A Collar Device for Hand Gesture Recognition Using Convolutional Neural Networks (CNN)

Abstract

The present invention discloses a wearable collar device for hand gesture recognition utilizing Convolutional Neural Networks (CNN). The collar device incorporates multiple sensors, including infrared cameras, depth sensors, and inertial measurement units (IMUs), along with a microcontroller and processing unit capable of running CNN models. The device is worn around the neck and captures gesture data from the user's hand, which is processed in real-time to recognize and interpret hand gestures. This collar device is lightweight, unobtrusive, and designed for easy use, making it suitable for various applications such as communication aids for individuals with speech and hearing impairments, gesture-based control of electronic devices, and input for augmented or virtual reality systems. The device's high accuracy in gesture recognition, portability, and versatility make it an ideal solution for enhancing accessibility, convenience, and user experience in everyday activities.

Specification

Description:[014] The following sections of this article will provide various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention. Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[015] Referring now to the drawings, these are illustrated in FIG. 1, the collar device comprises:
Collar Housing: The collar is made from lightweight, flexible materials to ensure comfort during extended wear. It contains slots for mounting various sensors and electronic components.
Sensor Array: The collar device integrates multiple sensors, including infrared (IR) cameras, depth sensors, and inertial measurement units (IMUs). These sensors are strategically positioned around the collar to capture hand movements from multiple angles, ensuring high accuracy in gesture detection.
[016] In accordance with another embodiment of the present invention, the system utilizes a hardware-based AI accelerator, which may include either an FPGA or an ASIC, specifically designed to execute AI/ML algorithms for financial forecasting. The AI accelerator is capable of handling computationally intensive operations, such as deep learning, neural networks, and other predictive modeling techniques, to generate accurate investment forecasts in real-time.
[017] In accordance with another embodiment of the present invention, Microcontroller and Processing Unit: A microcontroller is embedded in the collar for data acquisition and processing. The processing unit is capable of running Convolutional Neural Network (CNN) models. The microcontroller collects raw data from the sensors and processes it using the CNN model to classify and interpret hand gestures.
Power Supply: The device is powered by a rechargeable battery. The collar includes a USB port for charging, and an optional solar panel strip on the collar exterior provides additional charging capability.
Connectivity Module: A wireless communication module (e.g., Bluetooth or Wi-Fi) is included to connect the device to external systems, such as smartphones, computers, or other smart devices.
[018] In accordance with another embodiment of the present invention, hand Gesture Recognition Method comprises the steps:
Data Collection: The sensor array collects data corresponding to the position, movement, and orientation of the user's hand. The IR cameras and depth sensors capture visual data, while the IMUs measure motion-related data.
Preprocessing: The raw sensor data is preprocessed by the microcontroller to remove noise and standardize input dimensions. Preprocessing steps include filtering, normalization, and coordinate transformation as shown in figure 2.
[019] In accordance with another embodiment of the present invention, CNN-Based Recognition: The preprocessed data is fed into a Convolutional Neural Network (CNN) model implemented on the processing unit. The CNN is trained to recognize a set of predefined hand gestures. The CNN model consists of multiple layers, including convolutional layers, pooling layers, and fully connected layers, which enable it to learn spatial features and classify gestures effectively.
Output Generation: Upon recognizing a gesture, the microcontroller generates a corresponding output signal. The output can be communicated via the connectivity module to control external devices, generate text/speech output, or trigger specific actions in an application.
[020] The CNN model is initially trained using a dataset of hand gesture images and sensor data. The dataset is collected from multiple users to ensure generalizability across different hand shapes, sizes, and skin tones. The model is trained using supervised learning, employing labeled examples of each gesture class.
The training process involves optimizing the CNN parameters to minimize classification error, using techniques like backpropagation and stochastic gradient descent (SGD). The trained model is stored in the collar's processing unit for real-time inference.
[021] The collar device can be used by individuals with speech and hearing impairments to communicate effectively by translating hand gestures into text or speech using a connected device. It can control home appliances, such as lights or entertainment systems, through simple hand gestures, providing a convenient and accessible interface. The device can be used as an input mechanism for AR/VR systems, providing an intuitive way to interact with virtual environments.
[022] The collar device is lightweight, portable, and easy to wear, making it suitable for everyday use. The collar device allows hands-free gesture recognition, enabling users to perform gestures naturally without holding any external device. The use of CNN for gesture recognition provides high accuracy and robustness, even in varying lighting conditions and environments. The device can be used for a wide range of applications, including communication, home automation, and AR/VR interaction.
[023] The collar device for hand gesture recognition using Convolutional Neural Networks (CNN) represents a significant advancement in wearable technology for gesture-based control and communication. The device's compact, lightweight, and user-friendly design makes it accessible for a wide range of users, including individuals with disabilities. By employing a combination of infrared cameras, depth sensors, and inertial measurement units, along with a CNN model, the device provides high accuracy in gesture recognition and robust performance in various environments. Its versatility across multiple applications, including communication aids, home automation, and AR/VR systems, further underscores its potential impact in enhancing accessibility and convenience in everyday life. This invention not only addresses the limitations of existing gesture recognition systems but also promotes inclusivity and ease of interaction in modern digital environments.
[024] The benefits and advantages that the present invention may offer have been discussed above with reference to particular embodiments. These benefits and advantages are not to be interpreted as critical, necessary, or essential features of any or all of the embodiments, nor are they to be read as any elements or constraints that might contribute to their occurring or becoming more evident.
[025] Although specific embodiments have been used to describe the current invention, it should be recognized that these embodiments are merely illustrative and that the invention is not limited to them. The aforementioned embodiments are open to numerous alterations, additions, and improvements. These adaptations, changes, additions, and enhancements are considered to be within the purview of the invention. , Claims:1. A collar device for recognizing hand gestures, comprising:
a collar housing configured to be worn around the neck;
a sensor array comprising at least one infrared camera, one depth sensor, and one inertial measurement unit, the sensor array configured to capture data corresponding to hand gestures;
a microcontroller configured to collect and preprocess data from the sensor array;
a processing unit configured to implement a Convolutional Neural Network (CNN) model for recognizing hand gestures from the preprocessed data;
a power supply comprising a rechargeable battery and an optional solar panel;
a wireless communication module for transmitting recognized gestures to external devices.
2. The collar device as claimed in claim 1, wherein the CNN model is trained to recognize a plurality of predefined hand gestures using a dataset collected from multiple users.
3. The collar device as claimed in claim 1, wherein the sensor array is positioned around the collar to capture hand movement from multiple angles.
4. The collar device as claimed in claim 1, further comprising a connectivity module selected from the group consisting of Bluetooth and Wi-Fi, to facilitate communication with external devices.
5. The collar device as claimed in claim 1, wherein the power supply further comprises a USB port for recharging the battery.
6. A method for recognizing hand gestures using a collar device, the method comprising:
a) capturing hand gesture data using a sensor array comprising at least one infrared camera, one depth sensor, and one inertial measurement unit;
b) preprocessing the captured data to remove noise and standardize input dimensions;
c) processing the preprocessed data using a Convolutional Neural Network (CNN) model implemented on a processing unit to recognize a hand gesture;
d) generating an output signal corresponding to the recognized hand gesture and transmitting the output signal to an external device.
7. The method as claimed in claim 6, wherein the CNN model is trained using a dataset of hand gestures collected from multiple users to ensure generalizability.
8. The method as claimed in claim 6, wherein the output signal is used to control a home automation system, generate text or speech, or interact with an augmented reality or virtual reality system.

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