A GESTURESYNC MUSIC CONTROLLER SYSTEM

Abstract

The present invention relates to a GestureSync music controller system. The GestureSync music controller system comprises a webcam to capture the video, MediaPipe's advanced hand-tracking technology to detect hand landmarks and tracks the movements in real time, a user friendly interface, a gesture-based mouse control mechanism, and a virtual keyboard provides real-time visual feedback to users. The present system presents a gesture-based virtual keyboard and mouse controller leveraging the MediaPipe framework and PyAutoGUI for seamless human-computer interaction. By detecting hand landmarks via a webcam and interpreting finger gestures, the system allows for typing on a virtual keyboard and controlling mouse actions, including clicks and multimedia functions. The device offers intuitive control without the need for physical peripherals, enhancing user accessibility and providing a hands-free computing experience. The system integrates real-time gesture recognition for fluid navigation, keyboard input, and multimedia control.

Specification

Description:
Field of the Invention
The present invention relates to the field of remote controllers, more particular to a GestureSync music controller system.
Background of the Invention
The GestureSync Music Controller System addresses significant accessibility issues found in conventional input devices like keyboards and mice. Traditional setups often pose challenges in environments where physical contact with devices is undesirable. GestureSync provides a touchless alternative by relying on hand gestures for controlling music playback and volume. This system reduces the need for physical interaction, offering an accessible interface for users who require alternative input methods, making it suitable for various use cases where touchless interaction is preferred.
Existing solutions, such as:
Patent US6128003A for a "Hand Gesture Recognition System and Method" describes a noise-reduction technique using rotational vectors for hand images, along with color segmentation and principal component analysis for gesture modeling. Proposed solution enhances this by integrating gesture recognition into a music player, allowing intuitive control through hand gestures.
Patent EP0849697B1 for "A Hand Gesture Recognition System and Method" details a technique that reduces noise in hand image processing by using rotational vectors derived from a real-valued centroid. It employs color segmentation for identifying hand regions and principal component analysis for gesture modeling. Proposed solution builds on this by implementing gesture recognition in a music player, enabling intuitive control through hand gestures.
Patent US6859196B2 for "Pointing Device with Hand Detection" describes a technology that integrates hand detection into a pointing device, enhancing user interaction through gesture-based control. This innovation allows for more intuitive and flexible use of pointing devices. Proposed solution expands on this concept by incorporating hand gesture recognition into a music player, enabling users to control playback through natural hand movements.
Patent 20230393665 for "Hand Gesture Recognition Based on Detected Wrist Muscular Movements" utilizes micron-resolution radar sensors to detect subtle wrist muscle movements for gesture recognition in AR applications. Proposed solution enhances this by incorporating hand gesture recognition into a music player, enabling intuitive control through wrist gestures.
Proposed invention leverages MediaPipe's advanced hand-tracking technology, eliminating the need for external devices and offering high-precision real-time gesture recognition. It enables seamless virtual keyboard, mouse, and multimedia controls, improving upon previous methods by providing a more intuitive, accurate, and user-friendly experience.
Additionally, recent research in hand-gesture recognition systems emphasizes the need for improving usability in real-world applications. For example:
1. Abraham, A., et al. (2022): This paper explores the impact of gesture recognition systems in IoT devices, particularly within human-computer interaction frameworks. It discusses how gesture-based systems enhance accessibility and user experience by eliminating the need for physical input, making such systems valuable in various environments. Available at: Journal of Service Management and Sustainability
2. Rath, S., et al. (2022): This conference paper examines the development of hand gesture recognition systems for virtual controllers in smart home environments. It highlights the adaptability and efficiency of gesture-based systems in managing smart home devices, emphasizing the potential for touchless control. Available at: Springer
3. Ahmed, N., et al. (2020): A comprehensive review of gesture recognition systems for controlling robotic systems, with a focus on machine learning algorithms improving gesture interpretation.Available at: Springer
4. Singh, R., et al. (2021): This review discusses the challenges of computer vision-based gesture recognition for human-machine interaction and suggests algorithmic improvements for IoT applications.Available at: IEEE Xplore
By offering a contactless interface with enhanced precision and flexibility, our system significantly improves accessibility and usability, addressing the limitations of existing gesture-based solutions.
None of the prior art indicated above either alone or in combination with one another disclose what the present invention has disclosed.
Drawings
Figure1: Methodology of Gesture control music player
Figure 2: Virtual Keyboard for GestureSync (Windows 11 platform)
Figure3: Virtual Keyboard for GestureSync (MacOS)
Figure 4: Real time implementation of GestureSync (music play on left side and control on right side)
Figure 5: Chart for hand gestures of GestureSync
Detailed Description of the Invention
The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
In any embodiment described herein, the open-ended terms "comprising," "comprises," and the like (which are synonymous with "including," "having" and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like. As used herein, the singular forms "a", "an", and "the" designate both the singular and the plural, unless expressly stated to designate the singular only.
The GestureSync Music Controller System addresses significant accessibility issues found in conventional input devices like keyboards and mice. The GestureSync Music Controller System comprises: a webcam to capture the video, MediaPipe's advanced hand-tracking technology to detect hand landmarks and tracks the movements in real time, a user friendly interface, a gesture-based mouse control mechanism, PyAutoGUI used to translate these gestures into system-level commands, and a virtual keyboard provides real-time visual feedback to users.
The method of GestureSync Music Controller System comprising the following steps:
Hand Detection and Tracking:
The system begins by capturing video input from a standard webcam. Using MediaPipe's advanced hand-tracking technology, the model detects hand landmarks and tracks the movements in real time.
MediaPipe Hand model identifies 21 key points on each hand, including finger joints and the wrist, allowing the system to map gestures to specific actions. This high-precision landmark detection ensures accurate gesture recognition and smooth user interaction.
Gesture Recognition:
After detecting and tracking hand movements, the system interprets these movements as gestures using pre-defined rules. For example, a pinching motion could be translated into a left-click action, or a pointing motion could be used for cursor movement.
Various hand gestures are mapped to specific keyboard and mouse inputs, as well as multimedia controls. Python libraries like PyAutoGUI are used to translate these gestures into system-level commands, ensuring compatibility across different operating systems.
Virtual Keyboard Implementation:
A full-fledged virtual keyboard is implemented on the screen. This keyboard displays key highlights in real time as the user's hand hovers over specific areas. Once a gesture, such as a finger tap, is detected, the system simulates a keystroke. The virtual keyboard layout is customizable and can be tailored for various applications (e.g., specific industries or languages).
To avoid accidental key presses, the system includes gesture delay thresholds, ensuring that the input is intentional.
Mouse Control via Hand Gestures:
The system uses gestures for mouse control, where the hand position is mapped to the screen coordinates. For example, hand movements translate into cursor movement, while pinching gestures trigger click actions.
Advanced gesture tracking ensures that the cursor movement and mouse actions remain smooth and accurate, allowing for natural interaction with digital interfaces. Multi-finger gestures are also supported for complex actions like dragging, zooming, and scrolling.
Multimedia Controls:
Gesture recognition extends to multimedia control, such as adjusting volume, changing tracks, and controlling playback. These controls are mapped to specific hand gestures. For instance, closing a fist might trigger the play/pause function, while rotating the wrist may adjust the volume.
These functionalities are integrated to create a seamless multimedia experience without the need for physical buttons or remote controls.
Real-time Feedback and Error Handling:
Visual feedback is provided to the user to ensure smooth operation and prevent errors. The system highlights the selected keys on the virtual keyboard, offering immediate confirmation of key presses or mouse clicks.
Error-handling mechanisms are in place to minimize misinterpretation of gestures, including filtering out unintended hand movements and adjusting sensitivity based on user preferences.
Cross-platform Deployment:
The software is designed to be lightweight and cross-platform, ensuring compatibility with Windows, macOS, and Linux. The open-source nature of the libraries (MediaPipe, PyAutoGUI) allows for flexibility and easy modification for different environments.
The system leverages Python's flexibility to offer easy integration with various operating systems, ensuring minimal system resource consumption while maintaining high performance.
Results Obtained:
Accuracy of Gesture Recognition:
• The system achieved a high level of accuracy in recognizing hand gestures with minimal latency. Real-time detection and tracking using MediaPipe's hand-tracking technology resulted in an accuracy rate of over 98.7% for most gestures, including cursor movements, clicking actions, and keyboard input.
• The high accuracy ensures that users experience minimal delays or errors in input, making the system reliable for both everyday use and specialized applications such as sterile environments.
Typing Efficiency on the Virtual Keyboard:
• Tests with the virtual keyboard demonstrated high typing efficiency, especially for users familiar with touch typing. The visual feedback provided by the system, which highlights keys in real-time, significantly reduced errors. Users were able to type at speeds comparable to using a physical keyboard after a short acclimatization period.
• Customizable layouts also enabled users to adjust the keyboard to suit their preferences or specific applications, such as gaming or coding.
Smooth Mouse Control:
• The gesture-based mouse control function was smooth and responsive, allowing for precise cursor movement and accurate click actions. The system was able to handle rapid hand movements without misinterpreting gestures.
• Multi-finger gestures for scrolling, dragging, and zooming were also tested and found to be intuitive and effective. These functionalities enhanced user productivity and provided a natural interaction method for navigation.
Multimedia Control Responsiveness:
• The multimedia control gestures, such as play/pause, track changes, and volume adjustment, were highly responsive and functioned as expected. Users found these features particularly useful during presentations and media playback, as they could control the system from a distance without needing physical peripherals.
• The predefined gestures were easy to learn, and users were able to perform multimedia functions seamlessly after brief practice.
Ease of Deployment and Cross-Platform Performance:
• The system was easily deployed on multiple operating systems, including Windows, macOS, and Linux, without requiring significant modifications. It worked efficiently across different platforms, providing the same level of performance and gesture accuracy.
• The use of a standard webcam and open-source libraries like MediaPipe and PyAutoGUI kept resource usage low, allowing the system to run smoothly on most consumer-grade hardware. This ensures that the invention is accessible to a broad range of users without needing expensive upgrades.
User Experience and Accessibility:
• User feedback was overwhelmingly positive, particularly from individuals with limited mobility who found the hands-free operation empowering. The system's accessibility features, such as the ability to control the computer without physical interaction, made it an excellent solution for people with physical disabilities.
• The system's customizable layouts and intuitive gesture control made it suitable for a wide variety of use cases, from productivity to entertainment, enhancing the overall user experience.
The advantages of the present system are as follows:
Cost-effective: Unlike other virtual control systems that require specialized hardware such as motion sensors or wearable devices, this system only requires a standard webcam and open-source software libraries. MediaPipe, an open-source framework, is used for hand detection and gesture recognition, making the setup highly affordable. As no additional hardware is needed, the solution provides an economical alternative to expensive touch-free interfaces.
High accuracy: Leveraging the advanced capabilities of MediaPipe, this system ensures accurate and real-time hand landmark detection. The model's precision enables smooth and responsive user interaction, detecting even subtle finger movements. This high accuracy allows for reliable virtual typing on the keyboard and efficient mouse control, providing an experience comparable to physical peripherals.
Multifunctional: Beyond typing and mouse control, the system integrates various multimedia functions, such as volume adjustment, track selection, and play/pause control using predefined gestures. This makes the system versatile, allowing users to manage different types of input seamlessly, all through hand gestures. The multifunctional nature of the invention increases its utility in diverse settings, from office productivity to entertainment.
Customizable layouts: The virtual keyboard layout is customizable, allowing it to be adapted to various applications or user preferences. This flexibility means the system can support different languages, key arrangements, or specialized input methods required for unique tasks. Custom layouts can be optimized for specific fields such as gaming, design, or technical work, enhancing usability across different domains.
Cross-platform support: The system is designed to work seamlessly across multiple operating systems, including Windows, macOS, and Linux. This ensures that it can be implemented in a wide range of devices without requiring OS-specific modifications. Cross-platform compatibility increases its appeal and usability, making it suitable for different user bases and ensuring broader accessibility for individuals and organizations.
In summary, GestureSync Music Controller System developed a hands-free, gesture-based virtual keyboard and mouse controller using MediaPipe for hand detection and PyAutoGUI for system input. It allows users to control their computer through hand gestures, enabling accurate text input, mouse actions, and multimedia control. The system is cross-platform, cost-effective, and accessible, providing an intuitive solution for users with physical limitations or in sterile environments. It achieved high accuracy in real-time gesture recognition and efficient performance across Windows, macOS, and Linux, using only a standard webcam and open-source software.

, Claims:1. A GestureSync Music Controller System comprises:
a webcam to capture the video, MediaPipe's advanced hand-tracking technology to detect hand landmarks and tracks the movements in real time, a user friendly interface, a gesture-based mouse control mechanism, PyAutoGUI used to translate these gestures into system-level commands, and a virtual keyboard provides real-time visual feedback to users.
2. The GestureSync Music Controller System as claimed in the claim 1, wherein method of the present system comprising the following steps:
• Step1: Start;
• Step2: Initialize OpenCV, MediaPipe, PyAutoGUI
• Step 3: Set up webcam and hand detection;
• Step 4: Detect hand and landmarks;
• Step 5: Check for key hover or gesture;
• Step 6: Simulate key press if key hovered;
• Step 7: Control mouse movement and clicks;
• Step 8: Control media playback with gestures
• Step 9: Display virtual keyboard and gestures
• Step 10: Press 'q' to quit?
• Step 11: End
3. The GestureSync Music Controller System as claimed in the claim 1, wherein system achieved a high level of accuracy in recognizing hand gestures with minimal latency, Real-time detection and tracking using MediaPipe's hand-tracking technology resulted in an accuracy rate of over 98.7% for most gestures, including cursor movements, clicking actions, and keyboard input.
4. The GestureSync Music Controller System as claimed in the claim 1, wherein system achieved high accuracy in real-time gesture recognition and efficient performance across Windows, macOS, and Linux, using only a standard webcam and open-source software.

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