A SYSTEM AND METHOD FOR BROWSER CONTROL WITH HAND GESTURE

Abstract

The invention presents a hand gesture recognition system for controlling web browsers using real-time gesture recognition. Utilizing MediaPipe Hands, a neural network model implemented in TensorFlow.js, the system accurately detects hand gestures and interprets them through a mathematical model. Delivered as a Chrome extension, it allows users to customize gestures for various website actions, such as scrolling and clicking. This innovative approach ensures efficient, secure, and privacy-conscious interactions, effectively overcoming the limitations of existing gesture control applications. By prioritizing user configurability and accessibility, the invention enhances the browsing experience, making it more intuitive and user-friendly.

Specification

Description:[0001] The present invention pertains to the domain of human-computer interaction (HCI), specifically focusing on systems and methods that enable users to control web browsers and web-based applications through intuitive hand gestures. This invention falls within the broader fields of computer science, machine learning, and user interface design, aiming to enhance the accessibility and efficiency of web interactions.

Background of the Invention
[0002] Gesture-based recognition systems have garnered significant attention as a means of improving human-computer interaction by offering more natural and intuitive ways for users to engage with digital devices. Traditional input methods, such as keyboards and mice, may not always provide an optimal experience, particularly for individuals with disabilities or those in environments where conventional input devices are impractical.
[0003] However, existing applications that utilize hand gestures to control web browsers face several inherent challenges. First, there are substantial privacy concerns, as many systems require the continuous monitoring of users' gestures and movements, potentially leading to unauthorized data collection. Second, high bandwidth requirements can impede the performance of gesture recognition systems, particularly in environments with limited internet connectivity. Third, many gesture recognition systems are often dependent on specific hardware configurations, limiting their accessibility across different devices. Finally, users frequently encounter limited configurability, restricting their ability to personalize gestures according to their preferences.
[0004] Given these issues, there is a pressing need for an innovative solution that enables secure, efficient, and customizable hand gesture control within web browsers, thereby enhancing user experience and interaction.

Objects of the Invention
[0005] An object of the present invention is to create an effective hand gesture recognition system that operates seamlessly within web browsers, allowing users to interact with web content without relying on traditional input devices.
[0006] Another object of the present invention is to achieve high accuracy in real-time gesture recognition to provide users with reliable control over web actions, thereby enhancing the effectiveness of the system.
[0007] Yet another object of the present invention is to design the system to process gesture data locally within the user's browser, ensuring that sensitive information is not transmitted to external servers, thereby addressing privacy concerns.
[0008] Another object of the present invention is to enable users to configure and customize gestures according to their preferences, allowing for a tailored user experience that adapts to individual needs and workflows.
[0009] Another object of the present invention is to ensure that the system is easily accessible and portable across different devices and platforms, promoting inclusivity for users with varying abilities and contexts.
[0010] Another object of the present invention is to minimize the bandwidth and computational requirements of the system, ensuring smooth operation in a variety of network conditions.

Summary of the Invention
[0011] The present invention introduces a novel hand gesture recognition system that operates entirely within web browsers, utilizing advanced JavaScript deep learning models to facilitate real-time interaction. The core of this system is based on MediaPipe Hands, a cutting-edge neural network model developed using TensorFlow.js. This model is designed to accurately detect hand gestures by identifying key landmarks on the user's hand in real-time.
[0012] The implementation of the system is achieved through a user-friendly Chrome extension, which simplifies the installation process and provides an intuitive interface for users. Within this extension, users have the ability to configure custom gestures that can be assigned to various actions on websites, such as scrolling, clicking buttons, or navigating between pages.
[0013] By utilizing this approach, the invention aims to provide a secure and efficient method of interacting with websites using hand gestures. It effectively overcomes the limitations posed by existing systems, such as privacy concerns, high bandwidth demands, and lack of configurability, thus setting a new standard for gesture-based control in web environments.
[0014] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0015] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.

Detailed Description of the Invention
[0016] An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
[0017] The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.

[0018] The proposed hand gesture recognition system is designed to facilitate intuitive control of web browsers through the use of hand gestures. This system consists of multiple integrated components that work in tandem to provide a seamless user experience. Below is a detailed breakdown of each component and its functionality:
1. Gesture Recognition Model:
[0019] Core Technology: The system is built around the MediaPipe Hands model, which utilizes state-of-the-art computer vision techniques for hand tracking. MediaPipe is a cross-platform framework developed by Google that allows real-time detection and tracking of hand landmarks through a neural network.
[0020] Real-Time Detection: The model operates in real-time, meaning it can continuously monitor the user's hand movements without noticeable latency. This is achieved through optimized algorithms that process video frames captured by the user's webcam.
[0021] Key Landmarks: The model identifies critical landmarks on the hand, such as the tips of the fingers, the base of the palm, and joint angles. These landmarks provide a comprehensive representation of the hand's position and orientation, enabling accurate gesture recognition.
2. Mathematical Gesture Interpretation:
[0022] Data Processing: Once hand landmarks are detected, the system employs mathematical algorithms to analyze the spatial relationships between these landmarks. By calculating distances, angles, and trajectories, the system can interpret specific hand gestures.
[0023] Gesture Library: The system comes pre-loaded with a library of common gestures, such as swipes, taps, and pinches, each corresponding to different actions within a web browser. For example, a swipe gesture might be mapped to scrolling down a webpage, while a pinch gesture could be interpreted as zooming in or out.
[0024] Custom Gesture Creation: Users can define custom gestures by recording their movements and associating them with specific actions. This feature allows for personalized interaction, enabling users to create shortcuts that streamline their browsing experience.
3. Browser Extension:
[0025] User-Friendly Interface: The gesture recognition system is packaged as a Chrome extension, providing a straightforward installation process. Upon installation, users can easily access the extension's interface directly from their browser toolbar.
[0026] Configuration Dashboard: The extension includes a configuration dashboard where users can view their gesture settings, adjust sensitivity levels, and customize actions associated with each gesture. This dashboard is designed to be intuitive, allowing users to modify settings with minimal effort.
[0027] Action Mapping: Users can map gestures to a wide range of actions, including:
Scrolling: Users can scroll up or down a webpage by performing a simple upward or downward swipe gesture.
Navigation: Swiping left or right can be mapped to moving back or forward in the browser history.
Interactions: Users can tap their fingers to perform clicks on hyperlinks or buttons, similar to using a mouse.
4. Privacy and Security Features:
[0028] Local Processing: The system is designed with user privacy as a top priority. All gesture recognition and processing occur locally within the user's browser, ensuring that sensitive data is not transmitted to external servers. This local processing model minimizes the risk of data breaches and unauthorized tracking.
[0029] Data Anonymization: Even if gesture data were to be collected for the purpose of improving the system, measures would be in place to anonymize this data, preventing any association with individual users.
[0030] User Control: Users have complete control over the data collected by the system. They can choose to enable or disable data collection features, ensuring that their browsing experience remains private and secure.
5. User Configurability:
[0031] Custom Gesture Configuration: Users can easily create and configure custom gestures through the extension's interface. The configuration process typically involves:
[0032] Recording Gestures: Users can perform a gesture they wish to define, which the system records and analyzes to determine its characteristics.
[0033] Assigning Actions: After recording a gesture, users can assign a specific action to it, such as navigating to a particular website, refreshing a page, or executing JavaScript commands.
[0034] Gesture Sensitivity: Users can adjust the sensitivity of gesture detection, allowing them to fine-tune how responsive the system is to their movements. This feature is particularly useful for accommodating different environments and user preferences.
6. Performance Optimization:
[0035] Resource Efficiency: The system is optimized to minimize the computational and bandwidth requirements associated with gesture recognition. This optimization ensures that the model can run efficiently even on devices with limited processing power, such as low-end laptops or tablets.
[0036] Adaptive Learning: The gesture recognition model can be designed to improve over time through adaptive learning techniques. This could involve adjusting its parameters based on user feedback and performance metrics, leading to more accurate gesture recognition as users interact with the system.
[0037] Fallback Mechanisms: To enhance usability, the system can include fallback mechanisms. For instance, if gesture recognition fails or is obstructed, users can still interact with the web browser using traditional methods, such as keyboard shortcuts or mouse controls.
7. User Experience Enhancements:
[0038] Visual Feedback: The system can provide visual feedback to users during gesture recognition. For instance, when a gesture is detected, a subtle visual cue (like a highlighting effect) can confirm that the gesture has been recognized, enhancing user confidence and engagement.
[0039] Accessibility Features: The system is designed with accessibility in mind, ensuring that users with disabilities can utilize the gesture recognition capabilities effectively. This may include options for simplifying gesture commands or providing alternative interaction methods for those with limited mobility.
[0040] Tutorial and Help Section: A tutorial section within the extension can guide users on how to use the gesture recognition features effectively. This section may include instructional videos, quick-start guides, and FAQs to address common user concerns.
[0041] By integrating these components, the proposed hand gesture recognition system aims to provide a robust and user-friendly solution for controlling web browsers, enabling a more intuitive and engaging online experience. The innovation not only addresses existing limitations of gesture-based interfaces but also enhances accessibility and security, making it a significant advancement in the field of human-computer interaction.
[0042] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.
, Claims:1. An interactive hand gesture recognition system for controlling web browsers, comprising:
a gesture recognition model that utilizes a neural network to detect hand landmarks in real-time;
a mathematical model for interpreting recognized gestures based on the spatial relationships between the detected landmarks; and
a user-configurable interface that allows users to create, customize, and assign actions to specific gestures.

2. The system as claimed in claim 1, wherein the gesture recognition model is based on MediaPipe Hands implemented in TensorFlow.js.

3. The system as claimed in claim 1, wherein the mathematical model processes detected hand landmarks to recognize gestures including swipes, taps, and pinches, each mapped to specific web actions.

4. The system as claimed in claim 1, further comprising a browser extension that integrates the gesture recognition functionality within a web browser.

5. The system as claimed in claim 4, wherein the browser extension provides a configuration dashboard for users to view and modify gesture settings, including sensitivity levels and action mappings.

6. The system as claimed in claim 1, wherein the gesture recognition system processes gesture data locally within the user's browser to enhance user privacy and security.

7. The system as claimed in claim 1, wherein the users can record custom gestures and assign them to specific actions, providing flexibility in interaction with web content.

8. The system as claimed in claim 1, further comprising performance optimization features that minimize computational and bandwidth requirements for real-time gesture recognition.

9. The system as claimed in claim 8, wherein the performance optimization features include adaptive learning techniques that improve gesture recognition accuracy based on user interactions.

10. A method for controlling web browsers using the system of claim 1, comprising the steps of:
a) detecting hand landmarks using the gesture recognition model;
b) interpreting recognized gestures through the mathematical model; and
c) executing predefined actions on the web browser based on the interpreted gestures.

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