured to allow a caregiver to interact with the device.   The infant monitoring hub of claim 21, wherein the measurement data includes movement, body temperature and body position.   The infant monitoring hub of claim 21, wherein the platform identifies a plurality of receptive windows using data from one or more of a directional detector, a gaze detector, a shared attention detector, and a cognitive detector.   The infant monitoring hub according to claim 21, wherein the measurement data includes whether the infant is in a prone or supine position.   The infant monitoring hub of claim 21, wherein the measurement data includes an electrical skin reaction.   The infant monitoring hub of claim 21, wherein the processor is configured to process the measurement data into observations about the infant.   28. The infant monitoring hub of claim 27, wherein the observation comprises a state of sleep, mobility, stress, posture, comfort, health, alertness or intelligibility of the infant.   The infant monitoring hub of claim 21, wherein the processor is configured to process the measurement data into inferences about the infant.   30. The infant monitoring hub of claim 29, wherein the guess includes acceptability of the infant for learning, infant happiness, caregiver presence, environmental factors, infant safety or infant emotional state.   The infant monitoring hub of claim 21, wherein the processor is further configured to customize the learning material for the particular infant.   The infant monitoring hub of claim 21, wherein the persistent storage is further configured to store schedules, models and tables received from the platform.   The infant monitoring hub of claim 21, further comprising a peripheral device.   The infant monitoring hub according to claim 21, wherein the peripheral device is a camera, a microphone or a projector. An interface configured to receive measurement data transmitted wirelessly from an infant monitoring device including a plurality of sensors configured to track infant activity and posture;
A processor configured to process the measurement data;
A persistent storage configured to store learning material provided from a remote platform configured to receive information from a plurality of infant monitoring hubs associated with the plurality of infants;
A remote platform interface configured to exchange information with the remote platform, the system comprising:
The remote platform is configured to analyze measurement data to identify a plurality of receptive windows associated with the plurality of infants and learning material suitable for the plurality of receptive windows.   36. The system of claim 35, wherein the measurement data includes whether the infant is in a prone or supine position.   36. The infant monitoring hub of claim 35, wherein the processor is configured to process the measurement data into observations about the infant.   38. The infant monitoring hub of claim 37, wherein the observation includes a sleep, mobility, stress, posture, comfort, health, vigilance or articulation state of the infant.   36. The infant monitoring hub of claim 35, wherein the processor is configured to process the measurement data into inferences about the infant.   40. The infant monitoring hub of claim 39, wherein the inference includes acceptability of the infant for learning, infant happiness, caregiver presence, environmental factors, infant safety or infant emotional state. An infant monitoring device associated with a plurality of sensors configured to obtain measurement data;
The measurement in connection with a learning acceptability model obtained from a remote platform configured to receive measurement data from the plurality of sensors and receive information from a plurality of monitoring hubs associated with a plurality of infant monitoring devices. A monitoring hub configured to analyze the data, the system comprising:
A system that analyzes the measurement data to predict when and for how long an infant associated with an infant monitoring device can accept learning.   42. The system of claim 41, wherein the measurement data includes infant gaze intensity and duration.   42. The system of claim 41, wherein the measurement data includes an infant's posture and movement.   42. The system of claim 41, wherein the learning content is customized based on a developmental age associated with the infant.   42. The system of claim 41, wherein a developmental age associated with the infant is determined based on the measurement data.   42. The method of claim 41, wherein the measurement data includes movement, body temperature, posture, and electrodermal response. Receiving measurement data acquired from a plurality of sensors associated with the infant monitoring device at a monitoring hub;
Analyzing the measurement data in relation to a learning acceptability model obtained from a remote platform configured to receive information from a plurality of monitoring hubs associated with a plurality of infant monitoring devices;
Predicting when and for how long an infant associated with the infant monitoring device may accept learning.   48. The method of claim 47, wherein the measurement data includes an infant's gaze intensity and duration.   48. The method of claim 47, wherein the measurement data includes infant position and movement.   48. The method of claim 47, further comprising presenting customized learning content based on a developmental age associated with the infant.   48. The method of claim 47, further comprising determining a developmental age associated with the infant based on the measurement data.   52. The method of claim 51, wherein predicting a time and duration is based on the developmental age associated with the infant.   48. The method of claim 47, wherein the measurement data includes movement, body temperature, posture and electrodermal response.   48. The method of claim 47, wherein analyzing the measurement data comprises processing the data measurements into observations about the infant and comparing the observations with the learning acceptability model.   48. The method of claim 47, wherein the observation includes one of sleep, mobility, stress, posture, comfort, health, alertness or clarity.   48. The method of claim 47, wherein analyzing the measurement comprises processing the data measurement into a guess about the infant and comparing the guess to the learning acceptability model.   57. The method of claim 56, wherein the guess includes one of learning acceptability, infant happiness, caregiver presence, environmental factors, infant safety or infant emotional state.   58. The method of claim 57, wherein customizing learning content for the infant includes preparing materials for a caregiver associated with the infant.   59. The method of claim 58, wherein customizing learning content for the infant comprises providing suggestions to caregivers associated with the infant. 48. The method of claim 47, further comprising presenting customized learning content based on previous learning associated with the infant.