SYSTEM FOR HAND GESTURE-BASED HELP DETECTION AND RESCUE USING DEEP LEARNING AND METHOD THEREOF

Abstract

The present invention discloses a system and method for hand gesture-based help detection and rescue using deep learning and IoT integration. The system comprises a gesture detection camera, motion sensors, a microcontroller for real-time data processing, and a deep learning model for recognizing distress-indicating hand gestures. Upon detecting a distress gesture, the system triggers an alert through an IoT communication module, transmitting relevant data (gesture type, location, and timestamp) to emergency responders for immediate action. The system operates efficiently in diverse environments, leveraging AI to recognize gestures under varying conditions. It ensures rapid response, offering a reliable solution for emergency situations where verbal communication is not possible. The modular design allows scalability, and the use of IoT enables real-time communication with responders. This invention improves rescue operations by providing accurate, real-time distress detection, enhancing response times and minimizing delays in emergency interventions.

Specification

Description:[016] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered 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 spirit, and scope of the present disclosure as defined by the appended claims.
[017] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[018] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[019] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[020] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[021] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[022] In an embodiment of the invention and referring to Figures 1, the present invention relates to a system and method for detecting emergency or distress situations through hand gestures, specifically for use in rescue and assistance operations. The system incorporates deep learning techniques for gesture recognition, integrated with Internet of Things (IoT) devices to enable real-time communication and response. The invention offers a novel solution for providing rapid and reliable assistance to individuals in peril using an advanced fusion of AI, deep learning, and IoT technologies.
[023] In emergency situations, individuals often find themselves unable to communicate verbally, either due to injury, shock, or other constraints. The ability to convey a distress signal through hand gestures provides a means for such individuals to alert responders. Traditional methods rely on visual cues or manual reporting systems, which can be ineffective or delayed in critical situations. This invention aims to overcome these limitations by using AI-driven systems to automatically detect hand gestures associated with distress or help requests, triggering timely responses for rescue and assistance.
[024] The invention provides an integrated system comprising specialized hardware and advanced software components, working together to detect hand gestures that signal distress or the need for help. The system leverages deep learning algorithms to interpret hand gestures accurately and connects to IoT devices that allow for immediate communication with rescue teams. The invention involves sensors for real-time data collection, AI models for gesture recognition, and a communication interface for transmitting alerts to emergency responders, ensuring swift rescue operations.
[025] The system comprises several hardware components, each playing a crucial role in the detection, processing, and communication of hand gestures. These components include:
[026] Gesture Detection Camera: A high-definition camera, integrated with depth sensors (such as LiDAR), captures real-time hand gestures. The camera system is designed to operate under various lighting conditions and environmental factors to ensure accurate gesture recognition in diverse settings.
[027] Microcontroller/Processor Unit: The processing unit is responsible for collecting data from the camera system, running pre-trained deep learning models, and making decisions regarding whether a gesture is a distress signal. It manages real-time processing and communication between components. Examples include embedded processors like ARM Cortex or NVIDIA Jetson for AI applications.
[028] Motion Sensors: Additional sensors, including accelerometers and gyroscopes, are used to track hand movement and orientation in 3D space. These sensors provide enhanced gesture recognition capabilities, especially in dynamic environments where the user's hand movement is not confined to a static position.
[029] Communication Interface: IoT-based communication modules (such as Wi-Fi, Zigbee, or Bluetooth) are incorporated into the system to transmit data to emergency responders or rescue teams. These modules ensure low-latency, real-time alerts, enabling swift intervention.
[030] The heart of the system is a deep learning model trained to recognize specific hand gestures associated with distress or emergency. The model processes the images or data obtained from the camera and sensor system to detect relevant hand movements. A convolutional neural network (CNN) or a recurrent neural network (RNN) can be employed for image and time-series data analysis, respectively. The deep learning architecture is optimized to handle variations in hand position, speed, and environmental conditions.
[031] The gesture recognition model is trained using a large dataset comprising various hand gestures in different environmental settings. This dataset includes both controlled gesture images and real-world emergency gesture samples, ensuring robustness. The dataset is augmented with variations in lighting, background noise, and hand positions to ensure that the system performs well under diverse conditions. The model is continually updated and fine-tuned to improve accuracy and reduce false positives.
[032] The integration of deep learning models with IoT devices is a critical feature of the invention. Once a distress gesture is recognized, the system utilizes IoT communication protocols to send alerts to connected rescue teams, emergency services, or monitoring stations. The IoT module communicates data such as the type of gesture, timestamp, and location, ensuring that help is directed to the individual in need as soon as possible.
[033] The system processes data in real time, allowing for immediate detection and response. This includes constant monitoring of hand gestures via the camera and sensors, followed by immediate processing by the AI model to classify the gesture. If the gesture is classified as a distress signal, the system triggers an alert through the IoT communication network to the relevant responders. The speed and reliability of this process are crucial in ensuring effective rescue operations.
[034] To ensure the accuracy and relevance of gesture detection, the system performs both classification and validation of gestures. When a potential distress gesture is detected, the system cross-references the gesture with a predefined set of emergency hand signals stored in its database. This validation step reduces the possibility of misclassification, ensuring that only genuine distress signals result in action.
[035] The gesture detection camera, motion sensors, microcontroller, and communication interface are all interconnected within a unified system architecture. The camera and sensors feed data to the microcontroller, which processes the information in real time. The AI model, running on the processor unit, classifies the gesture, triggering a response. The communication interface then sends the alert to responders. This integration is facilitated by a custom-designed communication protocol that minimizes latency and ensures reliable performance in emergency situations.
[036] The seamless interaction of hardware and software components ensures that the system performs with high accuracy and reliability. The deep learning model's robust training enables it to detect a wide range of hand gestures, including those made in rapid or complex motions. By incorporating IoT devices, the system not only detects gestures but also enables immediate action, ensuring that the time between gesture detection and help arrival is minimized.
[037] The deep learning model employed in this invention is specifically designed for gesture recognition tasks. Through supervised learning, the model is trained on labeled data (hand gestures associated with distress or emergencies) and can accurately distinguish between normal hand movements and those that indicate a need for help. The AI component continuously improves through retraining on new data, thereby enhancing its performance over time.
[038] The IoT capabilities of the system extend the utility of the gesture recognition system to real-world emergency scenarios. Upon detecting a distress signal, the system sends a detailed alert, including the specific gesture, location (obtained via GPS), and other relevant contextual data to emergency responders. This ensures that the response teams can prioritize the situation and provide the necessary assistance efficiently.
[039] The coordination between the hardware and software components is optimized for real-time performance. The sensors and camera work in tandem to capture precise data about the user's hand movements, which is processed immediately by the AI model. The microcontroller synchronizes these hardware components to ensure smooth data flow and minimizes latency. The communication interface is designed to handle large amounts of data from multiple sources, ensuring rapid transmission of alerts to the relevant authorities.
[040] Considering that the system may be deployed in remote or resource-limited environments, energy efficiency is a key consideration. The hardware components are optimized for low power consumption, and the deep learning models are compressed to reduce computational load, making the system suitable for long-duration usage without frequent recharging or battery replacement.
[041] The system is designed to be scalable, allowing for deployment in various environments, from small-scale residential applications to large-scale public spaces like airports or stadiums. The modular architecture enables the addition of more cameras, sensors, and IoT devices to cover larger areas, enhancing the overall efficiency and responsiveness of the system.
[042] The system undergoes rigorous testing in simulated emergency scenarios to ensure its reliability and performance. These tests include various hand gesture types, environmental conditions (e.g., lighting, noise), and user variations. The deep learning model's accuracy is measured in terms of both detection rate and false-positive rate to ensure that it performs well across different real-world conditions.
[043] The system offers significant improvements over traditional emergency detection systems. Unlike systems based on audio or voice recognition, this gesture-based system is highly effective in situations where verbal communication is not possible. The integration of AI and IoT further enhances the system's capability to detect distress signals accurately and respond in real-time, ensuring faster and more efficient rescue operations.
[044] The system employs stringent privacy and security measures to protect user data. The communication between devices is encrypted, ensuring that sensitive information such as location and personal data is kept secure. The system also operates in compliance with relevant data protection regulations, ensuring that it meets the required privacy standards.
[045] The system finds applications in various fields such as healthcare, disaster response, law enforcement, and public safety. It can be used in hospitals to assist patients who are unable to communicate, in public spaces for crowd control and emergency detection, and in remote or hazardous environments where human presence is limited.
[046] A user interface (UI) is provided for monitoring the system's operation and controlling various components. The UI allows responders to view incoming alerts, track the location of the distressed individual, and manage the system's settings. It can be accessed via a mobile app or a dedicated control panel, ensuring ease of use during emergency situations.
[047] The invention is designed to be adaptable to future technological advancements. For example, as AI models evolve, the system can be updated with new algorithms to improve accuracy. Additionally, new sensor technologies or communication protocols can be integrated to further enhance the system's capabilities.
[048] The system can be integrated with other rescue or emergency management systems, such as automated first-aid kits, drones, or robotic rescue devices. This integration allows for a coordinated and efficient response, leveraging the strengths of multiple technologies to address complex rescue scenarios.
[049] By utilizing off-the-shelf hardware components and open-source software tools for deep learning, the system offers a cost-effective solution for hand gesture-based distress detection. The modular nature of the system allows for gradual scaling and customization based on specific needs and budgets.
[050] The proposed system for hand gesture-based help detection and rescue using deep learning and IoT integration provides an innovative, efficient, and reliable solution for emergency response. Its novel combination of hardware and software components, including AI and IoT technologies, offers significant improvements in the speed and accuracy of distress detection and response. The system's scalability, privacy considerations, and ease of integration with other rescue systems make it a valuable tool for various real-world applications. , Claims:1. A system for hand gesture-based help detection and rescue using deep learning, comprising:
a) a gesture detection camera for capturing real-time images of a user's hand movements;
b) motion sensors configured to track hand orientation and movement in three-dimensional space;
c) a microcontroller or processor unit that processes the data from the camera and motion sensors;
d) a deep learning model for recognizing specific hand gestures indicative of distress or emergency;
e) an IoT communication module to transmit distress signals to emergency responders, including gesture type, location, and time;
wherein the system performs real-time gesture recognition and triggers immediate response to alert rescue teams based on identified gestures.
2. A method for hand gesture-based help detection and rescue using deep learning, comprising the steps of:
i. capturing images of hand movements using a gesture detection camera integrated with depth sensors;
ii. collecting data on hand orientation and movement from motion sensors;
iii. processing the captured data using a microcontroller or processor unit;
iv. analyzing the processed data with a deep learning model to identify gestures indicating distress or the need for help;
v. transmitting an alert through an IoT communication module to emergency responders with relevant data including gesture type, location, and time.
3. The system as claimed in claim 1, wherein the deep learning model is a convolutional neural network (CNN) or recurrent neural network (RNN), trained using a dataset of labeled hand gestures in various environmental settings.
4. The system as claimed in claim 1, further includes an interface for monitoring and controlling the system's operation, wherein responders can view incoming alerts and manage settings via a mobile application or a control panel.
5. The system as claimed in claim 1, wherein the gesture detection camera includes a high-definition camera combined with depth sensors for operating in various lighting and environmental conditions.
6. The system as claimed in claim 1, wherein the IoT communication module transmits data over wireless communication protocols selected from the group consisting of Wi-Fi, Zigbee, and Bluetooth to enable real-time communication with emergency responders.
7. The method as claimed in claim 2, further includes the step of cross-referencing identified gestures with a predefined database of emergency hand signals to validate the gesture before triggering the alert.
8. The system as claimed in claim 1, wherein the motion sensors include accelerometers and gyroscopes that track hand movement in three-dimensional space, improving the accuracy of gesture detection.
9. The system as claimed in claim 1, wherein the microcontroller or processor unit is selected from the group consisting of ARM Cortex processors and NVIDIA Jetson embedded processors for running the deep learning model and controlling real-time data processing.
10. The system as claimed in claim 1, wherein the system is scalable and modular, allowing additional cameras, sensors, and IoT devices to be added for larger area coverage without compromising system performance.

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