EMOTION-SENSITIVE LED WEARABLE DEVICE FOR REAL-TIME EMOTIONAL FEEDBACK

Abstract

The present invention is a wearable pendant device (100) that provides real-time emotional feedback based on physiological data. It includes biometric sensors (200)—specifically, a Heart Rate Variability (HRV) sensor (210), Galvanic Skin Response (GSR) sensor (220), and Body Temperature sensor (230)—that collect data linked to emotional states. An AI-based emotion detection engine (300) with a supervised learning model (310), neural network (320), and contextual data processing module (330) classifies emotions and assesses intensity. Feedback is delivered through an LED display system (400) with adjustable color, brightness, and pulsing frequency, supported by haptic (610) and auditory (620) feedback options. A user customization interface (500) allows personalized settings. The data storage and management module (1200) offers local (1210) and cloud backup (1220), while a power management module (700) with a rechargeable battery (710) and wireless connectivity module (800) support efficient operation and data synchronization, promoting adaptive emotional awareness and self-regulation. FIG.1 Claim 1-13

Specification

Description:FIELD OF INVENTION
The present invention relates to wearable technology in the fields of artificial intelligence and human-computer interaction, particularly to a wearable device capable of providing real-time emotional feedback through biometric data analysis.
BACKGROUND
[001] In recent years, there has been a growing interest in wearable technology that monitors physical health metrics, such as heart rate, steps, and sleep patterns, as a means to promote wellness and maintain a healthy lifestyle. Many wearable devices utilize sensors to track physiological data and provide users with valuable insights into their physical health. These devices are widely used in fitness tracking, health monitoring, and general wellness applications, offering users feedback that can encourage healthier habits and assist in medical monitoring.
[002] However, while physical health monitoring has become commonplace, technology for emotional or mental health monitoring remains underdeveloped. Current solutions in the wellness space are typically limited to mood-tracking applications or self-reported assessments, which often rely on manual inputs from the user. This approach, however, lacks real-time adaptability and is often impractical in dynamic environments where users may not have the opportunity to engage with an app to track their emotions. Such solutions fail to provide immediate feedback, limiting users' ability to develop self-awareness of their emotional state in real-time, which is essential for emotional regulation and mental well-being.
[003] Moreover, while some systems in mental health and productivity aim to promote emotional awareness, they often depend on post-event analysis rather than real-time feedback, rendering them less effective in assisting users to respond to their current emotional states. Additionally, most wearable devices do not incorporate emotion-tracking features that provide feedback on the user's emotional shifts as they occur. This gap in existing technology limits the benefits that wearable devices could offer in helping users achieve greater mental and emotional well-being through timely awareness and regulation of emotions.
[004] In view of the foregoing disadvantages inherent in the known systems in the prior art, the present invention provides a novel method and system.
OBJECTS OF THE INVENTION
[001] The principal objective of the present invention is to provide a wearable device that delivers real-time visual feedback of the user's emotional state, enabling users to develop emotional awareness and respond to their emotions instantly without the need for manual input.
[002] Yet another objective is to offer a system that passively monitors physiological signals, such as heart rate variability (HRV), galvanic skin response (GSR), and body temperature, to accurately detect emotional states based on scientifically recognized biomarkers. This objective aims to enhance the reliability and accuracy of emotional detection by leveraging well-established physiological indicators.
[003] Yet another objective is to employ artificial intelligence (AI) and machine learning algorithms that continuously adapt to the user's unique emotional patterns, learning the individual baseline responses of each user to refine the system's accuracy over time. This adaptive AI feature allows for personalized emotional insights, making the wearable more attuned to individual emotional responses.
[004] Yet another objective is to integrate customizable visual indicators, allowing users to select colors, brightness, and pulsing frequency on the device's LED display to represent different emotional states. This customization capability provides users with the flexibility to personalize their experience and align the device's feedback with their preferences.
[005] Yet another objective is to support alternative feedback mechanisms beyond visual indicators, such as haptic or auditory feedback, to enhance the versatility of the device in environments where visual cues may be impractical. This alternative embodiment broadens the range of scenarios in which the device can be effectively used.
[006] Ultimately, the invention seeks to create a wearable system that promotes emotional awareness and mental well-being by providing real-time, intuitive feedback on the user's emotional state, thereby empowering users to manage their emotions proactively and improve their overall quality of life.


SUMMARY OF THE INVENTION
[001] The present invention relates to an Emotion-Sensitive LED Wearable Device (100) designed to provide real-time emotional feedback through a combination of biometric sensors (200), an AI-based emotion detection engine (300), and a customizable LED display system (400). The device passively monitors physiological signals-including Heart Rate Variability (HRV) (210), Galvanic Skin Response (GSR) (220), and Body Temperature (230)-to detect the user's emotional state.
[002] The emotion detection is powered by a machine learning model (310), part of the AI-based engine (300), which classifies emotions by analyzing real-time data from the biometric sensors (200). Over time, the adaptive model learns the user's baseline emotional patterns, refining its accuracy. The LED display system (400) offers visual feedback, with customizable options for color mapping (410), brightness control (420), and pulsing frequency (430) based on the intensity of the detected emotion.
[003] Users can further personalize their experience via a mobile app interface (500), where they can adjust color mappings (510) and set intensity levels (520) for emotional states. The invention supports alternative feedback mechanisms, such as a haptic feedback module (610) and auditory feedback module (620), broadening the device's adaptability in various environments.
[004] Through wireless connectivity (800) to a companion app, the device collects emotional data, stored and managed within a data storage system (1200), which includes both local storage (1210) and optional cloud backup (1220). The app interface (1300) provides real-time emotional trend displays (1310), historical visualizations (1320), and system settings (1330) for user convenience.
DETAILED DESCRIPTION
[001] The present invention is an Emotion-Sensitive LED Wearable Device (100) designed to detect, interpret, and provide real-time feedback on a user's emotional state. The device achieves this through an integration of biometric sensors (200), an AI-driven emotion detection engine (300), and a customizable LED display system (400). This wearable device is intended to promote emotional awareness by providing instant visual cues that correspond to the user's emotional state.
Biometric Sensors
[002] The Biometric Sensors (200) play a crucial role in the real-time detection of emotional states by capturing physiological data that reflects emotional variations. Three primary sensors are integrated into the device:
[003] Heart Rate Variability (HRV) Sensor (210): The HRV sensor measures the variation in intervals between heartbeats, which serves as a reliable indicator of emotional arousal, stress levels, and relaxation. The HRV data offers insights into the autonomic nervous system's response, making it a valuable metric for real-time emotional monitoring.
[004] Galvanic Skin Response (GSR) Sensor (220): This sensor measures the electrical conductance of the skin, which varies with perspiration and can indicate stress or heightened emotional arousal. GSR data is highly responsive to changes in emotional intensity, offering a sensitive measure for emotional detection.
[005] Body Temperature Sensor (230): Body temperature provides additional data that complements HRV and GSR readings. Emotional states, such as anxiety or stress, can affect skin temperature, thus validating and refining the emotional detection accuracy achieved by the HRV and GSR sensors.
AI-Based Emotion Detection Engine
[006] The AI-Based Emotion Detection Engine (300) processes the physiological data from the biometric sensors (200) to classify and interpret the user's emotional state. The engine is powered by a Supervised Learning Model (310) and a Neural Network (320) that are trained on datasets correlating biometric signals with various emotional states.
[007] Adaptive Learning: The emotion detection engine features adaptive capabilities, allowing it to adjust to the user's individual emotional baselines over time. This adaptability enhances the system's accuracy by learning the unique physiological responses of each user, leading to a personalized emotional assessment. The engine also includes a Contextual Data Processing Module (330) that incorporates situational data, such as time of day or location, to refine emotion classification based on the user's environment.
LED Display System
[008] The LED Display System (400) provides real-time, visually intuitive feedback on the user's emotional state through dynamic color changes. The system includes a LED Color Mapping Module (410), which translates each detected emotion into a predefined color based on user-selected mappings.
[009] Brightness Control (420): This component adjusts the LED brightness based on the intensity of the detected emotion, with higher brightness levels for stronger emotional states. For example, a high-stress state may be indicated by a bright red LED.
[010] Pulsing Frequency Control (430): The pulsing frequency of the LED varies according to the emotional intensity, with rapid pulses indicating heightened emotional arousal and slow, steady pulses reflecting calm or relaxed states. These customizations allow the wearable to serve as a real-time emotional indicator for both the user and others around them.
User Customization Interface (Mobile App)
[011] The User Customization Interface (500) enables users to personalize the wearable's feedback mechanism. Through a mobile application, users can adjust the Color Mapping Customization (510) and Intensity Level Adjustment (520) for each emotional state, tailoring the feedback to their preferences.
[012] The app also offers options to activate or deactivate specific features, such as the brightness or pulsing of the LED, providing flexibility for various environments and personal sensitivities.
Alternative Feedback Modules
[013] The Alternative Feedback Modules (600) offer feedback mechanisms beyond the LED display, broadening the device's functionality for users who may require alternative cues:
[014] Haptic Feedback Mechanism (610): The device can provide tactile feedback, with distinct vibration patterns corresponding to different emotions. For instance, quick pulses could signify stress, while a steady vibration might represent calmness.
[015] Auditory Feedback Mechanism (620): This option provides audio cues linked to emotional states. For example, a soft chime could indicate calmness, while an urgent beep could signal stress. This feature can be paired with conversational AI for spoken suggestions based on detected emotional states.
Power Supply and Battery Management
[016] The Power Supply and Battery Management (700) system powers the wearable and manages its energy consumption. It includes a Rechargeable Battery (710) that is optimized for long-lasting use, along with a Power Optimization Circuit (720) that minimizes energy drain when biometric data indicates a steady emotional state, thereby prolonging battery life.
Wireless Connectivity Module
[017] The Wireless Connectivity Module (800) enables data transfer between the wearable device and the mobile app via Bluetooth Connectivity (810) or Wi-Fi Connectivity (820). This connection facilitates real-time updates and data synchronization for enhanced user experience and data management.
Data Collection Module
[018] The Data Collection Module (900) is responsible for acquiring and processing physiological data in real time. It includes Real-Time Data Acquisition (910) and a Signal Processing Unit (920) that filters and prepares the biometric data for analysis by the AI-based emotion detection engine.
Emotion Classification Module
[019] The Emotion Classification Module (1000) categorizes detected emotions and assigns an Emotion Intensity Scoring (1010) to indicate the strength of each emotional state. The Emotion State Classification (1020) differentiates emotions, such as calm, stress, happiness, and excitement, based on the real-time data from the biometric sensors.
User Feedback Control
[020] The User Feedback Control (1100) allows users to adjust the device's responsiveness to specific emotions. This module includes a Feedback Override Option (1110) for temporary suppression of feedback, as well as a Feedback Sensitivity Adjustment (1120) to control the system's reactivity to detected emotional states.
Data Storage and Management
[021] The Data Storage and Management (1200) system provides storage options for emotional data collected over time. Local Data Storage (1210) retains data on the device, while Cloud Data Backup (1220) allows for optional storage in a secure cloud environment, enabling users to access historical emotional trends.
Companion App Features
[022] The Companion App Features (1300) enhance the device's functionality by offering data visualization and user management options. The app provides a Real-Time Emotional Trend Display (1310), Historical Emotional Data Visualization (1320), and System Settings and Preferences (1330) that allow users to review their emotional patterns and make adjustments as needed.
BIOMETRIC SENSORS (200)
[001] The Biometric Sensors (200) are integral to the invention's ability to detect and interpret emotional states in real time. By capturing physiological data that correlates with distinct emotional conditions, the biometric sensors provide the foundation upon which the AI-Based Emotion Detection Engine (300) operates. These sensors work continuously, passively collecting data without requiring user intervention, thus allowing for seamless emotional monitoring.
[002] The device incorporates three primary types of biometric sensors:
[003] Heart Rate Variability (HRV) Sensor (210): The HRV sensor measures the interval between consecutive heartbeats, capturing variations that reflect the user's autonomic nervous system activity. These variations, known as heart rate variability, serve as a reliable proxy for stress, relaxation, and emotional arousal. By analyzing HRV, the wearable can identify shifts between sympathetic (stress) and parasympathetic (relaxation) nervous states. The HRV sensor is designed for high sensitivity to detect even subtle fluctuations, which allows the device to accurately assess changes in emotional intensity as they occur.
[004] Galvanic Skin Response (GSR) Sensor (220): The GSR sensor measures the electrical conductance of the skin, which is influenced by sweat gland activity. This sensor is highly responsive to the physiological changes associated with emotional arousal, such as stress or excitement, as sweat gland activity typically increases in response to heightened emotional states. The GSR sensor is advantageous because it can detect rapid changes in arousal, providing real-time insight into the user's emotional response.
[005] Body Temperature Sensor (230): This sensor monitors variations in the user's skin temperature, which can fluctuate with changes in emotional states. For instance, stress or anxiety may cause a drop in skin temperature, whereas relaxation may correlate with stable or slightly elevated skin temperatures. The body temperature sensor complements the HRV and GSR data, offering a cross-verification mechanism to improve the accuracy of the emotion detection process.
[006] Collectively, these sensors (200) enable the wearable device to gather a comprehensive profile of the user's physiological state, which is essential for accurate emotion classification. The data from each sensor is relayed to the AI-Based Emotion Detection Engine (300), where it undergoes processing and analysis to derive the user's emotional state. This continuous, automated data capture ensures that the device remains unobtrusive while delivering high-quality emotional insights.
AI-BASED EMOTION DETECTION ENGINE (300)
[007] The AI-Based Emotion Detection Engine (300) is the central processing unit responsible for analyzing the physiological data collected by the Biometric Sensors (200) to classify and interpret the user's emotional state. This engine utilizes advanced machine learning techniques to accurately identify emotional conditions by processing real-time data from the HRV, GSR, and body temperature sensors. The AI engine continuously learns and adapts to the user's individual emotional patterns, enhancing its classification accuracy over time.
[008] The emotion detection engine is primarily powered by a Supervised Learning Model (310), trained on extensive datasets that correlate biometric data patterns with specific emotional states. This model is capable of recognizing physiological markers associated with emotions such as calmness, stress, happiness, and excitement. Each emotional state is identified based on unique patterns within the biometric data, allowing the model to provide real-time emotional feedback. The Neural Network (320) within the AI engine processes complex data relationships and enhances the model's capacity to detect and differentiate subtle emotional shifts.
[009] Adaptive Learning Capabilities: To provide personalized insights, the engine incorporates adaptive learning algorithms that adjust to the user's baseline physiological responses over time. As the system gathers more data on the user's reactions to various emotional stimuli, it develops a nuanced understanding of their emotional profile. This adaptability allows the AI to improve its accuracy by factoring in individual variations, resulting in an increasingly precise classification of emotions.
[010] The Contextual Data Processing Module (330) supplements the engine by incorporating situational information-such as time of day, location, and activity context-into the analysis. This module enhances emotion classification by contextualizing physiological signals within the user's environment, leading to a more refined understanding of emotional states. For example, a stress indicator detected during a commute might be interpreted differently than one detected during a social gathering, adding a layer of situational awareness to the emotion detection process.
[011] The output from the AI-Based Emotion Detection Engine (300) includes not only the classified emotional state but also an Emotion Intensity Score that reflects the strength of the detected emotion. This score guides the LED Display System (400) in adjusting its color, brightness, and pulsing frequency to represent the emotional state visually, creating a dynamic and responsive feedback system.
LED DISPLAY SYSTEM (400)
[012] The LED Display System (400) serves as the primary visual feedback mechanism for the Emotion-Sensitive LED Wearable, enabling users to instantly recognize their emotional state through a dynamic color display. This system interprets the output from the AI-Based Emotion Detection Engine (300) and adjusts the LED display to reflect the detected emotion. The LED feedback is not only color-coded but can vary in brightness and pulsing frequency, offering a visually intuitive representation of both the type and intensity of the user's emotions.
[013] LED Color Mapping Module (410): This module is responsible for mapping specific colors to different emotional states, providing the primary visual indicator of the user's emotional condition. Each color corresponds to a distinct emotion, with common mappings such as blue for calm, green for happiness, red for stress, and yellow for excitement. The color mappings can be predefined based on common emotional associations or customized by the user through the User Customization Interface (500). This flexibility allows users to align the feedback system with their personal preferences, enhancing the overall user experience.
[014] Brightness Control (420): The brightness of the LED display is modulated based on the intensity of the detected emotion, with higher brightness levels signifying stronger emotional arousal. For instance, mild stress may be indicated by a soft red glow, while high stress could be represented by a bright, intense red. This variation in brightness provides users with a quick visual cue for the strength of their emotional state, allowing for a more nuanced emotional awareness.
[015] Pulsing Frequency Control (430): To further distinguish between emotional intensities, the LED display can pulse at varying frequencies. Rapid pulsing may be used to indicate heightened emotional states such as high excitement or stress, while a slower, steady pulse might represent calmness or relaxation. This pulsing frequency adjustment adds another layer of feedback, allowing the device to convey subtle differences in emotional intensity and alert the user to significant changes in their emotional condition.
[016] The LED Display System (400) thus serves as an intuitive and customizable visual interface, translating complex emotional data into a straightforward, accessible format. By adjusting color, brightness, and pulsing frequency, the system enables users to quickly recognize and interpret their emotional state, facilitating greater emotional awareness and potentially encouraging improved emotional regulation in real-time.
USER CUSTOMIZATION INTERFACE (MOBILE APP) (500)
[017] The User Customization Interface (500), accessible through a mobile app, enables users to personalize their experience with the Emotion-Sensitive LED Wearable. This interface provides users with control over the device's visual feedback mechanisms, allowing them to adjust color mappings, brightness, and intensity settings based on their individual preferences. The customization options enhance the user's emotional connection to the device, ensuring that feedback is meaningful and aligned with personal preferences.
[018] Color Mapping Customization (510): Through this feature, users can define the colors associated with different emotional states. The LED Color Mapping Module (410) defaults to standard color associations (e.g., blue for calm, red for stress), but users may choose alternative color schemes based on their preferences or specific needs. This customization allows each user to tailor the emotional feedback display to reflect their unique preferences, potentially improving emotional engagement and comprehension.
[019] Intensity Level Adjustment (520): This feature allows users to adjust the brightness and pulsing intensity of the LED display. By setting their preferred levels, users can control how prominently the device displays their emotional state. For instance, users in quieter or more private settings may choose softer, dimmer displays, while those in active environments may prefer brighter or more intense feedback. This flexibility makes the wearable adaptable to various social contexts and user environments, maximizing comfort and usability.
[020] Feedback Control Options: The app interface includes settings that allow users to enable or disable specific feedback modes, such as brightness or pulsing, based on their current environment or emotional needs. For instance, in settings where visual feedback may not be practical, users can disable the LED or switch to an alternative feedback mechanism like haptic (610) or auditory feedback (620) provided in the Alternative Feedback Modules (600).
[021] Historical Emotional Data Access: Beyond real-time customization, the app enables users to access historical emotional trends through the Companion App Features (1300), offering insights into past emotional patterns. This feature supports long-term emotional awareness, allowing users to observe changes over time and make informed decisions about emotional management.
[022] Overall, the User Customization Interface (500) offers an adaptable, user-centered approach to interacting with the device. By allowing users to control how emotions are represented visually and configure the device to suit various contexts, the mobile app elevates the device's functionality, making it a highly personalized tool for real-time emotional awareness and self-regulation.
Alternative Feedback Modules (600)
[023] The Alternative Feedback Modules (600) provide additional feedback mechanisms beyond the primary LED display, enhancing the device's adaptability to various user needs and environments. These modules are designed to deliver emotional feedback through alternative sensory channels, such as haptic or auditory cues, making the device versatile and effective in contexts where visual feedback may not be suitable or preferred. These alternative modes allow users to engage with their emotional feedback in a way that best aligns with their surroundings and personal preferences.
[024] Haptic Feedback Mechanism (610): This module offers tactile feedback through vibration patterns that correspond to different emotional states. For example, quick, short pulses could signify a high-stress state, while a steady, calming vibration might represent relaxation. The haptic feedback option is particularly useful in environments where visual cues may be distracting or impractical, such as during meetings or while driving. By providing an unobtrusive yet noticeable feedback method, the haptic feedback mechanism enables the user to receive emotional insights without needing to check the LED display.
[025] Auditory Feedback Mechanism (620): The auditory feedback module provides sound-based cues linked to various emotional conditions. This feedback option might include gentle chimes to indicate calmness or more pronounced beeps for stress. The auditory cues can be further enhanced with spoken suggestions through integrated conversational AI, offering real-time guidance based on the detected emotional state. For example, upon detecting elevated stress levels, the device may suggest, "Take a deep breath," promoting self-regulation. The auditory feedback option is ideal for users who may benefit from verbal reinforcement or subtle audio reminders of their emotional state.
[026] Integration and Customization: Both the haptic (610) and auditory (620) feedback options can be customized through the User Customization Interface (500), allowing users to select their preferred feedback type and adjust intensity settings. Users can enable or disable these feedback modes according to their needs, providing flexibility to switch between feedback types depending on the environment.
[027] By incorporating Alternative Feedback Modules (600), the wearable device becomes a multifunctional emotional awareness tool that can be adapted to diverse settings and user requirements. These modules extend the functionality of the primary LED feedback, ensuring that users receive continuous emotional insights, regardless of their situational constraints or preferences.
Power Supply and Battery Management (700)
[028] The Power Supply and Battery Management (700) module is responsible for providing efficient and long-lasting power to the wearable device, ensuring uninterrupted operation for continuous emotional monitoring and feedback. This module has been designed with power optimization features to maximize battery life while supporting the real-time functionalities of the Biometric Sensors (200), AI-Based Emotion Detection Engine (300), and LED Display System (400). It enables the device to deliver reliable emotional insights without frequent recharging, enhancing user convenience.
[029] Rechargeable Battery (710): The device is equipped with a high-capacity rechargeable battery (710) that powers all components of the wearable. The battery is chosen for its longevity, compactness, and ability to sustain the device's energy demands throughout extended periods of use. Users can recharge the battery via a standard charging interface, which is designed to be easily accessible, thus promoting convenience and user satisfaction.
[030] Power Optimization Circuit (720): The power optimization circuit (720) manages the device's energy consumption by regulating power distribution to various components based on usage patterns. For instance, the LED Display System (400) may operate at a reduced brightness level in low-intensity emotional states or during inactive periods to conserve energy. Similarly, the biometric sensors (200) may reduce their sampling rate when the user's physiological signals are stable, minimizing power drain without compromising data accuracy. The optimization circuit also intelligently shifts the device to a low-power mode during prolonged inactivity, preserving battery life.
[031] Low-Battery Notifications: To ensure seamless operation, the power management system is integrated with the User Customization Interface (500) to notify users of low battery levels through the mobile app. This notification prompts users to recharge the device before power depletion, preventing disruption in emotional monitoring and feedback.
[032] Safety and Efficiency Features: The power management system includes safety protocols to prevent overheating, overcharging, and short-circuiting. These safeguards enhance device reliability and ensure safe, efficient battery usage under all operating conditions.
[033] Overall, the Power Supply and Battery Management (700) module is designed to balance high performance with energy efficiency, extending operational time and allowing users to experience continuous emotional feedback without frequent recharging. This module supports the device's aim to deliver reliable, real-time insights into emotional states while minimizing interruptions due to power-related issues.
Wireless Connectivity Module (800)
[034] The Wireless Connectivity Module (800) enables seamless data transmission between the wearable device and the User Customization Interface (500) on the user's mobile app. This module allows real-time synchronization of emotional data, customization settings, and system updates, ensuring that users can interact with the device and adjust settings remotely. The connectivity options offered in this module are designed to provide reliable and secure communication without significantly impacting battery life.
[035] Bluetooth Connectivity (810): The primary mode of connection between the wearable and the user's mobile device is Bluetooth (810). Bluetooth connectivity is chosen for its low-power consumption and ability to maintain stable data transfer within short-range distances, suitable for continuous synchronization of biometric data and real-time feedback adjustments. Through Bluetooth, users can quickly access the device's settings, check emotional trends, and receive notifications from the mobile app without requiring physical interaction with the wearable.
[036] Wi-Fi Connectivity (820): For users who may need extended connectivity options, the device is equipped with Wi-Fi connectivity (820) to allow cloud-based data synchronization. This feature enables the Data Storage and Management (1200) system to back up emotional data to a secure cloud environment, offering users access to their emotional history and trends even when away from their mobile device. Wi-Fi connectivity is particularly beneficial in applications requiring comprehensive data analysis or integration with other wellness platforms.
[037] Data Encryption and Security: To safeguard sensitive biometric and emotional data, the connectivity module incorporates data encryption protocols that protect information during wireless transmission. These protocols prevent unauthorized access to emotional data and ensure user privacy. Both Bluetooth and Wi-Fi connections are equipped with industry-standard security measures to maintain the confidentiality and integrity of data shared between the wearable and the mobile app.
[038] Low-Power Connectivity Mode: The wireless module includes a low-power mode that activates during periods of inactivity or low data transfer requirements. This mode reduces the energy consumption of the connectivity components, preserving battery life without compromising data synchronization when real-time updates are not immediately necessary.
[039] Connection Management: The module automatically manages connection priorities based on available networks. Bluetooth is used as the default connection for immediate data access and device customization. When Wi-Fi is available, the device can shift to Wi-Fi connectivity to enable data backup to the cloud, allowing users to access their emotional data remotely and review historical trends.
[040] Through the Wireless Connectivity Module (800), the wearable provides a robust and adaptable data exchange system, facilitating real-time interactions between the device and the user's mobile app. This module enables continuous customization and synchronization, ensuring that users receive a seamless emotional feedback experience with secure, reliable connectivity.
Data Collection Module (900)
[041] The Data Collection Module (900) is responsible for gathering and processing physiological signals in real time from the Biometric Sensors (200). This module serves as the first stage in the emotion detection process, capturing raw biometric data, filtering it, and preparing it for analysis by the AI-Based Emotion Detection Engine (300). The data collection process is designed to operate continuously and efficiently, ensuring that real-time emotional states are accurately recorded without delays or disruptions.
[042] Real-Time Data Acquisition (910): The real-time data acquisition unit (910) is dedicated to collecting continuous data from the biometric sensors, including heart rate variability (HRV), galvanic skin response (GSR), and body temperature. This unit ensures that data is captured at a frequency and resolution that enables accurate emotion detection. The acquisition rate is calibrated to maintain sensitivity to rapid changes in physiological signals, allowing the device to detect even minor fluctuations in emotional intensity.
[043] Signal Processing Unit (920): Once collected, the raw data undergoes initial processing within the signal processing unit (920). This unit filters out noise and artifacts that could arise from environmental factors, physical activity, or sensor limitations, thereby enhancing data accuracy and reliability. The signal processing algorithms are designed to isolate meaningful physiological changes linked to emotional states, discarding irrelevant data to improve processing efficiency and reduce the computational load on the AI engine.
[044] Data Calibration and Synchronization: To enhance the accuracy of emotional detection, the data collection module periodically calibrates the biometric sensors to account for environmental changes or individual variations. Calibration ensures that the baseline readings of each sensor are stable, providing a consistent reference point for detecting deviations associated with emotional states. Additionally, the module synchronizes the data streams from each sensor to provide a comprehensive, time-aligned dataset to the AI-based emotion detection engine.
[045] Low-Power Data Sampling: To optimize battery life, the data collection module includes a low-power data sampling mode that activates during periods of minimal physiological change. In this mode, the module reduces the data acquisition rate, conserving energy while still monitoring the user's baseline state. When the sensors detect significant shifts, the module automatically returns to full sampling mode, ensuring that emotional changes are captured in detail as they occur.
[046] The Data Collection Module (900) thus functions as the foundational layer of the wearable device's emotion detection system, capturing and preparing high-quality biometric data for further processing. By ensuring continuous, accurate data acquisition and efficient signal processing, this module enables the device to provide real-time emotional feedback that is both responsive and reliable.
Emotion Classification Module (1000)
[047] The Emotion Classification Module (1000) is responsible for interpreting the processed data from the Data Collection Module (900) to classify and quantify the user's emotional state. This module works in conjunction with the AI-Based Emotion Detection Engine (300), employing machine learning models to accurately identify specific emotions based on real-time physiological data. The emotion classification process not only categorizes emotions but also evaluates the intensity, providing a nuanced understanding of the user's emotional landscape.
[048] Emotion Intensity Scoring (1010): This component assigns an intensity score to each detected emotional state, reflecting the strength of the user's current emotions. The intensity scoring is based on a combination of factors, such as the degree of deviation in heart rate variability (HRV), the amplitude of galvanic skin response (GSR) signals, and any significant fluctuations in body temperature. The system scales these signals to create a comprehensive intensity score, where higher scores indicate more pronounced emotional states. For instance, a high intensity score might correlate with intense stress or excitement, while a low score could reflect mild relaxation or calmness.
[049] Emotion State Classification (1020): The emotion state classification component categorizes emotions into specific types, such as calmness, stress, happiness, or excitement. The classification is determined by analyzing patterns within the biometric data-distinctive changes in HRV, GSR, and body temperature are mapped to predefined emotional states based on trained machine learning models. By linking physiological data to established emotional markers, this component enables the wearable device to deliver accurate, real-time feedback that reflects the user's emotional state.
[050] Adaptive Learning: The module's classification process is adaptive, allowing it to refine its accuracy based on individual user patterns over time. By continuously learning from the user's physiological responses, the system can adjust its emotional markers to better reflect the user's unique baseline. This personalized adaptation enhances the device's ability to classify emotions accurately, even as individual reactions to stimuli evolve.
[051] Continuous Monitoring and Real-Time Updates: The Emotion Classification Module (1000) operates in real time, providing continuous updates to the LED Display System (400) or Alternative Feedback Modules (600) to ensure that the user receives instant feedback on emotional shifts. As emotional states change, the classification module promptly communicates these updates to the output systems, enabling timely and accurate visual or sensory feedback.
[052] By delivering both intensity scores and specific emotional classifications, the Emotion Classification Module (1000) enhances the functionality of the wearable device, offering users a detailed and responsive emotional feedback system. This module's ability to identify nuanced emotional states and adjust to individual patterns supports the device's mission of promoting real-time emotional awareness and self-regulation.
User Feedback Control (1100)
[053] The User Feedback Control (1100) module empowers users to manage how the wearable device delivers emotional feedback, offering customizable options to adjust the responsiveness and sensitivity of the device based on personal preferences and situational needs. This control module provides flexibility, enabling users to tailor feedback frequency, intensity, and modes, thereby enhancing the device's adaptability across different environments and personal contexts.
[054] Feedback Override Option (1110): The feedback override option allows users to temporarily suppress the device's feedback, pausing visual, haptic, or auditory responses. This feature is particularly useful in environments where users may prefer to keep their emotional state private or where feedback may be distracting, such as in meetings or social gatherings. By using the override option, users can prevent any visible or sensory feedback without deactivating the device's data collection and processing functions.
[055] Feedback Sensitivity Adjustment (1120): The sensitivity adjustment feature enables users to control how readily the device responds to detected emotional changes. Users can increase sensitivity to receive immediate feedback for subtle emotional shifts, or decrease sensitivity to limit feedback to more pronounced changes. This flexibility is especially beneficial for users who may want a gentle, unobtrusive experience or, conversely, a more responsive device that tracks and displays every emotional nuance.
[056] Customized Feedback Modes: Through the User Customization Interface (500), users can select their preferred feedback mode-such as visual (LED), haptic (vibration), or auditory (sound)-based on their environment or specific needs. For instance, users in a quiet setting may prefer vibration feedback over auditory cues. The device seamlessly integrates with the chosen feedback mode, adapting its output to suit the user's circumstances.
[057] Real-Time Adjustments: The User Feedback Control (1100) module supports real-time adjustments through the mobile app, allowing users to make quick changes to feedback settings on demand. This capability enables users to adapt feedback control instantly, responding to situational changes without needing to reset or pause the device's primary functions.
[058] By offering options for feedback override, sensitivity adjustment, and mode selection, the User Feedback Control (1100) module allows users to experience highly personalized emotional feedback. This module makes the device adaptable, user-friendly, and considerate of diverse environmental or personal preferences, ensuring that feedback remains relevant, respectful, and effective in promoting emotional awareness.
Data Storage and Management (1200)
[059] The Data Storage and Management (1200) module is designed to securely store and organize the emotional data collected by the device, allowing users to access both real-time and historical emotional insights. This module ensures that data is readily available for review, analysis, and comparison, promoting long-term emotional awareness and supporting informed self-regulation practices. The storage system is structured to accommodate both on-device storage for immediate access and optional cloud backup for extended historical data management.
[060] Local Data Storage (1210): The device features an on-board storage system, or Local Data Storage (1210), where recently collected emotional data is stored directly on the wearable. This local storage option provides immediate access to recent emotional trends and allows for offline functionality, enabling the device to continue recording and displaying data even without an active wireless connection. Local storage capacity is optimized to maintain detailed records of short-term data, ensuring real-time insights are continuously available.
[061] Cloud Data Backup (Optional) (1220): For users who prefer long-term data retention and more comprehensive historical analysis, the device offers an optional Cloud Data Backup (1220) feature. Through the Wireless Connectivity Module (800), the device can transfer emotional data to a secure cloud environment, where it is safely stored and accessible for extended review. Cloud storage enables users to track emotional patterns over weeks, months, or even years, providing valuable longitudinal insights. Additionally, this data can be synchronized across multiple devices, allowing users to view their emotional history from any connected device with the companion app.
[062] Data Encryption and Security: Recognizing the sensitive nature of biometric and emotional data, the storage and management system includes robust encryption protocols for both local and cloud-stored data. These security measures protect user privacy, ensuring that all stored data is encrypted and accessible only to authorized users. The device adheres to industry-standard security practices to maintain data confidentiality and integrity.
[063] Data Management and Synchronization: The Data Storage and Management (1200) module synchronizes seamlessly with the User Customization Interface (500) and Companion App Features (1300), updating stored data regularly and providing real-time insights in the app. This synchronization allows users to view current emotional states and recent trends instantly. Historical data stored on the cloud is also synchronized, enabling users to analyze emotional trends over time and monitor any patterns in their emotional health and responses.
[064] Automatic Data Deletion: To manage storage capacity efficiently, the module can be set to automatically delete older data from local storage after a specified period, such as 30 days. This option ensures that on-device storage remains optimized for real-time data while long-term records are safely retained in the cloud for historical analysis. Users have the option to adjust data retention periods or disable auto-deletion through the app if they wish to keep a longer record on the device.
[065] The Data Storage and Management (1200) module provides a secure, accessible system for storing and managing the device's emotional data. By enabling both local storage for immediate access and cloud backup for extended data retention, this module enhances the user's ability to gain meaningful insights from their emotional data, supporting personal growth and emotional resilience over time.
Companion App Features (1300)
[066] The Companion App Features (1300) provide an intuitive and user-friendly interface that enhances the functionality of the Emotion-Sensitive LED Wearable by enabling comprehensive control, customization, and data visualization. Accessible through the user's mobile device, the app allows for seamless interaction with the wearable's feedback mechanisms, detailed tracking of emotional trends, and adjustments to the device settings. Through these features, the app supports users in gaining valuable insights into their emotional health, encouraging sustained emotional awareness and self-regulation.
[067] Real-Time Emotional Trend Display (1310): This feature provides users with a live visualization of their current emotional state, including the intensity and type of emotion detected. The real-time trend display synchronizes with the Emotion Classification Module (1000) and LED Display System (400), allowing users to view a graphical representation of their emotional data as it is captured. The display also updates in sync with any changes in emotional state, ensuring that users can monitor their emotions continuously and respond proactively to shifts in emotional intensity.
[068] Historical Emotional Data Visualization (1320): This feature offers users an in-depth look at their emotional patterns over time. Through interactive graphs and timelines, users can review emotional trends across different days, weeks, or months, enabling them to identify recurring patterns, triggers, or periods of heightened emotional intensity. The historical visualization is generated from data stored in the Data Storage and Management (1200) module, combining local data for recent trends and cloud data for long-term analysis. This feature promotes reflective insights, allowing users to understand how their emotions vary in response to different situations and environmental factors.
[069] System Settings and Preferences (1330): The system settings and preferences feature enables users to configure various aspects of the wearable, including feedback modes, sensitivity adjustments, and notification preferences. Through this interface, users can access settings for the User Feedback Control (1100), such as enabling or disabling specific feedback types (LED, haptic, or auditory), adjusting feedback sensitivity, and setting brightness and intensity levels. Additionally, users can set preferences for data retention, privacy, and data synchronization, customizing the wearable to fit their lifestyle and privacy requirements.
[070] Personalized Recommendations: Leveraging data from the AI-Based Emotion Detection Engine (300), the app can provide personalized recommendations to help users manage their emotional states more effectively. For example, if the device frequently detects high stress, the app might suggest relaxation techniques or activities to lower stress. These recommendations support users in proactively managing their emotional health, integrating the wearable as an active tool for emotional well-being.
[071] Data Privacy and Security Settings: Recognizing the importance of data privacy, the app includes settings that allow users to manage their data security preferences. Users can control data sharing permissions, encryption settings, and access levels for cloud storage, ensuring that all emotional data is managed according to personal privacy standards. These settings work in conjunction with the Data Storage and Management (1200) module, ensuring secure storage and access to sensitive data.
[072] The Companion App Features (1300) provide users with comprehensive access to real-time and historical emotional insights, customizable settings, and personalized recommendations. By enabling easy control over the wearable's functionality and presenting detailed emotional data, the app transforms the wearable into an invaluable tool for emotional awareness and personal growth, empowering users to engage actively with their emotional well-being.
Various Embodiments of the Invention
[073] The present invention encompasses a range of embodiments to enhance adaptability and suit different user needs, environments, and applications. While the primary embodiment is an Emotion-Sensitive LED Wearable Device (100) with real-time emotional feedback via an LED display system, alternative embodiments enable emotional awareness feedback through other sensory channels and integrations with different technologies. These embodiments aim to expand the functionality of the invention, making it versatile for use in varied scenarios and by different user groups.
LED-Based Wearable Embodiment
[074] In the primary embodiment, the wearable device uses an LED Display System (400) to visually represent the user's emotional state. The system varies color, brightness, and pulsing frequency to convey different emotions and intensities, offering immediate, accessible feedback. This embodiment is suited for users who prefer a discreet, yet effective, visual cue to monitor and manage their emotional well-being. Through the User Customization Interface (500), users can adjust the LED display settings to their preferences, enhancing personalization and usability.
Haptic Feedback Embodiment
[075] In another embodiment, the wearable incorporates a Haptic Feedback Mechanism (610) from the Alternative Feedback Modules (600). This haptic-based approach provides feedback through vibrations of varying intensity and patterns that correlate with different emotional states, such as rapid vibrations for stress or steady pulses for calmness. This embodiment is particularly beneficial in settings where visual cues may be less practical or desired, such as during meetings or activities requiring focus. The haptic feedback can be customized via the companion app, allowing users to control the intensity and pattern of vibrations based on personal preference.
Auditory Feedback Embodiment
[076] A further embodiment includes an Auditory Feedback Mechanism (620) within the Alternative Feedback Modules (600), offering sound-based feedback that aligns with detected emotional states. This embodiment could produce soft chimes, alert tones, or even voice-based guidance using conversational AI, providing gentle reminders of the user's emotional state. For example, a calm state might be indicated by a soothing chime, while a voice prompt might suggest relaxation techniques in response to elevated stress. This embodiment is suitable for users who benefit from auditory reinforcement or for situations where visual feedback is impractical.
Smartphone-Based Feedback Embodiment
[077] Another embodiment focuses on delivering emotional feedback through a Smartphone-Based Visual Interface within the User Customization Interface (500). In this approach, the user's emotional state is displayed directly on the mobile app via color-coded notifications or graphical representations, without relying on the LED display or other on-device feedback mechanisms. This embodiment is suitable for users who prefer subtle feedback or wish to check their emotional state discreetly without external indicators. Additionally, it allows for more detailed graphical data displays and trend visualizations, providing comprehensive emotional insights directly on the smartphone.


Wearable Integration in Clothing or Accessories
[078] This embodiment involves integrating the emotional feedback system into Smart Clothing or Accessories rather than a standalone pendant. Here, the LED Display System (400) could be embedded into fabric, wristbands, or other accessories, allowing the color-coded feedback to blend seamlessly with daily attire. This design option turns the device into a fashionable item, making emotional feedback part of the user's personal style. This embodiment would appeal to users who prefer a subtle, integrated approach to emotional feedback.
Augmented Reality (AR) and Virtual Reality (VR) Integration Embodiment
[079] In this advanced embodiment, the emotional detection system integrates with AR or VR devices, enabling feedback within virtual environments. Through this integration, the user's emotional state could influence their avatar's appearance or the environmental colors in VR, providing real-time visual feedback within an immersive experience. This embodiment is particularly applicable in virtual collaboration or gaming environments, where understanding emotional cues enhances user interactions.
Software-Only Embodiment for Emotional Awareness Apps
[080] Another embodiment of the invention is a Software-Only Solution that operates as an emotional awareness app, utilizing smartphone sensors or external biometric devices for data input. This version performs emotional detection and feedback without the wearable hardware, offering emotional insights, trend visualizations, and suggestions for managing emotional states based on processed data. The software-only embodiment would be suitable for users who prefer a non-wearable solution or for applications where emotional tracking needs to be more integrated with other digital wellness tools.
Combined Embodiment for Multi-Mode Feedback
[081] In this embodiment, the device incorporates multiple feedback mechanisms-LED, haptic, and auditory-within a single wearable. Users can select or switch between feedback types based on context, environment, or personal preference, providing maximum flexibility in how emotional insights are delivered. The User Customization Interface (500) enables users to configure settings for each mode, allowing for a customizable, adaptable experience that caters to a broad range of user preferences and scenarios.
[082] These various embodiments highlight the versatility of the present invention, demonstrating its adaptability to different user needs, environments, and applications. Whether in a wearable LED form, haptic or auditory feedback, smartphone-only approach, or integration into AR/VR, the invention's multi-faceted design makes it a comprehensive tool for real-time emotional awareness, suited for diverse user preferences and lifestyle choices.
Steps Involved in the System
[083] The Emotion-Sensitive LED Wearable system operates through a series of integrated steps that work together to detect, analyze, and display real-time emotional feedback to the user. The following steps outline the full process, from data acquisition to emotional feedback delivery, ensuring seamless and continuous monitoring of the user's emotional state.
Step 1: Data Acquisition
[084] The system begins with data acquisition from the Biometric Sensors (200), which include the Heart Rate Variability (HRV) Sensor (210), Galvanic Skin Response (GSR) Sensor (220), and Body Temperature Sensor (230). These sensors collect physiological data that corresponds with various emotional states, providing the foundational inputs for emotion detection. The Data Collection Module (900) ensures that this data is gathered continuously, capturing even subtle shifts in physiological signals.
Step 2: Signal Processing
[085] The raw data acquired from the biometric sensors is then processed by the Signal Processing Unit (920) within the Data Collection Module (900). In this step, the system filters out noise and environmental artifacts, refining the data to ensure accuracy. This processed data is then synchronized across different sensor inputs to create a unified dataset for emotional analysis.
Step 3: Data Calibration and Baseline Adjustment
[086] To improve accuracy, the system periodically calibrates each sensor and adjusts baseline readings in the Data Collection Module (900). This calibration accounts for user-specific variations and environmental factors, helping to ensure that changes in the sensor data genuinely reflect emotional states rather than external influences or sensor drift.
Step 4: Emotion Detection and Classification
[087] The processed and calibrated data is then transmitted to the AI-Based Emotion Detection Engine (300), where it undergoes real-time analysis. Using the Supervised Learning Model (310) and Neural Network (320), the AI engine interprets physiological data patterns to classify emotions. The system generates an Emotion Intensity Score (1010) and an Emotion State Classification (1020), identifying the specific emotional state (e.g., calm, stressed, happy) and its intensity level. The AI engine's adaptive learning capabilities allow it to refine this classification over time based on individual user data.
Step 5: Contextual Analysis
[088] The Contextual Data Processing Module (330) integrates situational information, such as the time of day, location, or activity, to provide additional context for the detected emotions. By contextualizing the physiological signals, the system can adjust its interpretation to better align with the user's environment, offering more accurate and relevant emotional insights.
Step 6: Real-Time Emotional Feedback Display
[089] Based on the classified emotional state and intensity score, the system generates real-time feedback through the LED Display System (400) or Alternative Feedback Modules (600). For LED feedback, the system adjusts color, brightness, and pulsing frequency to visually represent the user's emotional state. Alternative feedback options, such as Haptic Feedback (610) or Auditory Feedback (620), may be used based on the user's preferences and current environment. These feedback mechanisms are configured to reflect both the type and intensity of the user's emotional state, providing immediate and intuitive feedback.
Step 7: User Customization and Adjustments
[090] Through the User Customization Interface (500) on the companion app, users can personalize the feedback settings, including color mappings, intensity levels, and feedback modes. This customization step allows users to align the feedback display with their preferences, making the emotional feedback more meaningful and practical in different contexts. Users can also adjust the sensitivity of the device to respond to subtle or only more pronounced emotional changes as desired.
Step 8: Data Storage and Synchronization
[091] The system stores the emotional data in the Data Storage and Management (1200) module, which includes Local Data Storage (1210) for immediate data access and Cloud Data Backup (1220) for long-term retention. The system automatically synchronizes data with the companion app, allowing users to view their emotional trends in real-time as well as historically.
Step 9: Historical Data Visualization and Analysis
[092] Using the Companion App Features (1300), the system enables users to view historical emotional data through visualizations such as graphs and timelines. The Historical Emotional Data Visualization (1320) feature helps users identify patterns, trends, and emotional triggers over time, supporting self-reflection and emotional regulation. This step is crucial for users who wish to analyze how their emotions fluctuate over days, weeks, or months.
Step 10: Adaptive Learning and Continuous Improvement
[093] Finally, the system incorporates Adaptive Learning through the AI-Based Emotion Detection Engine (300), adjusting its detection algorithms based on individual emotional patterns and physiological responses. As more data is collected and processed, the AI engine refines its classification models to reflect the user's unique emotional baselines more accurately. This continuous learning cycle improves the precision and relevance of emotional feedback over time, enhancing the system's overall effectiveness.
[094] Together, these steps form an integrated, end-to-end process that enables the Emotion-Sensitive LED Wearable to deliver accurate, real-time emotional feedback while adapting to the user's specific needs. This streamlined workflow ensures that the device remains an effective tool for emotional awareness, fostering both immediate self-regulation and long-term emotional growth.
ADVANTAGES
[095] The Emotion-Sensitive LED Wearable offers several distinct advantages that make it a valuable tool for real-time emotional awareness, personalization, and adaptability across different settings. Here are the primary advantages of this invention:
1. Real-Time Emotional Awareness and Feedback
[096] The wearable device provides instantaneous emotional feedback, allowing users to monitor and respond to their emotional states as they occur. The LED Display System (400) delivers visual cues through color, brightness, and pulsing frequency, offering intuitive and immediate insights into the user's emotional state. This real-time awareness empowers users to regulate emotions proactively, rather than relying on post-event analysis.
2. High Accuracy Through Multi-Sensor Integration
[097] The integration of Heart Rate Variability (HRV), Galvanic Skin Response (GSR), and Body Temperature sensors ensures a comprehensive physiological profile for accurate emotion detection. Each sensor captures unique physiological indicators of emotional states, and the AI-Based Emotion Detection Engine (300) processes this data to classify emotions with high accuracy. This multi-sensor approach ensures that the feedback is both reliable and sensitive to subtle emotional shifts.
3. Personalization and Adaptability
[098] Through the User Customization Interface (500), users can adjust various feedback settings, including color mappings, intensity levels, and feedback types (LED, haptic, or auditory). This flexibility allows the device to adapt to each user's preferences, making the emotional feedback experience highly personalized. Additionally, the Emotion Classification Module (1000) adapts to each user's unique physiological baseline over time, further enhancing accuracy and relevance.

4. Versatile Feedback Options for Different Environments
[099] The invention includes Alternative Feedback Modules (600), such as haptic and auditory feedback, in addition to the LED display. This versatility allows users to choose feedback types that suit their environment and personal needs. For instance, haptic feedback may be ideal in social settings where discreet emotional cues are preferred, while LED feedback may be suitable for private environments where visual cues are beneficial.
5. Long-Term Emotional Insight and Self-Reflection
[100] The Data Storage and Management (1200) module, with both local storage and optional cloud backup, enables users to track emotional trends over time. Through the Companion App Features (1300), users can view historical visualizations of their emotional data, allowing them to observe patterns, triggers, and trends. This long-term insight promotes self-reflection and supports emotional growth by helping users understand their emotional responses in various contexts.
6. Adaptive Learning for Enhanced Accuracy
[101] The system's Adaptive Learning feature within the AI-Based Emotion Detection Engine (300) continuously refines its detection model based on individual user data. This adaptive capacity allows the device to become increasingly accurate in classifying emotions as it learns the user's unique emotional patterns, providing more precise and meaningful feedback over time.
7. Data Privacy and Security
[102] The device incorporates Data Encryption and Security measures to protect user privacy. Sensitive emotional and biometric data is encrypted during transmission and securely stored on the device or cloud. This emphasis on privacy ensures that users can confidently rely on the device without concerns about unauthorized access to their emotional information.
8. Non-Intrusive and Continuous Monitoring
[103] The wearable operates passively, continuously collecting and analyzing biometric data without requiring user intervention. This non-intrusive approach makes it easy for users to incorporate the device into daily life without feeling disrupted by active input requirements. The device's power optimization features further support continuous operation with minimal battery drain.
9. Multi-Context Application and Adaptability
[104] With its range of embodiments, including LED, haptic, and auditory feedback, as well as integration with AR/VR environments, the wearable is highly adaptable. It serves diverse user groups across varied applications, from mental health and wellness to productivity and social interaction. This flexibility makes the device suitable for multiple scenarios, whether in personal, professional, or immersive environments.
[105] These advantages collectively make the Emotion-Sensitive LED Wearable a powerful tool for real-time emotional monitoring, personalized feedback, and long-term emotional growth. By addressing both immediate and reflective aspects of emotional awareness, the device promotes well-being, emotional resilience, and enhanced self-understanding for a wide range of users.
Enablement of the Emotion-Sensitive LED Wearable
[106] The Emotion-Sensitive LED Wearable is fully enabled by a set of carefully integrated components, design methodologies, and processing algorithms that work together to detect, analyze, and provide real-time feedback on the user's emotional state. Each component, from data acquisition through biometric sensors to real-time display through LED or other feedback mechanisms, has been developed with specific considerations to achieve the intended function effectively. The following section explains how each component contributes to enabling the invention, ensuring that a skilled person in the field could implement and utilize the invention.
1. Biometric Sensor Integration for Accurate Data Collection
[107] The enablement of this invention begins with the integration of multiple Biometric Sensors (200), each chosen for its sensitivity to physiological signals associated with emotional states. The Heart Rate Variability (HRV) Sensor (210), Galvanic Skin Response (GSR) Sensor (220), and Body Temperature Sensor (230) capture different physiological markers that together provide a robust dataset for emotion analysis. The combination of these sensors enables comprehensive, real-time physiological monitoring, which is essential for accurate emotion detection.
[108] These sensors are selected for their compatibility with wearable technology, ensuring that they can operate continuously without user intervention. The sensors' high sensitivity and low power requirements make them suitable for long-term monitoring, supporting the device's objective of continuous emotional tracking.
2. Data Processing for Reliable Signal Interpretation
[109] Raw data collected from the sensors is processed by the Signal Processing Unit (920) within the Data Collection Module (900). This module filters out noise and artifacts that may result from physical activity, environmental conditions, or other external factors. By refining the data through filtering and synchronization, the signal processing unit ensures that only relevant, high-quality data is passed to the AI-Based Emotion Detection Engine (300), enabling the engine to classify emotions with greater accuracy and reliability.
3. Machine Learning for Emotion Detection and Classification
[110] The AI-Based Emotion Detection Engine (300), which includes a Supervised Learning Model (310) and Neural Network (320), processes the filtered biometric data to classify emotional states. Trained on large datasets linking physiological signals to emotional responses, the AI engine recognizes distinct patterns in HRV, GSR, and body temperature that correspond to emotions such as calmness, stress, happiness, and excitement. The neural network and supervised learning model enable the system to classify emotions in real time, adjusting classifications as new data becomes available.
[111] Adaptive Learning functionality within the AI engine allows it to learn from the individual user's unique physiological responses, adapting the model over time to better align with the user's baseline. This personalization improves accuracy and ensures that the feedback remains relevant and meaningful.
4. Real-Time Emotional Feedback Through Customizable Display
[112] The LED Display System (400) provides an intuitive, customizable interface that translates emotional data into visual cues using color, brightness, and pulsing frequency. The LED Color Mapping Module (410) maps each detected emotion to a specific color, while Brightness Control (420) and Pulsing Frequency Control (430) allow for additional layers of feedback to represent emotional intensity.
[113] For environments where visual feedback may be less practical, the Alternative Feedback Modules (600) provide options for haptic or auditory feedback. These modules enable users to receive feedback that aligns with their surroundings and personal preferences, further enhancing the device's usability and adaptability.
5. User Customization and Control
[114] Through the User Customization Interface (500) on the companion app, users can personalize feedback settings, including color mappings, feedback modes, and sensitivity. This customization feature is fully enabled through software integration, allowing users to interact directly with the device, set preferences, and adjust feedback controls based on their individual needs. The app's interactive design makes customization straightforward and accessible, ensuring that users can easily tailor the device to suit various contexts.
6. Data Storage and Long-Term Analysis
[115] The Data Storage and Management (1200) module, featuring Local Data Storage (1210) and Cloud Data Backup (1220), ensures that emotional data is securely retained for both real-time access and long-term analysis. This storage enables users to reflect on emotional trends over time, while cloud backup provides additional storage capacity and accessibility across devices.
[116] The Companion App Features (1300), such as Real-Time Emotional Trend Display (1310) and Historical Emotional Data Visualization (1320), allow users to access stored data, providing visualizations and insights into their emotional patterns. These features are enabled through data synchronization with the storage module, ensuring that users can analyze emotional data seamlessly.
7. Power Optimization for Continuous Operation
[117] To support continuous operation, the Power Supply and Battery Management (700) module includes a Rechargeable Battery (710) and Power Optimization Circuit (720). These components are specifically chosen to provide the necessary power while maximizing battery life through optimized energy distribution. The power management system includes a low-power mode that activates during periods of minimal data change, preserving battery life without sacrificing the quality of emotional monitoring.
8. Secure Wireless Connectivity for Real-Time Synchronization
[118] The Wireless Connectivity Module (800), featuring Bluetooth Connectivity (810) and Wi-Fi Connectivity (820), enables real-time data synchronization between the wearable device and the companion app. Bluetooth provides low-power data transfer within close range, while Wi-Fi connectivity enables cloud data backup and remote access. This connectivity ensures that users have real-time access to both current and historical data, enhancing the system's overall functionality and accessibility.
9. Robust Data Security for Privacy Protection
[119] Recognizing the sensitivity of emotional data, the system is equipped with comprehensive Data Encryption and Security protocols. These protocols ensure that biometric and emotional data are protected both during transmission (via wireless connectivity) and storage (locally and in the cloud). The security framework adheres to industry standards, providing users with confidence in the privacy of their data.
Enabling Functionality and Usability
[120] The coordinated operation of these components ensures that the Emotion-Sensitive LED Wearable performs its intended function effectively, providing real-time emotional feedback, continuous monitoring, and adaptability to individual preferences. By integrating hardware, machine learning, and customizable feedback, the invention is enabled as a practical, accessible tool for emotional awareness and self-regulation, ready for implementation by a skilled practitioner in the field.




BRIEF DESCRIPTION OF THE DRAWING
Figure 1 illustrates the overall architecture of the Emotion-Sensitive LED Wearable Device for Real-Time Emotional Feedback.
Numbering Scheme for Elements of the Invention: numbering scheme:
1. Wearable Pendant Device - (100)
2. Biometric Sensors - (200)
o Heart Rate Variability (HRV) Sensor - (210)
o Galvanic Skin Response (GSR) Sensor - (220)
o Body Temperature Sensor - (230)
3. AI-Based Emotion Detection Engine - (300)
o Supervised Learning Model - (310)
o Neural Network - (320)
o Contextual Data Processing Module - (330)
4. LED Display System - (400)
o LED Color Mapping Module - (410)
o Brightness Control - (420)
o Pulsing Frequency Control - (430)
5. User Customization Interface (Mobile App) - (500)
o Color Mapping Customization - (510)
o Intensity Level Adjustment - (520)
6. Alternative Feedback Modules - (600)
o Haptic Feedback Mechanism - (610)
o Auditory Feedback Mechanism - (620)
7. Power Supply and Battery Management - (700)
o Rechargeable Battery - (710)
o Power Optimization Circuit - (720)
8. Wireless Connectivity Module - (800)
o Bluetooth Connectivity - (810)
o Wi-Fi Connectivity - (820)
9. Data Collection Module - (900)
o Real-Time Data Acquisition - (910)
o Signal Processing Unit - (920)
10. Emotion Classification Module - (1000)
o Emotion Intensity Scoring - (1010)
o Emotion State Classification - (1020)
11. User Feedback Control - (1100)
o Feedback Override Option - (1110)
o Feedback Sensitivity Adjustment - (1120)
12. Data Storage and Management - (1200)
o Local Data Storage - (1210)
o Cloud Data Backup (Optional) - (1220)
13. Companion App Features - (1300)
o Real-Time Emotional Trend Display - (1310)
o Historical Emotional Data Visualization - (1320)
o System Settings and Preferences - (1330)
, Claims:CLAIMS
1. A wearable pendant device for providing real-time emotional feedback, comprising:
a. a plurality of biometric sensors (200) configured to collect physiological data associated with the emotional state of a user, the biometric sensors including a Heart Rate Variability (HRV) sensor (210), a Galvanic Skin Response (GSR) sensor (220), and a Body Temperature sensor (230);
b. an AI-based emotion detection engine (300) that processes the physiological data from the biometric sensors, the AI-based emotion detection engine comprising a supervised learning model (310), a neural network (320), and a contextual data processing module (330) for classifying the emotional state and assigning an intensity score;
c. a feedback module comprising an LED display system (400) configured to visually represent the classified emotional state through an LED color mapping module (410), brightness control (420), and pulsing frequency control (430);
d. alternative feedback modules (600), including a haptic feedback mechanism (610) and an auditory feedback mechanism (620), providing additional sensory feedback modes to represent the emotional state;
e. a user customization interface (500) accessible via a mobile app, enabling the user to adjust feedback settings including color mappings, intensity levels, feedback type, and sensitivity;
f. a data collection module (900) comprising a real-time data acquisition unit (910) and a signal processing unit (920) to refine the physiological data before classification;
g. an emotion classification module (1000) comprising an emotion intensity scoring component (1010) and an emotion state classification component (1020);
h. a data storage and management module (1200) configured to store both real-time and historical emotional data, including local data storage (1210) for immediate access and optional cloud data backup (1220) for long-term analysis;
i. a power supply and battery management module (700), including a rechargeable battery (710) and a power optimization circuit (720) to selectively activate biometric sensors and feedback mechanisms based on usage patterns; and
j. a wireless connectivity module (800) comprising Bluetooth connectivity (810) and Wi-Fi connectivity (820) to synchronize emotional data between the wearable device and the mobile app, enabling real-time and historical data access.
2. The wearable pendant device of Claim 1, wherein the AI-based emotion detection engine (300) further includes an adaptive learning model that adjusts baseline physiological readings based on individual user patterns over time, refining the classification accuracy of emotional states.
3. The wearable pendant device of Claim 1, wherein the LED display system (400) is configurable through the user customization interface (500), allowing users to select color mappings for specific emotional states, set brightness levels, and control the pulsing frequency of the LED display.
4. The wearable pendant device of Claim 1, wherein the haptic feedback mechanism (610) provides vibrational patterns that vary in frequency and intensity to represent different emotional states, with high-frequency vibrations for heightened emotional arousal and steady, low-frequency vibrations for calm states.
5. The wearable pendant device of Claim 1, wherein the auditory feedback mechanism (620) provides sound-based cues, including chimes or spoken prompts, based on the classified emotional state, enabling audio feedback for emotions such as stress, excitement, or calmness.
6. The wearable pendant device of Claim 1, wherein the data storage and management module (1200) supports automatic data synchronization with the mobile app, providing users with access to historical emotional data visualizations, including real-time emotional trend display (1310) and historical emotional data visualization (1320).
7. The wearable pendant device of Claim 1, further comprising a user feedback control (1100) accessible via the mobile app, including a feedback override option (1110) to temporarily suppress feedback and a feedback sensitivity adjustment (1120) to control the responsiveness of the feedback module to emotional changes.
8. The wearable pendant device of Claim 1, wherein the data collection module (900) is configured to process real-time physiological data through a real-time data acquisition unit (910) and a signal processing unit (920) to remove noise and prepare data for classification.
9. The wearable pendant device of Claim 1, wherein the wireless connectivity module (800) enables Bluetooth connectivity (810) for low-power, close-range synchronization and Wi-Fi connectivity (820) for remote data backup and access.
10. A method for providing real-time emotional feedback in a wearable pendant device, comprising:
a. acquiring physiological data from a plurality of biometric sensors, including an HRV sensor (210), a GSR sensor (220), and a body temperature sensor (230);
b. processing the acquired physiological data through an AI-based emotion detection engine (300) comprising a supervised learning model (310) and a neural network (320), classifying the emotional state and assigning an intensity score;
c. adjusting color, brightness, and pulsing frequency of an LED display system (400) to visually represent the classified emotional state based on the intensity of the detected emotional state;
d. providing alternative feedback via haptic (610) and auditory (620) feedback mechanisms, depending on user-selected preferences;
e. storing the acquired physiological data and classified emotional states in a data storage and management module (1200) for real-time and historical access; and
f. enabling user customization of feedback settings, including color mapping, feedback type, and sensitivity adjustments, through a user customization interface (500) accessible via a mobile app.
11. The method of Claim 10, wherein the AI-based emotion detection engine (300) further incorporates adaptive learning through a contextual data processing module (330) to refine classification accuracy based on individual user patterns and environmental contexts.
12. The method of Claim 10, further comprising the step of synchronizing emotional data between the wearable device and a mobile application via a wireless connectivity module (800), including Bluetooth connectivity (810) and Wi-Fi connectivity (820), allowing for real-time and historical access to emotional data and trend visualization.
13. The method of Claim 10, further comprising providing historical emotional data visualization (1320) through the companion app, enabling the user to analyze emotional patterns over time.

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