Description:FIELD OF THE INVENTION
The present invention relates to an emotion prediction system designed for individuals who use sign language. More particularly, it integrates gesture recognition with facial expression analysis and real-time psychological assessment to improve communication and support mental health monitoring.
BACKGROUND OF THE INVENTION
Developing an emotion prediction system for individuals who use sign language is important to improve communication and support mental health. This system will identifies hand gestures and facial expressions instantly providing insights into emotional states and psychological wellbeing for caregivers and health professionals.
The existing system stands out due to its integration of gesture and facial recognition with real time psychological evaluation allowing for the early identification of emotional distress. The existing systems typically do not evaluate mental well-being. The proposed system however integrates gesture and facial recognition with psychological evaluations to capture of real-time emotions and the identification of patterns over time. Utilizing advanced models, it observes subtle behavioral changes, offering a more comprehensive understanding of emotions. This method is especially beneficial for deaf and mute individuals as it encompasses both communication and mental health support addressing gaps present in existing system.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The present invention proposes an advanced emotion prediction system specifically designed for individuals who utilize sign language. The system integrates gesture recognition and facial expression analysis with real-time psychological assessment to interpret emotional states and assess psychological health. By leveraging machine learning models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and advanced facial recognition techniques, the system ensures accurate emotion prediction and comprehensive well-being analysis.
The system captures video input to analyze hand movements and facial expressions, identifying emotions such as happiness, sadness, anger, and anxiety. It employs machine learning techniques, including CNN and LSTM, to analyze both static and dynamic features of gestures. To enhance accuracy, the system integrates facial expression analysis using YOLOv5 and the Facial Action Coding System (FACS), providing deeper insights into emotional states. Additionally, Natural Language Processing (NLP) is incorporated to decode language patterns, further refining the emotional analysis process.
The system generates detailed reports tracking emotional trends over time, issuing alerts for potential psychological issues and recommending mental health interventions when necessary. By incorporating psychological techniques such as Cognitive Behavioral Therapy (CBT), it offers real-time alerts to caregivers, enabling proactive mental health support. This comprehensive approach ensures early identification of emotional distress and fosters better communication for individuals who rely on sign language.
The invention stands out by combining gesture recognition, facial expression analysis, and real-time psychological assessment, making it a valuable tool for deaf and mute individuals. Through continuous monitoring and predictive analysis, the system enhances emotional intelligence and facilitates timely interventions, significantly improving the quality of life for users and their caregivers.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
This initiative seeks to establish a real-time emotion prediction system designed for individuals who utilize a sign language. By integrating gesture recognition with psychological analysis, the system aim to interpret emotional states and assess psychological health thereby improving communication and mental health support for the deaf and mute communities. The system will analyze video input to assess hand movements and facial expression, allowing it to identify emotions such as happiness, sadness, anger and anxiety. Utilizing advanced machine learning techniques including CNN and LSTM methods, the system will effectively interpret gestures and detect emotions by examining by analyzing both static and dynamic features of the gestures.
 
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The emotion prediction system consists of multiple components that work cohesively to capture, process, and analyze gestures and facial expressions, providing real-time emotional insights. The system employs a high-resolution camera to capture video input, which is then processed using advanced computer vision algorithms to detect hand gestures and facial expressions. The core functionalities of the system are driven by CNN and LSTM models that analyze spatial and temporal features of gestures and expressions, ensuring precise emotion recognition.
The first step in the process involves extracting hand gestures from the video feed. The system utilizes deep learning-based object detection techniques, such as YOLOv5, to track hand movements accurately. Once the gestures are identified, LSTM networks analyze sequential patterns, distinguishing between various emotional states based on movement dynamics. Simultaneously, the system employs the Facial Action Coding System (FACS) to decode subtle facial expressions, identifying micro-expressions that indicate underlying emotions.
To enhance predictive accuracy, the system integrates gesture recognition with facial analysis, cross-referencing emotional cues to generate a comprehensive assessment. The system also utilizes NLP techniques to interpret any linguistic cues that may be present in the video input, refining its understanding of the user’s emotional state. The fusion of multiple data sources ensures robust emotion detection, minimizing errors and improving reliability.
A key feature of the system is its ability to generate real-time emotional assessments. By continuously monitoring the user’s emotional state, the system identifies trends over time, detecting behavioral shifts that may indicate psychological distress. This capability is particularly beneficial for caregivers and mental health professionals, allowing for early intervention and personalized mental health support.
Furthermore, the system incorporates Cognitive Behavioral Therapy (CBT) techniques, offering guided interventions based on detected emotional patterns. In cases where psychological distress is detected, the system provides alerts and recommendations, ensuring timely support for the user. The integration of CBT principles enhances the system’s effectiveness in mental health management, making it a proactive tool for emotional well-being.
The emotion prediction system also features a user-friendly interface, allowing individuals, caregivers, and professionals to access real-time emotional insights. Reports generated by the system include graphical representations of emotional trends, enabling easy interpretation of emotional fluctuations. These reports facilitate informed decision-making, improving mental health care and communication support for deaf and mute individuals.
By combining advanced AI models, computer vision techniques, and psychological assessments, the proposed system represents a significant advancement in emotion recognition and mental health support. Its real-time monitoring, predictive analysis, and proactive intervention capabilities make it a groundbreaking tool in assistive technology.
This initiative seeks to establish a real-time emotion prediction system designed for individuals who utilize a sign language. By integrating gesture recognition with psychological analysis, the system aim to interpret emotional states and assess psychological health thereby improving communication and mental health support for the deaf and mute communities. The system will analyze video input to assess hand movements and facial expression, allowing it to identify emotions such as happiness, sadness, anger and anxiety. Utilizing advanced machine learning techniques including CNN and LSTM methods, the system will effectively interpret gestures and detect emotions by examining by analyzing both static and dynamic features of the gestures.
To improve accuracy the idea will integrate facial expression analysis with gesture recognition. It will employ advanced models such as YOLOv5 and the Facial Action Coding System (FACS) will be used to analyze expressions and provide enhanced emotional insights. The system will utilize Natural Language Processing to decode any language patterns found in the video input, further refining its understanding of the user’s emotional condition. This emotion prediction system will generate detailed reports that track emotional trends over time, alerts for possible psychological issues and recommend mental health assistance when necessary.
The proposed system is remarkable because it combines gesture and facial recognition alongside real-time psychological assessments to enable the early identification of emotional distress. Utilizing advanced technologies such as CNN-LSTM, FACS and YOLOv5, it goes beyond conventional emotion recognition by examining hand gestures, facial expression and emotional trends over time. The inclusion of psychological techniques like Cognitive Behavioral Therapy (CBT) and providing real-time alerts for caregiver, this system serves as proactive mental health tool designed specifically for individuals who are deaf and mute.
, Claims:1. An emotion prediction system for individuals using sign language, comprising a video input module, a gesture recognition module, a facial expression analysis module, and a real-time psychological assessment module.
2. The system as claimed in claim 1, wherein the gesture recognition module utilizes Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to analyze hand movements.
3. The system as claimed in claim 1, wherein the facial expression analysis module employs the Facial Action Coding System (FACS) to decode facial micro-expressions.
4. The system as claimed in claim 1, wherein the psychological assessment module integrates Cognitive Behavioral Therapy (CBT) principles to provide mental health support.
5. The system as claimed in claim 1, further comprising Natural Language Processing (NLP) to decode linguistic cues from the video input.
6. The system as claimed in claim 1, wherein the gesture and facial recognition modules utilize YOLOv5 for real-time object detection.
7. The system as claimed in claim 1, further comprising an alert mechanism to notify caregivers of detected emotional distress.
8. The system as claimed in claim 1, wherein emotional trends are tracked over time to provide predictive mental health insights.
9. The system as claimed in claim 1, wherein real-time reports are generated to visualize emotional patterns for caregivers and health professionals.
10. The system as claimed in claim 1, wherein predictive analytics identify behavioral shifts and recommend mental health interventions.