SYSTEM AND METHOD TO DETECT AND PREVENT CHILD ABUSE IN REAL-TIME IN CHILD CENTRIC ENVIRONMENT

Abstract

The system and method to detect and prevent child abuse in real-time is provided. The system (100) includes a wearable sensor device (110) configured to continuously monitor a child's physiological parameters. A processing subsystem (105) hosted on a server (108) facilitates bidirectional communication among various modules. A control system (120) analyses physiological data to detect stress or abnormalities indicative of potential abuse. A location determination module (130) identifies the child's geographic location through communication signals. A neighbourhood determination module (135) identifies the people around, while an anomaly detection engine (140), powered by an artificial intelligence model, detects abnormal physiological patterns and correlates them with environmental factors linked to high-stress situations. The notification engine (150) transmits alerts to pre-configured stakeholders and escalates alerts if no intervention occurs within a specified timeframe. Additionally, a data storage module (160) retains historical data for further analysis and trend identification related to chronic stress and incidents of abuse. FIG. 1

Specification

Description:FIELD OF INVENTION
[0001] Embodiments of the present disclosure relate to the field of abuse detection, and more particularly, system and method for real-time child abuse detection and prevention utilizing smart wearable devices using Internet of things (IoT) and artificial intelligence (AI) techniques to monitor physiological data, detect stress or abuse in a child centric environment or child centric organizations.
BACKGROUND
[0002] Child abuse, whether emotional, physical, or sexual, is a widespread and deeply concerning issue that can have severe and long-lasting effects on the well-being and development of children. The impacts of such abuse extend well beyond the immediate harm, often affecting the child's mental health, emotional stability, and ability to form relationships too. Early detection and prevention are critical, but existing systems and approaches to safeguarding children from abuse, especially in educational organisations are largely reactive, only identifying incidents after they have occurred. This highlights a significant gap in current technology and the urgent need for systems that can proactively monitor and detect abuse in real time to prevent harm before it escalates.
[0003] The current systems used in child-centric environments or child centric organizations such as schools, child care centres, orphanages, or other similar educational institutions or learning communities rely heavily on reactive measures. CCTV cameras are one of the most widely used tools for monitoring, but they are typically limited to public areas and are incapable of covering private or restricted zones, such as bathrooms or secluded areas where abuse might occur. Even where CCTV is in use, it is primarily helpful in reviewing footage after an incident has been reported, making it ineffective in real-time prevention. Furthermore, CCTV systems cannot detect emotional or psychological abuse, which may not leave visible physical signs but can be equally damaging to the child's well-being.
[0004] Another common approach is the use of panic buttons or emergency alert systems for children, which depend on the child or a bystander to activate them during or after an abusive incident. However, this method is inherently flawed because children may not be in a position to trigger an alert, especially if they are afraid, overwhelmed, or do not recognize that the behaviour they are experiencing constitutes abuse. Panic buttons are also incapable of addressing chronic emotional abuse or prolonged stress, which often manifests gradually and subtly, without an immediate, identifiable incident to report.
[0005] Child centric organisations also implement safety committees and run awareness programs aimed at educating children, parents, teachers and authorities about the signs of abuse and how to report it. While these initiatives are important, they are still manual, human-dependent interventions that do not offer a real-time solution to detect or prevent abuse as it happens. These programs rely on adults or children to recognize the abuse and take action, often leaving gaps in protecting children, particularly those who are unable to speak out or are too young to fully understand their experiences.
[0006] Moreover, current systems lack the ability to monitor the physiological wellness of children, which could be a key indicator of distress or abuse. Signs of stress, such as elevated heart rate, increased skin conductivity, or changes in body temperature, and other wellness or physiological parameters, can indicate that a child is experiencing anxiety or fear. However, no conventional system integrates this type of biometric monitoring to continuously assess a child's well-being in real time. The failure to track physiological data means that early, non-physical signs of abuse often go unnoticed, leading to delayed interventions and potentially exacerbating the harm.
[0007] Finally, existing child protection systems do not leverage artificial intelligence (AI) techniques or real-time analytics in a meaningful way to analyse data and provide immediate, automated alerts. Most systems rely on human review and judgment, which can be slow and error-prone. The absence of AI-driven decision-making and predictive analytics further limits the ability of current solutions to proactively detect patterns of abuse or chronic stress, which could provide critical early warnings to prevent ongoing harm.
[0008] In summary, conventional systems for detecting and preventing child abuse in a child centric organization, are reactive, limited in coverage, and lack the technological capabilities needed for real-time, data-driven monitoring. They fail to detect the early signs of emotional or physical stress in children and do not provide sufficient mechanisms for immediate alerts and intervention. This underscores the urgent need for an AI-powered system that can continuously monitor children's physiological and environmental conditions, detect stress or potential abuse in real time, and trigger alerts to enable swift action and prevention.
[0009] Hence, there is a need for an improved system and method for abuse detection and prevention of abuse in a child centric organizations in real-time, which addresses the aforementioned issue(s).
OBJECTIVE OF THE INVENTION
[00010] An objective of the present invention is to provide a system capable of detecting child abuse-whether sexual, emotional, physical, or any other type of abuse in real time by continuously monitoring physiological and environmental data.
[00011] Another objective of the present invention is to provide a system that continuously tracks a child's physiological parameters, such as heart rate, skin conductivity, and body temperature, through wearable devices to detect signs of stress or distress.
[00012] Another objective of the present invention is to provide a system that triggers immediate alerts when any anomalies or signs of abuse are detected, notifying parents, guardians, or authorities for rapid intervention.
[00013] Another objective of the present invention is to provide a system that detects the child's location, neighbouring environment and neighbourhood people at the time of the abuse to potential threats or high-risk situations using IoT devices and environmental controllers.
[00014] Another objective of the present invention is to provide a system that identifies a network of friends of an individual within the child-centric organization.
[00015] Another objective of the present invention is to provide a system that identifies abnormal patterns in the child's behaviour and physiological data, enhancing the system's ability to detect subtle and prolonged forms of abuse using artificial intelligence.
[00016] Another objective of the present invention is to provide a system that identifies and analyses chronic stress caused by ongoing abuse, using long-term physiological and environmental data trends.
[00017] Another objective of the present invention is to provide a system that provides detailed, contextual alerts, including the child's physiological state, location, and surrounding environment, for faster and informed decision-making.
[00018] Another objective of the present invention is to provide a system that ensures incidents are logged, tracked, and escalated to the appropriate authorities or stakeholders until resolved.
[00019] Another objective of the present invention is to provide a system that is designed to monitor children while respecting their privacy, particularly in private or controlled areas, ensuring real-time detection of abuse without violating personal space.
[00020] Another objective of the present invention is to provide a system that continuously learns and adapts to each child's physiological baseline and applicable behaviour patterns through machine learning and feedback from stakeholders.
[00021] Another objective of the present invention is to provide a system that uses real-time data analytics to analyse the collected physiological and environmental data, enabling faster detection and response to potential threats.
BRIEF DESCRIPTION
[00022] In accordance with an embodiment of the present disclosure, a system to detect and prevent child abuse in real time. The system includes a wearable sensor device configured to continuously monitor physiological parameters of a child. The system also includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules.
The processing subsystem includes a control system (control device and control engine) operatively coupled to the wearable sensor device and configured to analyse the physiological and location signal data received from the wearable sensor device, to detect stress or abnormalities indicative of potential abuse. The control system includes a control device operatively coupled to the wearable sensor device and configured to receive the physiological and location signal data and execute preprocessing activities. The control system also includes a control engine operatively coupled to the control device and configured to analyse the physiological and location signal data received from the wearable sensor device, to detect stress or abnormalities indicative of potential abuse.
The processing subsystem also includes a location determination module operatively coupled to the control engine and configured to identify geographic location of the child based on one or more communication signals. The processing subsystem also includes a neighbourhood determination module coupled to the control engine and configured to identify the people around based on the nearest neighbourhood method. The processing subsystem also includes an anomaly detection engine operatively coupled to the control engine, location determination module and neighbourhood determination module, and comprising an artificial intelligence (AI) model, trained to detect abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time.
The processing subsystem also includes a notification engine configured to transmit alerts to pre-configured stakeholders in response to the detection of an anomaly, and to escalate the alert if no intervention is recorded within a predefined time threshold. The processing subsystem also includes a data storage module configured to store historical physiological, environmental, and alert data for further analysis and trend identification related to chronic stress or repeated incidents of abuse.
[00023] In accordance with another embodiment of the present disclosure, a method for detecting and preventing child abuse in real time. The method includes monitoring physiological parameters of a child via a wearable sensor device. The method also includes analysing the physiological data in real time by an anomaly detection engine using artificial intelligence technique for detecting stress or abnormal physiological conditions. The method also includes identifying the geographic location of the child using a location determination module integrated with the control engine. The method also includes identifying the nearest neighbourhood people of the child using a neighbourhood determination module integrated with the control engine. The method further includes detecting abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time. Furthermore, the method includes applying an AI-based technique for correlating detected stress or abnormal physiological conditions with environmental factors or proximity data. The method also includes generating a real-time alert based on detected anomalies and transmitting the alert to designated stakeholders through a notification engine. The method also includes escalating the alert if a response is not received within a predetermined time, allowing for follow-up action and tracking until resolution.
[00024] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[00025] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[00026] FIG. 1a is a block diagram representation of a system to detect and prevent child abuse in real time, in accordance with an embodiment of the present disclosure;
[00027] FIG. 1b is a block diagram representation of an exemplary embodiment of the system to detect and prevent abuse in a child centric environment in real time of FIG. 1a, in accordance with an embodiment of the present disclosure;
[00028] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and
[00029] FIG. 3 illustrates a flow chart representing the steps involved in a method for detecting and preventing abuse in a child centric environment in real time in accordance with an embodiment of the present disclosure.
[00030] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[00031] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[00032] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[00033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[00034] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise.
[00035] Embodiments of the present disclosure relate to the field of abuse detection, and more particularly, system and method for detection and prevention of child abuse in real-time , utilizing wearable devices, control devices, artificial intelligence (AI) technique to monitor physiological data, detect stress or abuse. The system includes a wearable sensor device configured to continuously monitor physiological parameters of a child. The system also includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes a control system operatively coupled to the wearable sensor device and configured to analyse the physiological data received from the wearable sensor device, to detect stress or abnormalities indicative of potential abuse. The processing subsystem also includes a location determination module operatively coupled to the control engine and configured to identify geographic location of the child based on one or more communication signals. The processing subsystem also includes a neighbourhood determination module operatively coupled to control engine and configured to identify the neighbourhood people of the child. The processing subsystem also includes an anomaly detection engine operatively coupled to control engine, the location determination module and neighbourhood determination module, and comprising an artificial intelligence (AI) model, trained to detect abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time. The processing subsystem also includes a notification engine configured to transmit alerts to pre-configured stakeholders in response to the detection of an anomaly, and to escalate the alert if no intervention is recorded within a predefined time threshold. The processing subsystem also includes a data storage module configured to store historical physiological, environmental, and alert data for further analysis and trend identification related to chronic stress or repeated incidents of abuse.
[00036] FIG. 1a is a block diagram representation of a system to detect and prevent abuse in a child centric environment in real time, in accordance with an embodiment of the present disclosure. The system (100) includes a wearable sensor device (110) configured to continuously monitor physiological parameters of a child. In one embodiment, the school can be a learning institute, child care centre , an orphanage or any other similar child centric organization. It should be noted that, for the sake of the description, the term 'school is being used, however, it should be inferred as child centric organization in the entire application.
[00037] The wearable sensor device (110) is designed to be worn comfortably by the child, typically on the wrist, as a band or similar form factor. It is equipped with various sensors that can measure and record critical physiological data such as heart rate, body temperature, skin conductivity, and other vital signs that are indicators of stress, fear, or anxiety, and other location parameters.
[00038] In one embodiment, one or more sensors within the wearable sensor device (110) may include Heart rate sensor which monitors the child's heart rate to detect abnormal increases or irregularities, which could be signs of stress or anxiety; body temperature sensor which tracks fluctuations in the child's body temperature, helping to identify potential stress, fear responses, or health abnormalities; galvanic skin response (GSR) sensor which measures skin conductivity, which varies with moisture levels on the skin, an important marker for detecting emotional stress or anxiety; motion sensor such as accelerometer and gyroscope which tracks the child's movements and activity levels, providing additional context to physiological changes, such as whether the child is stationary, agitated, or engaged in sudden movement.
[00039] These sensors work together to continuously monitor the child's physical state, collecting data throughout the day to provide a comprehensive understanding of their well-being. The sensors are designed to work independently as well as collectively, creating a more accurate picture of physiological changes that could be associated with abuse or stress.
[00040] The wearable sensor device (110) is designed to transmit this physiological data wirelessly, using communication mediums to send real-time readings to a nearest connected control device (120.1) of control system (120) for further processing and analysis. Importantly, the wearables designed to be energy-efficient, ensuring that it functions for extended periods without frequent charging. The device is also water-resistant, making it suitable for daily wear in various environments.
[00041] By integrating multiple physiological sensors, the wearable device (110) and control system (120) offers a non-invasive, real-time method for detecting signs of emotional or physical distress in a child, acting as a proactive tool for early intervention in cases of abuse or chronic stress.
[00042] The system (100) includes a processing subsystem (105) hosted on a server (108). In one embodiment, the server (108) may include a cloud-based server. In another embodiment, parts of the server (108) may be a local server coupled to a control device or user device (not shown in FIG.1a). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. In one example, the network (115) may be a private or public local area network (LAN) or Wide Area Network (WAN), such as the Internet. In another embodiment, the network (115) may include both wired and wireless communications according to one or more standards and/or via one or more transport mediums. In one example, the network (115) may include wireless communications according to one of the 802.11 or Bluetooth specification sets, or another standard or proprietary wireless communication protocol. In yet another embodiment, the network (115) may also include communications over a terrestrial cellular network, including, a global system for mobile communications (GSM), code division multiple access (CDMA), and/or enhanced data for global evolution (EDGE) network.
[00043] The processing subsystem (105) includes a control system (120) operatively coupled to the wearable sensor device (110). The control device (120.1) is configured to receive the physiological data and location signals from the wearables. The control engine (120.2) is configured to analyse the physiological data received from the wearable sensor device, to detect stress or abnormalities indicative of potential abuse. More specifically, once the data is transmitted wirelessly from the wearable sensor device (110), the control engine (120.2) analyses the various physiological parameters, such as heart rate, body temperature, skin conductivity, and motion data. In one embodiment, the control engine (120.2) is equipped with techniques designed to detect deviations from normal physiological patterns, which may indicate stress or abnormalities in the child's condition.
[00044] By continuously comparing the incoming data against predefined thresholds and baselines, the control engine is able to detect significant changes that could be indicative of emotional, physical, or sexual abuse. These thresholds may be set based on typical physiological ranges expected in children under non-stressful conditions. In one embodiment, such range may vary from one child to another, which can be generic range or specific range. In cases where the physiological data reflects sudden or sustained changes, such as an elevated heart rate or increased skin conductivity, the control engine interprets these variations as potential signs of distress. The control engine (120.2) also uses machine learning techniques to refine its ability to detect abnormalities by learning each child's unique physiological patterns over time, improving detection accuracy and minimizing false positives.
[00045] In the event of detecting potential stress or abuse, the control engine (120.2) triggers alerts that are sent to preconfigured stakeholders like school authorities, parents, guardians or any other relevant authorities for immediate intervention.. This real-time analysis and alert mechanism ensure that any concerning physiological changes are quickly identified and addressed, allowing for a proactive response to potential incidents of child abuse and tracked to closure.
[00046] The processing subsystem (105) also includes a location determination module (130) operatively coupled to the control engine (120.2). The location determination module (130) is configured to identify geographic location of the child based on one or more communication signals. More specifically, the location determination module (130) continuously tracks the child's movements and determines their real-time location in campus, whether they are indoors or outdoors. By analysing the communication signals from surrounding control devices, the location determination module (130) accurately pinpoints the child's whereabouts.
[00047] The processing subsystem (105) also includes a neighbourhood determination module (135) operatively coupled to the control engine (120.2). The neighbourhood determination module (135) is configured to identify the neighbourhood people of the child, based on nearest neighbourhood method. More specifically, the neighbourhood determination module (135) tracks the people around the child in real-time.
[00048] The location data and neighbourhood data is processed alongside the physiological data from the wearable sensor device (110). This allows the system (100) to provide contextual information about the child's environment, which is critical in assessing potential risk situations. For example, if the child's physiological data indicates elevated stress levels and the location determination module identifies the child in a secluded or high-risk area, such as a restricted zone within a school, the system (100) can trigger immediate alerts for faster intervention. Additionally, the location determination module (130) can identify proximity to certain individuals or environments, helping the system (100) correlate the geographic context with potential causes of stress or danger. This real-time location tracking, combined with physiological data analysis, enhances the system's ability to proactively detect and respond to incidents of child abuse or distress.
[00049] The processing subsystem (105) also includes an anomaly detection engine (140) operatively coupled to the control engine (120.2), location determination module (130) and neighbourhood determination module(135). The anomaly detection engine (140) includes an artificial intelligence (AI) model which is trained to detect abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time. In one embodiment, the AI model may be designed to continuously analyse the physiological data, such as heart rate, skin conductivity, and body temperature, alongside the location and contextual information provided by the location determination module and neighbourhood module
[00050] By learning and recognizing normal behavioural and physiological patterns for each child, the AI model can accurately detect deviations that signal potential distress or abusive conditions. For example, the AI engine is capable of distinguishing between typical variations in physiological data caused by daily activities and those that may indicate heightened stress or fear, particularly when these anomalies coincide with certain locations or proximity to specific individuals. The engine is also capable of processing large volumes of data in real-time, allowing it to detect signs of chronic or long-term stress, which may not be immediately visible.
[00051] As the anomaly detection engine (140) identifies patterns indicative of abuse, it triggers alerts that are relayed to the appropriate stakeholders. These alerts include detailed information on the physiological and environmental factors contributing to the detected anomaly, enabling quick and informed intervention. The AI model continually refines its detection accuracy through machine learning, using historical data to improve its understanding of each child's unique patterns and their responses to various stimuli, thereby enhancing the system's ability to detect abuse early and reduce false alarms.
[00052] Furthermore, the processing subsystem (105) includes a notification engine (150) configured to transmit alerts to pre-configured stakeholders in response to the detection of an anomaly. The notification engine is also configured to escalate the alert if no intervention is recorded within a predefined time threshold. More specifically, the notification engine (150) is configured to send these alerts in real time to designated individuals such as school officials, parents, guardians or any other relevant authorities. Upon detecting an anomaly in the child's physiological data or environment, the notification engine (150) ensures that the relevant parties are notified immediately through various communication channels, including mobile/web applications, SMS, email or automatic voice call.
[00053] In addition to sending the initial alert, the notification engine (150) is also designed with an escalation mechanism. If no intervention or response is recorded from the notified stakeholders within a predefined time threshold, the system (100) automatically escalates the alert to higher authorities or additional stakeholders to ensure that the situation is addressed promptly. This escalation process is crucial in cases where immediate action is required, such as when the child is in a potentially dangerous situation or experiencing severe stress. The notification engine (150) ensures that the alerts contain comprehensive information, including the child's physiological status, location, and the nature of the detected anomaly, enabling stakeholders to make informed decisions quickly. By incorporating both alert transmission and escalation functionalities, the notification engine (150) plays a vital role in ensuring timely intervention in response to detected signs of abuse or distress.
[00054] The processing subsystem (105) includes a data storage module (160) configured to store historical physiological, environmental, and alert data for further analysis and trend identification related to chronic stress or repeated incidents of abuse. This module (160) securely stores and archives all the data captured by the wearable sensor device and processed by the control engine, including heart rate, body temperature, skin conductivity, location, neighbourhood and any alerts generated by the system. By maintaining a comprehensive record of these data points over time, the data storage module (160) enables detailed analysis of long-term trends, which can be crucial in identifying patterns related to chronic stress or repeated incidents of abuse.
[00055] The stored data can be accessed for further analysis by authorized stakeholders, allowing them to review previous anomalies, locations, and contextual factors that contributed to the detection of potential abuse. This historical data is particularly valuable in assessing whether a child has been exposed to recurring high-stress situations or abusive environments. Additionally, the data storage module (160) supports the anomaly detection engine by providing historical data that can be used to refine the artificial intelligence model, helping it improve the accuracy of future anomaly detection. This stored data also assists in post-incident investigations, providing a clear timeline and pattern of events leading up to alerts, which can support intervention efforts and inform ongoing monitoring strategies.
[00056] The operation of the invention begins with the wearable sensor device (110) continuously monitoring the child's physiological parameters, including heart rate, body temperature, skin conductivity, and motion. The data collected by these sensors is wirelessly transmitted to the processing subsystem (105), where the control system (120) receives and processes the physiological information in real time. Simultaneously, the location determination module (130) identifies the geographic location of the child by tracking communication signals and sends this information to the control engine (120.2). and neighbourhood determination module (135) identifies the neighbourhood people of the child and sends this information to the control engine (120.2)
[00057] The control engine (120.2) analyses the incoming data, and the anomaly detection engine (140), equipped with an AI model, compares the physiological and environmental data against predefined thresholds and learned historical patterns. The AI model is trained to detect abnormal physiological responses, such as elevated stress indicators or unusual activity, especially when combined with specific locations or proximity to certain individuals, signalling high-risk situations. If an anomaly is detected, such as a sudden spike in stress levels or irregular behaviour based on the data, the system recognizes it as a potential sign of abuse or distress. More specifically, the term 'sudden spike is defined as stress spike which is continual for a configured period of time, and not point spikes.
[00058] Once an anomaly is identified, the notification engine (150) transmits an alert to pre-configured stakeholders, such as parents, teachers, or authorities, via mobile applications, SMS, email or automatic voice call. This alert contains detailed information about the child's physiological condition, location, and the nature of the detected anomaly. If no response or intervention is recorded within a predefined time threshold, the system escalates the alert to higher authorities or additional stakeholders, ensuring timely intervention.
[00059] Meanwhile, the data storage module (160) continuously stores all the physiological, environmental, and alert data. This archived information is used for further analysis to identify trends, such as chronic stress or repeated incidents of abuse. Additionally, the historical data feeds into the AI model within the anomaly detection engine (140), allowing it to refine its accuracy in detecting future anomalies and improving the system's overall efficiency in protecting children from abuse.
[00060] FIG. 1b is a block diagram representation of an exemplary embodiment of the system to detect and prevent child abuse in real time of FIG. 1a, in accordance with an embodiment of the present disclosure
[00061] Consider a scenario involving a child named R (125), who is wearing a wearable device (110) designed to monitor R's physiological parameters throughout the school day. One afternoon during physical education class, control engine (120.2) detects a significant increase in R's heart rate and a rise in skin conductivity, both of which are indicators of acute stress. Simultaneously, the location determination module (130) indicates that R (125) is near the locker room, a location that is typically unsupervised and has been previously identified as a potential hotspot for bullying and neighbourhood determination module (135) identifies the neighbourhood people of the child at the time.
[00062] As the control engine (120.2) processes this incoming data, the anomaly detection engine (140) analyses the physiological signals against R's historical baseline data. The AI model recognizes that R's current heart rate is significantly higher than R's normal range for that time of day, especially given that he has been at rest after physical activity. The combination of elevated stress levels and the fact that R is in a secluded area and/or people around raises a red flag within the system (100), suggesting that R (125) may be in a situation of abuse, possibly being bullied by classmates.
[00063] In response to this detection, the notification engine (150) sends an immediate alert to R's authorities, detailing the spike in R's stress levels and R's current location and/or people around. The alert includes information about the physiological anomalies detected, prompting school authorities to check on R (125) in the locker room area. An escalation process will be configured first, which can be the school staff and their authority levels who can act immediately; and finally it to parents. This is to avoid the panic at the parents end.
[00064] If no response or intervention occurs within the predefined time frame, the system (100) automatically escalates the alert to higher authorities, such as school counsellors or child protection services, ensuring that urgent action is taken to protect R (125).
[00065] Meanwhile, the data storage module (160) records this incident, along with R's physiological data and location, allowing for future analysis. Over time, this historical data can reveal patterns that may indicate chronic stress related to repeated bullying incidents, prompting proactive measures to ensure R's safety and well-being in school. By leveraging real-time monitoring, alert systems, and data analysis, the system (100) aims to intervene early in potential abuse situations, providing a safeguard for children like R (125).
[00066] FIG. 2 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[00067] The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIGs. 1a and 1b. The memory (210) includes a processing subsystem (105) of FIG.1. The processing subsystem (105) further has following modules: a control system (120), a control engine (120.2), a location determination module (130), neighbourhood determination module (135), an anomaly detection engine (140), a notification engine (150), and a data storage module (160).
[00068] The control engine (120.2) is configured to analyse the physiological data received from the wearable sensor device, to detect stress or abnormalities indicative of potential abuse. The location determination module (130) is configured to identify geographic location of the child based on one or more communication signals. The neighbourhood determination module (135) is configured to identify nearest neighbourhood people around the child . The anomaly detection engine (140) includes an artificial intelligence (AI) model which is trained to detect abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time. The notification engine (150) configured to transmit alerts to pre-configured stakeholders in response to the detection of an anomaly. The data storage module (160) configured to store historical physiological, environmental, and alert data for further analysis and trend identification related to chronic stress or repeated incidents of abuse.
[00069] The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.
[00070] FIG. 3 illustrates a flow chart representing the steps involved in a method for detecting and preventing child abuse in real time in accordance with an embodiment of the present disclosure. The method (300) includes monitoring physiological parameters of a child via a wearable sensor device in step 310. More specifically, the method (300) begins with the continuous monitoring of a child's physiological parameters through a wearable sensor device. The device is equipped with sensors that track various indicators, such as heart rate, skin conductivity, body temperature, and movement. These physiological parameters are recorded in real time as the child goes about their day.. The data collected by the sensors is then transmitted wirelessly to a processing subsystem for further analysis, ensuring continuous and unobtrusive monitoring of the child's well-being.
[00071] The method (300) also includes analysing the physiological data in real time using real-time analytics for detecting stress or abnormal physiological conditions in step 320. More specifically, as the data is received, processes it by comparing the current physiological readings, such as heart rate, body temperature, and skin conductivity, against pre-established norms and historical patterns. The real-time analytics identify the signs of stress or abnormal physiological conditions that may indicate distress or abuse. When an anomaly, such as elevated stress levels, is detected, the system flags it for further action
[00072] The method (300) also includes identifying the geographic location of the child using a location determination module integrated with the control engine in step 330. More specifically, as the wearable sensor device continuously monitors physiological data, the location determination module tracks the child's real-time position by utilizing communication signals. This location data is processed alongside the physiological readings by the control engine, allowing the system to correlate the child's physical whereabouts with any detected stress or abnormal conditions. By identifying the child's location, the system provides context for potential high-risk or isolated environments, aiding in the timely detection of abuse or distress.
[00073] The method (300) also includes identifying the neighbourhood people of the child using a neighbourhood determination module integrated with the control engine in step 340. More specifically, as the wearable sensor device continuously monitors physiological data, the neighbourhood determination module tracks the child's nearest neighbourhood people. This neighbourhood people data is processed alongside the physiological readings by the control engine, allowing the system to correlate the child's neighbourhood with any detected stress or abnormal conditions. By identifying the child's neighbourhood people, the system provides context for potential high-risk, aiding in the timely detection of abuse or distress.
[00074] Furthermore, the method (300) includes detecting abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time in step 350. More specifically, As the wearable sensor device collects data on the child's physiological parameters, the anomaly detection engine, powered by artificial intelligence, continuously analyses this information. It monitors for unusual patterns, such as elevated heart rate or skin conductivity, which may indicate stress or distress. Simultaneously, the system evaluates environmental factors, including the child's location and proximity to certain individuals(neighbourhood people) or high-risk areas. By correlating these physiological and environmental cues, the system identifies situations that could be abusive or harmful, enabling immediate intervention in real-time.
[00075] The method (300) also includes applying an AI-based technique for correlating detected stress or abnormal physiological conditions with environmental factors or proximity data in step 360. More specifically, as the wearable sensor device monitors physiological indicators like heart rate, skin conductivity, and body temperature, the AI model analyses these in conjunction with the child's environmental context, such as their geographic location and proximity to individuals or areas identified as potentially risky. The AI algorithm identifies patterns linking stress indicators with specific locations or people, allowing the system to detect situations where environmental factors may be contributing to the child's distress. This correlation enhances the system's ability to identify abusive or harmful scenarios with greater accuracy.
[00076] The method (300) further includes generating a real-time alert based on detected anomalies and transmitting the alert to designated stakeholders through a notification engine in step 370. More specifically, When the system identifies abnormal stress levels or distress patterns through its AI-based analysis, an immediate alert is triggered. This alert contains relevant details, such as the child's physiological condition, current location, neighbourhood and the nature of the detected anomaly. The notification engine then transmits this alert to designated stakeholders, such as parents, school authorities, or emergency contacts, via Web/mobile applications, SMS, email, or automatic voice call, ensuring timely intervention to prevent or address potential abuse or harm.
[00077] Also, the method (300) includes escalating the alert if a response is not received within a predetermined time, allowing for follow-up action and tracking until resolution in step 380. More specifically, once an anomaly is detected and an initial alert is sent, the system monitors for a response or intervention. If no action is recorded within the set time frame, the alert is automatically escalated to additional stakeholders or higher authorities, such as senior school officials, parents and other relevant authorities . This escalation ensures that follow-up action is taken promptly, and the situation is tracked until it is resolved, enhancing the system's ability to protect the child from ongoing or repeated abuse.
[00078] Various embodiments of the system and method for real-time child abuse detection, utilizing wearable devices offers several key advantages in enhancing child safety and preventing abuse using a wearable sensor device to continuously monitor physiological parameters, it enables real-time detection of stress or distress in children, allowing for immediate intervention before situations escalate. The integration of an AI-based anomaly detection engine improves accuracy by correlating physiological data with environmental factors, such as location and proximity to certain individuals(neighbourhood people), ensuring a comprehensive assessment of potentially harmful situations. Additionally, the system's ability to generate and transmit real-time alerts to designated stakeholders ensures timely responses, while the automatic escalation feature guarantees that no alert goes unnoticed, providing an additional layer of protection. The system's data storage module also enables long-term trend analysis, helping identify chronic stress or repeated incidents of abuse. Overall, this invention offers a proactive, data-driven approach to child safety, reducing the reliance on reactive measures and providing a safeguard against various forms of abuse.
[00079] Another benefit is the ability to customize alerts and designate specific stakeholders based on individual and organization preferences, ensuring that the right people are notified during critical situations. The escalation feature creates a multi-layered safety net, providing an additional layer of security for the child by automatically escalating alerts if initial contacts do not respond. Additionally, the system can integrate with existing school/organization safety protocols, enhancing current measures without the need for a complete overhaul of established practices.
[00080] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
[00081] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[00082] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. , Claims:1. A system (100) to detect and prevent abuse in real time in a child centric environment, comprising:
a wearable sensor device (110) configured to continuously monitor physiological parameters of a child in the child centric environment;
a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules comprising:
a control system (120) operatively coupled to the wearable sensor device (110), and configured to analyse the physiological data received from the wearable sensor device (110), to detect stress or abnormalities indicative of potential abuse;
a location determination module (130) operatively coupled to a control engine (120.2), wherein the control engine (120.2) is a part of the control system (120), wherein the location determination module (130) is configured to identify geographic location of the child based on one or more communication signals;
a neighbourhood determination module (135) operatively coupled to the control engine (120.2), and is configured to identify one or more neighbourhood people of the child based on nearest neighbourhood method
an anomaly detection engine (140) operatively couped to the control engine (120.2), location determination module (130), neighbourhood determination module (135) and comprising an artificial intelligence (AI) model, trained to detect abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time;
a notification engine (150) configured to transmit alerts to pre-configured stakeholders in response to the detection of an anomaly; escalate the alert if no intervention is recorded within a predefined time threshold; and
a data storage module (160) configured to store historical physiological, environmental, and alert data for further analysis and trend identification related to chronic stress or repeated incidents of abuse.
2. The system (100) as claimed in claim 1, wherein the physiological parameters comprise of heart rate and at least one of other parameters such as body temperature, skin conductivity or a combination thereof.
3. The system (100) as claimed in claim 1, wherein the wearable sensor device (110) is configured to detect motion data and using an accelerometer and gyroscope, to provide input to the control system (120) for detecting physical abuse.
4. The system (100) as claimed in claim 1, wherein the control engine (120.2) is configured to continuously compare the physiological data with a baseline profile of the child, wherein the baseline being dynamically updated over time.
5. The system (100) as claimed in claim 1, wherein the location determination module (130) is further configured to calculate the child's proximity to specific geofenced areas within a school, , or playground, and trigger location-specific alerts in the event of anomaly detection.
6. A method (300) for detecting and preventing abuse in a child centric environment in real time, comprising:
monitoring physiological parameters of a child via a wearable sensor device; (310)
analysing the physiological data in real time by an anomaly detection engine using artificial intelligence technique for detecting stress or abnormal physiological conditions; (320)
identifying the geographic location of the child using a location determination module integrated with the control engine; (330)
identifying the neighbourhood people of the child using a neighbourhood determination module integrated with the control engine; (340)
detecting abnormal physiological patterns and environmental factors that correlate with abusive or high-stress situations in real-time; (350)
applying an AI-based technique for correlating detected stress or abnormal physiological conditions with environmental factors or proximity data; (360)
generating a real-time alert based on detected anomalies and transmitting the alert to designated stakeholders through a notification engine; and (370)
escalating the alert if a response is not received within a predetermined time, allowing for follow-up action and tracking until resolution. (380)
7. The method (300) as claimed in claim 6, comprising prioritizing alerts based on severity of the detected anomaly, with high-severity alerts generating immediate notifications to authorities and low-severity alerts generating notifications to corresponding one or more people.
8. The method (300) as claimed in claim 6, comprising analysing historical physiological data for detecting patterns of chronic stress in the child,
9. The method (300) as claimed in claim 6, comprising identifying trends indicative of long-term abuse or emotional distress by the anomaly detection engine.
10. The method (300) as claimed in claim 6, wherein the real-time alert comprises contextual data such as the child's location, detected proximity of individuals, and the specific physiological or environmental anomaly detected, or a combination thereof providing the authorities with actionable information for intervention.


Dated this 28th day of October 2024

Signature

Jinsu Abraham
Patent Agent (IN/PA-3267)
Agent for the Applicant

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