Description:FIELD OF THE INVENTION
The present invention relates to a system for monitoring and tracking children's activity levels, specifically utilizing embedded machine learning (ML) models, vibration sensors, and accelerometers. The system is designed to continuously assess the activity status of children, classify activity levels, and notify caregivers in real-time of inactivity, ensuring enhanced safety and support for children playing in various environments, including indoor and parking areas, where internet connectivity may be unreliable.
BACKGROUND OF THE INVENTION
Monitoring children's physical activity levels is crucial for promoting a healthy lifestyle and detecting potential health issues. However, there is a lack of affordable, easy-to-use systems for real-time activity tracking, especially for young kids who may not wear fitness trackers. Prolonged inactivity can be an indicator of potential health concerns, but current solutions are either expensive or intrusive.
1. Wearable Fitness Trackers (e.g., Fitbit, Garmin): These devices track physical activity but require children to wear them consistently and are often too large for young children. They primarily use accelerometers real-time cloud alerts based on inactivity.
2. Mobile Apps: Some smartphone apps track steps and activity through the phone’s sensors, but they require the child to carry the phone, which may not be suitable for younger children.
3. Prior Art: There are patents around general fitness monitoring systems, such as US Patent 9,986,581 (wearable device activity monitoring), but these focus on adult users and do not target children or embedded machine learning with cloud alerts.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention integrates a vibration sensor and accelerometer to monitor and classify children's activity levels. Using an embedded machine learning (ML) model on a microcontroller, the system analyzes movement data in real time, categorizing activity levels as high, moderate, or low. The system sends this data through a Bluetooth network to a mobile device where caregivers can access a dashboard for real-time monitoring. The system alerts caregivers through mobile notifications when prolonged inactivity is detected, allowing timely intervention. This invention is designed to operate without the need for internet connectivity, making it effective in environments where traditional network connections might not be available. The compact, cost-effective nature of the system makes it a viable and child-friendly alternative to other wearable fitness trackers or activity monitoring devices, offering a more practical and efficient solution for monitoring children's activity levels and ensuring their safety during play.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The proposed system combines a vibration sensor and accelerometer for tracking movement in children, with an embedded machine learning (ML) model running on an embedded board. The system continuously monitors activity levels and classifies them into various categories (e.g., high, moderate, low). When low activity is detected for a prolonged period, the system sends information through the network of blue tooth, which is accessible via a dashboard. Caregivers can receive notifications in real-time, allowing them to monitor the child's activity ground level or parking area while playing and intervene when necessary.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: Proposed system connectivity
FIGURE 2: Flow chart of Proposed System
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The proposed system combines a vibration sensor and accelerometer for tracking movement in children, with an embedded machine learning (ML) model running on an embedded board. The system continuously monitors activity levels and classifies them into various categories (e.g., high, moderate, low). When low activity is detected for a prolonged period, the system sends information through the network of blue tooth, which is accessible via a dashboard. Caregivers can receive notifications in real-time, allowing them to monitor the child's activity ground level or parking area while playing and intervene when necessary.
Key Features:
• Vibration and accelerometer sensors detect different levels of movement and orientation.
• ML-based classification of activity levels on the embedded board.
• Blue tooth network to connect the mobile phone in fast way within network
• Real-time monitoring through a dash board mobile notifications.

1. Embedded Machine Learning: The system uses a lightweight embedded ML model for real-time activity classification without the need for external processing.
2. Inactivity Alerts: The system is designed to notify caregivers of prolonged inactivity, which is not a common feature in existing solutions.
3. Affordable and Compact: The use of inexpensive sensors and embedded boards makes this system more accessible and cost-effective compared to high-end wearables or fitness devices.
4. In depended to internet connectivity for reducing the delay: The system is not using the cloud connectivity it need the Bluetooth connectivity so useful in ground and parking area in building where internet is not proper.

ADVANTAGES OF THE INVENTION
• Wearable Fitness Trackers: Our system is light weight and no need the carry mobile phone.
• Mobile Apps: Require carrying a smartphone, which may not be practical for young kids. Our solution is more child-friendly.
• Other ML Solutions: While some solutions not using the machine learning for activity recognition, this system integrates embedded ML directly on the board not cloud, providing a unique combination of real-time classification and notifications.
 
, C , Claims:1. A system for monitoring and classifying children's activity levels, comprising:
a vibration sensor configured to detect movement and orientation of the child;
an accelerometer configured to monitor the movement and orientation of the child;
an embedded machine learning (ML) model running on an embedded board, configured to classify activity levels based on data received from the vibration sensor and accelerometer;
a Bluetooth network configured to send activity data to a mobile device;
a dashboard accessible via the mobile device to display real-time activity levels and send notifications to caregivers when inactivity is detected for a prolonged period.
2. The system as claimed in claim 1, wherein the activity levels are classified into categories of high, moderate, and low based on the data received from the vibration sensor and accelerometer.
3. The system as claimed in claim 1, wherein the embedded ML model classifies the activity levels of the child in real-time without requiring external processing or cloud connectivity.
4. The system as claimed in claim 1, wherein the Bluetooth network is used to establish a fast connection between the system and the mobile device, facilitating real-time activity monitoring and notifications.
5. The system as claimed in claim 1, wherein the mobile device receives a notification when the system detects low activity in the child for a specified period of time.
6. The system as claimed in claim 1, wherein the system is configured to operate without internet connectivity by utilizing Bluetooth for communication, ensuring functionality in environments where internet access is limited, such as ground level or parking areas in buildings.
7. The system as claimed in claim 1, wherein the system includes a mobile application that allows caregivers to view the child’s activity data, receive notifications, and intervene if necessary.
8. The system as claimed in claim 1, wherein the vibration sensor and accelerometer are lightweight and compact, making the system more affordable and child-friendly compared to wearable fitness trackers or other high-end devices.
9. The system as claimed in claim 1, wherein the machine learning model embedded on the board performs activity classification without relying on external cloud processing, ensuring faster data processing and reduced latency.
10. The system as claimed in claim 1, wherein the dashboard displays real-time activity levels, status alerts, and data analysis to caregivers, providing an intuitive interface for monitoring the child's activity at all times.