Methods and systems for real-time skin health monitoring and wellness assessment

Abstract

Embodiments herein disclose a system and method for AI-driven real-time skin health monitoring and wellness assessment, utilizing advanced AI algorithms to analyze skin characteristics such as hydration, stress markers, and nutritional deficiencies. The method comprises capturing data comprising high-resolution images and live video via an image-capturing module, preprocessing the image for enhancing quality thereof, and analyzing the data with an AI analysis module to detect skin features and provide health insight, A contextual data integration module combines the analyzed data with metadata, while an edge computing processor ensures low-latency, on-device analysis and data privacy. The method includes providing real-time feedback and personalized wellness recommendation via a user-friendly interface. Using standard devices like smartphones. This method offers accessible, comprehensive health monitoring, empowering users to manage their well-being with accurate, personalized insights while ensuring data security.

Description

METHODS AND SYSTEMS FOR REAL-TIME SKIN HEALTH MONITORING AND WELLNESS ASSESSMENT FIELD OF THE INVENTION

[0001] This invention relates to the field of health monitoring systems, and more particularly to a method and an Artificial Intelligence (AI) system for real-time skin health monitoring and general wellness assessment via AI routines. BACKGROUND OF THE INVENTION

[0002] Skin, being the largest and most visible organ, often reflects underlying health conditions such as dehydration, stress, and nutritional deficiencies. However, there is currently no widely available, user-friendly system that leverages real-time skin analysis for assessing general wellness indicators.

[0003] Current health monitoring solutions focus on diagnosing diseases rather than identifying early warning signs of potential health issues. Many individuals lack access to tools that allow them to proactively manage their general health based on subtle changes in skin characteristics.

[0004] Most existing diagnostic tools require expensive equipment, specialized expertise, or clinical settings, making them inaccessible for everyday use.

[0005] While some mobile applications analyze skin for dermatological conditions like acne or melanoma, they fail to consider broader health implications reflected through the skin, such as hydration levels, stress markers, or signs of nutrient deficiencies.

[0006] Furthermore, many conventional tools rely on cloud-based processing, raising concerns about data privacy and security, particularly for sensitive health-related information.

[0007] Several existing technologies also attempt to analyze skin conditions, but their scope and functionality are limited. AI-based dermatological apps focus on detecting skin diseases such as acne, eczema, or melanoma using image-based analysis.

[0008] Examples include tools like SkinVision and First Derm. However, these apps do not extend their functionality to assess general wellness metrics.

[0009] Wearable technology devices like smartwatches and fitness trackers measure health indicators such as heart rate and activity levels. While helpful, these devices do not utilize skin analysis as a primary source for monitoring general health.

[0010] Medical diagnostic tools and high-end medical equipment such as spectrophotometers or specialized imaging systems are used for detailed skin analysis. However, these methods are costly, require professional operators, and are unsuitable for real-time, everyday monitoring.

[0011] Many individuals rely on subjective judgment or visual inspection of their skin to identify issues, which often leads to delayed action and inaccurate assessments.

[0012] The present invention solves these issues by offering proactive wellness monitoring. Real-time analysis of hydration, stress, and nutrition-related skin changes allow users to make timely adjustments to their lifestyle or seek medical advice.

[0013] The system leverages everyday devices, such as mobile phones or webcams, eliminating the need for expensive equipment. OBJECT OF THE INVENTION

[0014] The main objective of the invention is to disclose methods and a system for providing a user-friendly, non-invasive, and real-time solution for skin health monitoring and wellness assessment via Artificial Intelligence (AI).

[0015] Yet another objective of the present invention is to enable users to monitor skin health indicators such as hydration, stress, and nutrition levels in real-time using everyday devices like smartphones and webcams.

[0016] Yet another objective of the present invention is to act as an early warning system by identifying subtle changes in skin condition, allowing users to take timely actions.

[0017] Another objective of the present invention is to make health monitoring affordable and accessible through the use of widely available devices, eliminating the need for specialized equipment.

[0018] Another objective of the present invention is to offer holistic health assessments that go beyond traditional dermatological evaluations.

[0019] Another objective of the present invention is to ensure data privacy by using edge computing for on-device analysis.

[0020] Another objective of the present invention is to provide an intuitive interface that guides users through the process and presents results clearly.

[0021] Lastly, another objective of the present invention is to implement a continuous learning module for adapting to new user patterns and improving accuracy over time. SUMMARY OF THE INVENTION

[0022] In an aspect of the present invention, a method for real-time skin health monitoring and wellness assessment involves capturing images and live video of the skin of a user using an image capturing module, preprocessing the captured images with a preprocessing module to enhance image quality, analyzing the images with an AI analysis module to detect skin characteristics and integrate contextual metadata, and providing real-time feedback and personalized wellness recommendations through an output module.

[0023] Further, the method emphasizes low-latency processing and enhanced data privacy by performing preprocessing and analysis locally on the user device using edge computing.

[0024] In an aspect of the present invention, the Artificial Intelligence (AI)-driven real-time skin health monitoring and general wellness assessment system comprises several key components: an image capturing module to capture high-resolution images and live video of the skin of a user, a preprocessing module to enhance image quality, an AI analysis module to detect skin characteristics and provide health insights, a contextual data integration module to combine analyzed image data with contextual metadata, an edge computing processor for on-device analysis ensuring low-latency processing and data privacy, and an output module to present real-time feedback and wellness recommendations through a user interface.

[0025] Further, the system allows users to receive accurate, personalized, and actionable health insights based on their skin characteristics in real time. DESCRIPTION OF THE DRAWINGS

[0026] The advantages and features of the present invention will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawings, in which:

[0027] FIG. 1 depicts an Artificial Intelligence (AI) based system for real-time skin health monitoring and general wellness assessment, according to embodiments as disclosed herein;

[0028] FIG. 2 depicts a preprocessing module included in the system, according to embodiments as disclosed herein;

[0029] FIG.3A shows the raw input image, exemplifying the state before any preprocessing techniques have been applied, and FIG. 3B illustrates the impact of the AI-based preprocessing techniques applied to the raw image, according to embodiments disclosed herein;

[0030] FIG. 4 depicts an AI analysis module included in the system, according to embodiments as disclosed herein;

[0031] FIG.5, FIG.6, and FIG.7 depict example use cases of the AI based system, according to the embodiments disclosed herein;

[0032] FIG. 8 provides a flowchart outlining the steps involved in the AI-driven real-time skin health monitoring system, according to embodiments as disclosed herein; and

[0033] FIG. 9 is a flowchart depicting a method for real-time skin health monitoring and general wellness assessment, according to embodiments as disclosed herein. Like numerals denote like elements throughout the figures. DESCRIPTION OF THE INVENTION

[0034] The exemplary embodiments described herein detail for illustrative purposes are subjected to many variations. It should be emphasized, however, that the present invention is not limited to as disclosed.

[0035] It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but these are intended to cover the application or implementation without departing from the scope of the present invention.

[0036] Specifically, the following terms have the meanings indicated below.

[0037] The terms “a” and “an” herein do not denote a limitation of quantity but rather denote the presence of at least one of the referenced items.

[0038] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.

[0039] The inventive aspects of the invention along with various components and engineering involved will now be explained with reference to Figs. 1-9 herein.

[0040] FIG. 1 depicts an AI based system 100 (referred to as “system 100”) for real-time skin health monitoring and general wellness assessment. The system 100 comprises an image capturing module 110, a preprocessing module 120, an Artificial Intelligence (AI) analysis module 130, a contextual data integration module 140, an edge computing processor 150, and an output module 160. The features of the system 100 elements will now be explained as below.

[0041] The image capturing module 110 captures raw visual data and sends it to the preprocessing module 120. The preprocessing module 120 is configured to enhance the quality of the data and forwards it to the AI analysis module 130.

[0042] In various embodiments, the AI analysis module 130 extracts features and insights from the data and passes them to the contextual data integration module 140. The contextual data integration module 140 adds relevant contextual information and sends the integrated data to the edge computing processor 150.

[0043] The edge computing processor 150 processes the data in real-time and sends the results to the output module 160. The output module 160 presents the final processed data to the user device, completing the data processing cycle.

[0044] The image capturing module 110 comprises high-resolution cameras and sensors capable of capturing detailed images and live video feeds of the user's skin. Examples of the image capturing module 110 include, but are not limited to, smartphone cameras, DSLR and mirrorless cameras, webcams with high-resolution sensors, and specialized medical imaging cameras.

[0045] The image capturing module 110 is equipped with advanced sensors to detect various parameters such as ambient light, proximity, and movement. These sensors optimize the image capture process and ensure consistent quality.

[0046] The image capturing module 110 utilizes an autofocus mechanism to automatically adjust the lens position for sharp and focused images and incorporates Optical Image Stabilization (OIS) to reduce blur caused by camera shake.

[0047] The image capturing module 110 supports High Dynamic Range (HDR) imaging to enhance the contrast and color accuracy of captured images, making fine details more visible.

[0048] These features and functionalities ensure that the image capturing module 110 can effectively acquire high-quality visual data for accurate analysis by the system 100.

[0049] The image capturing module 110 is also responsible for acquiring the raw visual data from the user, ensuring the images are of sufficient quality for analysis.

[0050] In an embodiment of the present invention, the image capturing module 110 may have image storage module. In this embodiment, image storage module may directly store the previously captured images therein for pre-processing as per various embodiments of the present invention.

[0051] The captured images are then sent to the pre-processing module 120 for further enhancement and preparation.

[0052] The preprocessing module 120 includes hardware and software components designed for data cleaning, noise reduction, contrast adjustment, and normalization.

[0053] Examples of the preprocessing module 120 include, but are not limited to, image normalization software, noise reduction filters, contrast adjustment algorithms, edge sharpening techniques, and image resizing algorithms.

[0054] The preprocessing module 120 processes the raw data to enhance image quality, removing noise and adjusting the brightness and contrast to ensure clarity. It also resizes the images to fit the required input format for analysis.

[0055] In an embodiment of the present invention, the preprocessing module 120 removes random noise from the captured images using tools such as Gaussian filters and Median filters.

[0056] The preprocessing module 120 employs contrast adjustment algorithms to improve the visibility of fine details in the images, via tools such as histogram equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE).

[0057] Edge sharpening techniques enhance the definition of skin features by refining edges using tools such as Laplacian filters and Unsharp mask. In various embodiments of the present invention, image resizing algorithms adjust the images to the required input format for analysis, utilizing tools like bicubic interpolation and lanczos resampling.

[0058] These tools and techniques within the preprocessing module 120 ensure that the captured images are optimized for further analysis by the AI analysis module 130, improving the accuracy and reliability of the system 100.

[0059] The preprocessing module 120 dynamically adjusts parameters based on skin characteristics using machine learning models. It employs color normalization techniques to standardize color profiles under varying lighting conditions. A detailed explanation of the preprocessing module 120, including all components, is provided in FIG.2. The preprocessed data is then forwarded to the AI analysis module 130 for in-depth analysis.

[0060] The AI analysis module 130 is an advanced component that plays a critical role in the real-time skin health monitoring and wellness assessment system. In various embodiments, the AI analysis module 130 employs advanced AI algorithms, primarily Convolutional Neural Networks (CNNs), to analyze visual data captured by the image capturing module 110. In various embodiments, these image CNNs are configured at extracting patterns and features from images, making them ideal for detecting subtle variations in skin characteristics such as texture, tone, and elasticity.

[0061] To elaborate further, CNNs are a type of deep learning model that excels in analyzing visual data. CNNs are composed of layers that process and transform input data, usually images. The CNN may include layers, such as, convolutional layers, pooling layers, fully connected layers, and activation functions.

[0062] CNNs operate through a series of specialized layers. Convolutional layers apply filters to detect image features like edges and textures, creating feature maps. Pooling layers then down sample these maps using methods like max or average pooling, reducing computation and preventing overfitting. The processed data is then passed to fully connected layers, which analyze extracted features and classify the output. To enhance learning, activation functions like ReLU introduce non-linearity, allowing the model to recognize complex patterns.

[0063] In various embodiments of the present invention, the CNN follows a structured process, such as, convolution extracts image features, which are then passed through an activation function like ReLU for non-linearity. In various embodiments, pooling layers reduce the dimensions while preserving important information. The output is then flattened into a vector before being processed by fully connected layers, which generate the final classification result.

[0064] In various embodiments, the examples of CNN applications, such as, but not limited to, image classification, object detection, image segmentation, face recognition, and image generation.

[0065] The AI analysis module 130 also integrates preprocessing techniques, such as image normalization and noise reduction, to ensure high-quality inputs for analysis.

[0066] Additionally, the AI analysis module 130 supports multi-modal analysis by combining image data with contextual metadata, and it performs real-time processing locally on the user device through edge computing, thus enhancing privacy and computational efficiency.

[0067] In various embodiments of the present invention, the AI analysis module 130 is configured as a continuous learning module for updating the AI model using anonymized data. It should be noted that the anonymized data refers to data that has been processed to remove personal identifiers to ensure privacy.

[0068] Furthermore, in various embodiments of the present invention, the system 100 further comprises an AI system integration module that allows seamless integration with iOS and Android platforms. This ensures that users can access the system’s functionalities via mobile applications, enhancing usability and accessibility.

[0069] The AI analysis module 130 is responsible for extracting key characteristics of the skin, including hydration levels, stress markers, and signs of nutrient deficiencies.

[0070] By analyzing texture, tone, and elasticity, the AI analysis module 130 may classify findings into actionable health indicators.

[0071] In various embodiments, the AI analysis module 130 integrates contextual data, such as user age and environmental factors, to refine the accuracy of its analysis, providing personalized insights tailored to each user's specific conditions. More specifically, the AIanalysis module 130 generates actionable recommendations based on the analysis, such as hydration tips, stress reduction strategies, and dietary suggestions, which are crucial for proactive health management.

[0072] In various embodiments, the AI analysis module 130 interacts seamlessly with other components of the system 100. The AI analysis module 130 receives enhanced data from the preprocessing module 120, ensuring that the input for analysis is of high quality. Once the analysis is complete, the AI analysis module 130 passes extracted features and insights to the contextual data integration module 140 for further enhancement.

[0073] The integrated data is then sent to the edge computing processor 150 for real-time processing, after which the final analysis results and actionable insights are forwarded to the output module 160.

[0074] This integrated workflow ensures that users receive accurate, real-time feedback and personalized health recommendations, thereby enhancing the overall effectiveness and reliability of the system 100.

[0075] Examples of the AI analysis module 130 include systems that use CNNs like ResNet, VGGNet, and InceptionNet for detailed image analysis and feature extraction, identifying variations in skin characteristics.

[0076] These systems integrate preprocessing techniques, such as image normalization and noise reduction for high-quality inputs. Mobile applications utilize TensorFlow Lite and Qualcomm Snapdragon AI Engine for on-device processing, providing real-time skin health monitoring while maintaining privacy.

[0077] The AI analysis module 130 further incorporates transfer learning techniques to adapt pre-trained models specifically for skin health monitoring. The module 130 also integrates anomaly detection algorithms to identify unusual skin patterns that may indicate underlying health issues.

[0078] The AI analysis module 130 employs a federated learning approach, allowing AI models to be updated continuously using data from multiple users while preserving individual privacy. This ensures that personal health data remains secure and does not require centralized cloud storage.

[0079] In various embodiments, the AI analysis module 130 also provides confidence scores with its assessments, allowing users to gauge the reliability of health insights. Additionally, the AI analysis module 130 integrates explainable AI techniques, offering understandable justifications for the insights and recommendations generated. A detailed explanation of the AI analysis module 130, including all components, is provided in FIG. 4.

[0080] In various embodiments, the AI analysis module 130 also finds use in edge computing solutions, offering instant feedback on hydration levels, stress markers, and nutrient deficiencies by combining image data with contextual metadata, thus delivering accurate and personalized health assessments.

[0081] The contextual data integration module 140 is designed to enhance the accuracy and relevance of the analysis performed by the AI analysis module 130.

[0082] The contextual data integration module 140 integrates various contextual data sources, such as user demographics (age, gender), environmental factors (humidity, temperature), and user behavior (usage patterns, preferences).

[0083] The contextual data integration module 140 employs advanced data fusion techniques to combine and harmonize data from multiple sources, ensuring a comprehensive view of the user's context.

[0084] The primary functionality of the contextual data integration module 140 is to refine the analysis performed by the AI analysis module 130 by incorporating contextual information.

[0085] For example, the contextual data integration module 140 may adjust the interpretation of skin hydration levels based on the user's location and weather conditions.

[0086] In an embodiment, the contextual data integration module 140 also provides personalized insights by considering user-specific factors, such as age and lifestyle, to offer tailored recommendations.

[0087] The contextual data integration module 140 ensures that the health assessments and suggestions are not only accurate but also relevant to the user's unique circumstances.

[0088] Further, the contextual data integration module 140 interacts seamlessly with other components of the system 100. The contextual data integration module 140 receives processed data from the AI analysis module 130 and contextual data from external sources.The integrated data is then passed to the edge computing processor 150 for real-time processing.

[0089] Finally, the processed and contextualized information is forwarded to the output module 160, which presents the personalized insights and recommendations to the user. This integrated workflow ensures that the system 100 provides accurate, real-time feedback and actionable health recommendations.

[0090] Examples of the contextual data integration module 140 include systems that enhance health assessments by merging diverse data sources. For instance, mobile health apps use the contextual data integration module 140 to combine skin analysis with contextual information such as user age, location, and environmental conditions (humidity, temperature), providing precise and personalized health insights.

[0091] Fitness trackers integrate environmental data with user activity patterns for tailored exercise recommendations.

[0092] In telemedicine, the contextual data integration module 140 combines patient information with real-time data from wearable devices, enabling more informed healthcare decisions.

[0093] Furthermore, by employing advanced data fusion techniques, these systems deliver comprehensive and relevant health assessments, enhancing user engagement and outcomes.

[0094] The edge computing processor 150 is a high-performance computing device designed to process data locally, close to the data source. Edge computing processor 150 typically includes a powerful CPU (e.g., NXP i.MX8M Quad Core Cortex A53), ample RAM (e.g., 2GB LPDDR4), and storage options (e.g., 16GB eMMC).

[0095] The edge computing processor 150 also supports various connectivity options such as Ethernet ports, USB ports, and serial ports, enabling seamless communication with other devices and networks.

[0096] The primary functionality of the edge computing processor 150 is to perform real- time data processing and analysis. This includes tasks such as video analytics, object detection, anomaly prediction, and local data storage. By processing data locally, it reduces the need for continuous internet connectivity, minimizes latency, and ensures faster response times.

[0097] The edge computing processor 150 also supports running custom applications and algorithms, enabling tailored solutions for specific use cases.

[0098] The edge computing processor 150 interacts with various components within the system. The edge computing processor 150 receives data from sensors and Internet of Things (IoT) devices, processes this data locally, and then sends relevant insights or actions to other parts of the system, such as the output module or cloud services.

[0099] The edge computing processor 150 can also communicate with remote management systems for configuration, updates, and monitoring. This interconnected setup ensures efficient data flow and enables autonomous operations even in remote or disconnected environments.

[0100] Regarding data privacy, edge computing systems prioritize local data processing, which inherently enhances privacy by minimizing the amount of data transmitted to the cloud. Techniques such as local differential privacy and encryption are often employed to further protect sensitive information.

[0101] By processing data on the device, itself, edge computing reduces the risk of data breaches and unauthorized access, ensuring that user data remains secure.

[0102] The output module 160 is equipped with various hardware and software components designed to present the final processed data to the user in a user-friendly format.

[0103] The output module 160 typically includes a high-resolution display screen, speakers, and tactile feedback mechanisms. The software components include Graphical User Interfaces (GUIs) and Application Programming Interfaces (APIs) that facilitate communication between the module and the user device.

[0104] The output module 160 supports various output formats such as text, graphics, audio, and haptic feedback to ensure that users receive clear and actionable insights.

[0105] The primary functionality of the output module 160 is to present the final processed data and insights to the user in a comprehensible manner.

[0106] The output module 160 includes displaying visual representations of skin health indicators, such as hydration levels, stress markers, and nutrient deficiencies, through intuitive graphics and charts.

[0107] The output module 160 may also deliver real-time notifications and alerts, providing immediate feedback on changes in skin health.

[0108] Additionally, the output module 160 offers personalized recommendations and actionable tips based on the analysis, helping users make informed decisions about their health and wellness.

[0109] The output module 160 can adapt the presentation of information to suit different user preferences and accessibility needs. Thus, an alert mechanism configured to notify users of potential critical skin health conditions, prompting them to seek professional medical advice.

[0110] The system 100 further includes an alert mechanism configured to notify users of potential critical skin health conditions. Specifically, the alert mechanism prompts users to seek professional medical advice when anomalies such as extreme dehydration, stress indicators, or severe nutrient deficiencies are detected.

[0111] The output module 160 interacts seamlessly with other components of the system. After the edge computing processor 150 processes the data in real-time, the final analysis results and actionable insights are forwarded to the output module 160. The output module 160 then formats the data into user-friendly outputs and presents it to the user via the display screen or other output mechanisms.

[0112] The output module 160 also communicates with the user device through the wireless communication network, ensuring that users receive updates and notifications even when they are not actively using the system 100. The continuous data flow between the edge computing processor 150 and the output module 160 ensures that users receive timely and accurate feedback on their health status, enhancing the overall user experience.

[0113] Examples of the output module 160 include health monitoring apps that provide real- time feedback based on skin analysis. These apps can display visual representations of hydration levels, stress markers, and nutrient deficiencies through intuitive graphics and charts.

[0114] Fitness and wellness platforms use this module to send personalized notifications and alerts, offering immediate feedback on changes in skin health and suggesting actions such as increasing water intake or practicing relaxation techniques.

[0115] Telemedicine applications present detailed reports and insights to healthcare providers, enabling remote consultations and informed decisions about patient care. By integrating with various devices and networks, the output module 160 ensures users receive timely and actionable health information.

[0116] In another scenario, a user captures daily images of their skin. The system 100 tracks changes in skin texture, tone, and elasticity, providing daily updates and actionable insights on skincare routines and preventive measures.

[0117] These examples illustrate how the system provides real-time health insights and personalized recommendations to improve skin health and general wellness.

[0118] Although FIG. 1 shows various hardware components of the system 100, it is to be understood that other embodiments are not limited thereto. In other embodiments, the system 100 may include fewer or more components.

[0119] Further, the labels or names of the components are used only for illustrative purposes and do not limit the scope of the invention. One or more components can be combined to perform the same or substantially similar function in the system 100. Few of the components will now be discussed.

[0120] Fig. 2 depicts the pre-processing module 120 for enhancing the quality of captured images to ensure accurate analysis. The pre-processing module 120 comprises a noise reduction module 202, a histogram equalization module 204, an edge detection module 206, a colour normalization module 208, and a machine learning module 210.

[0121] The noise reduction module 202 is responsible for reducing noise in the captured images. Noise can arise from various sources, such as low light conditions, sensor imperfections, or electronic interference. Reducing noise improves the clarity of the image and helps in better feature extraction.

[0122] As explained above, noise reduction module 202 uses techniques such as, but not limited to, Gaussian filtering and median filtering. The Gaussian filtering technique smoothens the image by reducing high-frequency noise while preserving edges. For example, the Gaussian filtering technique helps make skin features like texture more distinguishable.

[0123] The median filtering technique replaces each pixel's value with the median of its neighboring pixels, effectively removing salt-and-pepper noise. This helps maintain edge sharpness while reducing noise.

[0124] For example, imagine capturing an image of the skin of a user in a dimly lit room. The noise reduction module 202 applies filters to minimize the graininess of the image, resulting in a clearer and smoother representation of the skin.

[0125] The histogram equalization module 204 enhances the contrast of the image by adjusting the intensity distribution. Histogram equalization spreads out the most frequent intensity values, making details in the image more discernible. It improves the visibility of features by ensuring a uniform distribution of pixel intensities. The technique makes dark regions darker and bright regions brighter.

[0126] For example, if the user captures an image where the lighting is uneven, this module will adjust the contrast so that features such as skin pores and wrinkles become more visible.

[0127] The edge detection module 206 identifies the boundaries within the image. Edge detection is crucial for recognizing shapes and objects within the image, such as wrinkles, pores, and other skin features.

[0128] The edge detection module 206 uses techniques like Laplacian filters to detect edges by highlighting regions of rapid intensity change. This helps segment the image into meaningful regions.

[0129] For example, when capturing an image of an area with fine lines and wrinkles, the edge detection module 206 highlights these features, making them more prominent for further analysis.

[0130] The colour normalization module 208 adjusts the colour balance of the image to ensure consistency across different lighting conditions. Colour normalization corrects colour distortions, making the colours appear natural and uniform.

[0131] The colour normalization module 208 uses techniques like colour space conversion, which converts the image to a standard colour space, adjusting the white balance so that white objects appear white under different lighting conditions.

[0132] For example, if a user takes a photo under artificial lighting, the skin might appear yellowish. The colour normalization module 208 corrects this, ensuring that the skin colour appears as it would under natural lighting.

[0133] The machine learning module 210 applies advanced machine learning algorithms to further enhance image quality and extract relevant features. The machine learning module 210 can adapt to different skin types and conditions, providing optimal preprocessing tailored to each user.

[0134] The machine learning module 210 uses techniques such as dynamic adjustment. Machine learning models continuously learn and adapt preprocessing parameters based on the input data. This ensures that the preprocessing is customized for each individual.

[0135] For example, for a user with darker skin tones, the machine learning module 210 adjusts the preprocessing parameters to enhance features specific to that skin type, ensuring accurate analysis.

[0136] Therefore, the captured images and videos are first sent to the noise reduction module 202 to filter out unwanted noise. The module ensures the raw data is clean and clear, providing a good starting point for further enhancements.

[0137] The noise-reduced image is then processed by the histogram equalization module 204 to enhance contrast. The histogram equalization module 204 improves the visibility of details, making it easier to identify key features in the image.

[0138] The contrast-enhanced image is fed into the edge detection module 206 to identify and highlight edges. The edge detection module 206 recognizes and emphasizes boundaries and contours within the image, crucial for detailed analysis.

[0139] The edge-detected image is then processed by the colour normalization module 208 to correct colour balance. The colour normalization module 208 ensures consistent colour representation, making the image appear natural regardless of lighting conditions.

[0140] Finally, the colour-normalized image is processed by the machine learning module 210 to dynamically adjust preprocessing parameters and extract relevant features. The machine learning module 210 adapts the preprocessing to individual characteristics, ensuring optimal image quality for accurate analysis.

[0141] FIG.3A shows the raw input image exemplifying the state before any preprocessing techniques have been applied. The captured image contains significant noise, making it difficult to discern fine details of the skin. The image appears blurry, with unwanted pixel variations that obscure key features.

[0142] This state represents the initial condition of the image, highlighting the challenges faced in skin analysis due to poor image quality. The lack of enhancements results in a low- visibility, low-clarity image that is not suitable for accurate analysis of skin health indicators.

[0143] On the other hand, the preprocessing module 130 applies a series of advanced AI- based techniques to improve the image quality. These enhancements are demonstrated in FIG. 3B, which illustrates the processed image after the application of noise reduction, contrast enhancement, and edge sharpening techniques.

[0144] The enhancements significantly improve the image quality by reducing noise through Gaussian and median filtering. This step removes unwanted pixel variations, resulting in a cleaner and clearer image.

[0145] Contrast enhancement, achieved through histogram equalization, brings out fine skin details that were previously obscured.

[0146] Additionally, edge sharpening using the Laplacian filter refines the skin texture and improves the overall clarity of the image.

[0147] These preprocessing techniques transform the initially noisy and blurry image into a high-quality, detailed image that is well-suited for further analysis by the AI analysis module 130.

[0148] FIG. 4 depicts the AI analysis module 130 included in the system 100. The AI analysis module 130 comprises a CNN module 402, a skin characteristics detection module 404, a multi-modal data integration module 406, a health insights analysis module 408, and a real-time feedback and recommendations module 410.

[0149] The CNN module 402 is responsible for processing and analyzing the image data. The CNN module 402 uses convolutional layers to detect patterns and features in the images, essential for identifying skin characteristics.

[0150] The CNN module 402 receives the preprocessed image data and extracts features, which are then sent to the skin characteristics detection module 404.

[0151] For example, the CNN module 402 may detect textures, edges, and specific patterns such as wrinkles or acne on the skin.

[0152] The skin characteristics detection module 404 utilizes the features extracted by the CNN module 402 to detect various skin characteristics, including skin tone, texture, elasticity, and the presence of lesions.

[0153] The skin characteristics detection module 404 takes the features from the CNN module 402 and identifies relevant skin characteristics, passing this information to the multi- modal data integration module 406.

[0154] For example, the skin characteristics detection module 404 can identify attributes such as dryness, oiliness, or the presence of moles on the skin.

[0155] The multi-modal data integration module 406 integrates data from multiple sources, including the detected skin characteristics and other relevant health data, to provide a comprehensive analysis.

[0156] The multi-modal data integration module 406 combines data from the skin characteristics detection module 404 with additional contextual data and sends the integrated data to the health insights analysis module 408.

[0157] For example, the multi-modal data integration module 406 can integrate user- specific data like age, lifestyle habits, and environmental factors, enhancing the accuracy of the skin analysis.

[0158] The health insights analysis module 408 analyzes the integrated data to generate health insights using machine learning algorithms to interpret the data and provide diagnostic information or health recommendations.

[0159] The health insights analysis module 408 receives integrated data from the multi- modal data integration module 406 and processes it to generate health insights, sending the results to the real-time feedback and recommendations module 410.

[0160] For example, the health insights analysis module 408 can predict the likelihood of skin conditions or recommend actions based on detected characteristics and integrated data, such as advising more hydration if dryness is detected.

[0161] The real-time feedback and recommendations module 410 provides real-time feedback and personalized recommendations to the user based on the health insights analysis.

[0162] The real-time feedback and recommendations module 410 receives health insights from the health insights analysis module 408 and presents the feedback and recommendations to the user.

[0163] For example, the real-time feedback and recommendations module 410 can recommend skincare routines, alert users to potential health issues, or suggest seeking medical advice if certain conditions are detected.

[0164] The CNN module 402 processes image data and extracts features, the skin characteristics detection module 404 detects relevant skin characteristics from the features, the multi-modal data integration module 406 integrates skin characteristics with additional health data, the health insights analysis module 408 generates health insights from the integrated data, and the real-time feedback and recommendations module 410 provides feedback and recommendations to the user.

[0165] For example, the user captures a skin image using their smartphone camera, the CNN module 402 analyzes the image and extracts features, the skin characteristics detection module 404 identifies skin hydration levels, the multi-modal data integration module 406 combines skin hydration data with user-specific contextual information, the health insights analysis module 408 predicts the user's hydration status, and the real-time feedback and recommendations module 410 suggests increasing water intake if dehydration is detected.

[0166] FIG. 5 depicts an example use case of the system 100. FIG. 5 illustrates a scenario in which a user captures an image of their skin using a mobile application. Once the image is captured, system 100 processes the visual data to analyze skin characteristics. The system 100 employs advanced image analysis, potentially leveraging AI or computer vision.

[0167] In this example, the processing step focuses on evaluating skin hydration levels. After processing the skin image, the system 100 determines the hydration status of the skin of the user. The system 100 then provides actionable feedback, such as suggesting that the user drink more water.

[0168] Additionally, it may recommend a personalized daily water intake goal. The depiction of a scanned hand symbolizes capability of the system 100 to analyze biological features, aligning with the hydration analysis function described in the example.

[0169] FIG. 6 depicts another example use case of the system 100. FIG. 6 illustrates a scenario where a user captures a live video of their face using a mobile application. Once the video is recorded, system 100 processes the visual data to analyze facial characteristics. The system 100 employs advanced image analysis, potentially leveraging AI or computer vision. In the given example, this processing step focuses on identifying stress markers, such as skin tone variations and under-eye darkness.

[0170] After processing the facial video, the system 100 determines the stress levels of the user. The system 100 then provides actionable feedback, such as recommending stress reduction techniques like relaxation exercises. Additionally, it may offer personalized well- being suggestions to help the user manage stress effectively.

[0171] The depiction of a scanned face symbolizes the capability of the system 100 to analyze biological features, aligning with the stress detection and analysis function described in the example.

[0172] FIG. 7 depicts yet another example use case of the system 100. FIG. 7 illustrates a scenario where a user captures a high-resolution image of their skin using a mobile application. Once the image is captured, the system 100 processes the visual data to analyze skin characteristics. The system 100 utilizes advanced image analysis techniques, such as AI or computer vision, to detect subtle skin changes.

[0173] In this example, the system 100 focuses on identifying signs of nutritional deficiencies, such as pallor or flaky skin. After analyzing the image, the system 100 determines the potential deficiency and provides actionable feedback. This includes dietary recommendations, such as consuming foods rich in essential vitamins and minerals to improve skin health.

[0174] The depiction of a scanned face with highlighted facial points symbolizes the ability of the system 100 to assess biological indicators related to nutrition, aligning with its function of detecting deficiencies and offering personalized nutritional guidance.

[0175] FIG. 8 provides a flowchart 800 outlining the steps involved in the AI-driven real- time skin health monitoring system. The user initiates the process by opening the health monitoring application on their device, such as, but not limited to, a smartphone and computer, as depicted in step 802. For example, step 802 involves opening the app on amobile device or computer to access the features of the system. This is the initial step where the user is ready to begin the skin health assessment process.

[0176] The system 100 prompts the user to scan a target area (e.g., skin or face). The user may need to adjust the camera for optimal scanning. For example, the app instructs the user to position their hand under the camera for scanning.

[0177] The app prompts the user to capture a high-resolution image and live video feed of their skin using the device's camera, as depicted in step 804. For example, the user positions the camera to capture a clear image of the skin area to be analyzed, such as the face, hands, or any other body part. The user must ensure proper lighting and positioning for accurate analysis.

[0178] The captured image and video feed undergo preprocessing to enhance their quality, as depicted in step 806. This involves several techniques, such as, but not limited to, noise reduction, histogram equalization, edge detection, colour normalization, and a machine learning module to ensure the image is suitable for analysis.

[0179] Noise reduction techniques such as Gaussian filtering and median filtering are used to reduce noise and improve image clarity. Histogram equalization adjusts the intensity distribution to enhance contrast and make details more discernible. Edge detection techniques like Laplacian filters highlight edges, enhancing the ability to recognize shapes and objects. Colour normalization adjusts the colour balance to ensure consistency across different lighting conditions. The machine learning module applies advanced algorithms to further enhance image quality and extract relevant features.

[0180] For example, the preprocessing module 120 enhances the image taken in a dimly lit room, making skin features like texture and tone more distinguishable.

[0181] The preprocessed image is analyzed by the AI analysis module 130. The AI analysis module 130 primarily uses CNNs and processes the preprocessed image to detect patterns and features in the skin such as texture, tone, elasticity, and hydration levels, as depicted in step 808.

[0182] The CNN module 402 detects textures, edges, and specific patterns such as wrinkles or acne. The skin characteristics detection module 404 identifies attributes like skin tone, texture, elasticity, and the presence of lesions. The multi-modal data integration module 406 combines detected skin characteristics with additional contextual data for comprehensiveanalysis. The health insights analysis module 408 uses machine learning algorithms to generate health insights from the integrated data.

[0183] For example, the AI analysis module 130 detects signs of dryness and stress by analyzing the texture and tone of the skin.

[0184] Specific features related to hydration levels, stress markers, and nutritional deficiencies are extracted from the AI analysis, as depicted in step 810. The AI analysis module 130 identifies hydration levels based on skin elasticity and brightness, stress markers (like redness and tone variations) through facial tension and redness, and nutritional deficiencies (like pallor and flaky skin).

[0185] The contextual data integration module 140 integrates contextual metadata, such as the user's age, location, and environmental factors, to provide a more accurate and personalized health assessment, as depicted in step 812. This step ensures the analysis considers all relevant variables.

[0186] Based on the extracted features and contextual data, the system 100 generates a real- time health score, as depicted in step 814. This score reflects the user's current wellness state, providing an overview of hydration, stress, and nutritional levels.

[0187] The analysis results are displayed to the user in a user-friendly format. This includes such as, but not limited to, graphs, scores, and visual indicators. For example, the app shows a hydration score with a color-coded meter (e.g., green for hydrated, red for dry skin).

[0188] The app generates personalized recommendations based on the health score, as depicted in step 816. These recommendations may include hydration tips, stress reduction techniques, and dietary adjustments to improve overall wellness.

[0189] The user reviews the health score and recommendations provided by the app, as depicted in step 818. They can take appropriate actions based on the insights, such as drinking more water, practicing relaxation exercises, or making dietary changes.

[0190] The system 100 collects anonymized data from user interactions, ensuring no personal information is retained, as depicted in step 820. This anonymized data is used to update and refine AI algorithms, improving their accuracy and performance. By analyzing patterns and trends in the data, the AI adapts to new conditions and addresses any biases. Thiscontinuous learning process ensures the system 100 remains effective in providing accurate health insights and recommendations.

[0191] For example, if similar skin patterns are frequently detected, the AI adjusts its algorithms for better detection. This ongoing process enhances the system's 100 accuracy, adaptability, and reliability while maintaining user privacy.

[0192] The system 100 allows users to save the results for tracking progress over time. Users may be able to compare past reports and set goals. For example, the hydration report is saved in the profile of the user for future reference.

[0193] The user completes the interaction, either by exiting the app or performing another scan. The process can restart from the beginning if the user wants to perform another analysis. For example, the user closes the app after checking their hydration level.

[0194] The flowchart 800 in FIG. 8 provides a clear sequence of steps for the AI-driven real-time skin health monitoring system, highlighting the use of advanced AI techniques, preprocessing, and contextual integration to deliver accurate and personalized health insights to the user.

[0195] The various actions in flowchart 800 may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments, some actions listed in FIG. 8 may be omitted.

[0196] FIG. 9 is a flowchart depicting a method 900 for real-time skin health monitoring and general wellness assessment. At step 902, the method 900 comprises capturing data comprising high-resolution images and video of the skin of a user on a real-time basis by the image-capturing module 110.

[0197] The user initiates the process by using the image-capturing module 110, such as a camera on a smartphone or computer, to capture high-resolution images and videos of their skin. This ensures that the data collected is detailed and suitable for further analysis.

[0198] At step 902, the method 900 comprises preprocessing the captured data to enhance the quality of the captured images by the preprocessing module 120.

[0199] The preprocessing module 120 enhances the quality of the captured images through several techniques and handles variations in lighting and environmental conditions bydynamically adjusting brightness and contrast and applying color normalization techniques to ensure consistent image quality.

[0200] The preprocessing module 120 applies noise reduction algorithms, including Gaussian and median filtering, and uses histogram equalization to improve image contrast and minimize image artifacts.

[0201] The preprocessing module 120 employs edge detection techniques, such as Laplacian filters, to highlight the contours and fine details of the skin in the captured images.

[0202] The preprocessing module 120 utilizes machine learning models to dynamically adjust preprocessing parameters based on the specific characteristics of the user's skin.

[0203] At step 906, the method comprises analyzing the images by the AI analysis module 130 to detect skin characteristics.

[0204] The AI analysis module 130 employs advanced AI routines and algorithms to analyze the preprocessed images.

[0205] The AI analysis module 130 detects skin characteristics, including hydration levels, stress markers, and nutritional deficiencies by analyzing features such as skin texture, tone variations, and fine lines.

[0206] The AI analysis module 130 includes the CNN for extracting high-level features from the captured skin images to detect health indicators.

[0207] The AI analysis module 130 employs transfer learning techniques to adapt pre- trained models to the specific task of skin health monitoring, ensuring high accuracy across diverse skin types and conditions.

[0208] The AI analysis module 130 incorporates anomaly detection algorithms to identify unusual skin patterns that may indicate underlying health issues.

[0209] The AI analysis module 130 integrates multi-modal data, including environmental data and user-specific metadata, to enhance the accuracy of the skin health assessment.

[0210] The AI analysis module 130 provides confidence scores for its assessments, allowing users to understand the reliability of the health insights provided.

[0211] The AI analysis module 130 includes explainable AI techniques to provide users with understandable justifications for the health insights and recommendations generated.

[0212] At step 908, the method 900 comprises integrating the analyzed image data with contextual metadata of the user by the contextual data integration module 140 to improve assessment accuracy.

[0213] The contextual data integration module 140 combines the analyzed image data with contextual metadata, such as the user's age, location, and environmental factors, to provide a more accurate and personalized health assessment. This step ensures that the analysis takes into account all relevant variables for a comprehensive evaluation.

[0214] At step 910, the method 900 comprises providing real-time feedback and personalized wellness recommendations through an output module 160. Based on the detected wellness indicators, the system 100 generates personalized insights and recommendations for the user. Feedback includes daily reports and personalized recommendations.

[0215] The output module 160 presents the health insights and personalized wellness recommendations to the user through a user-friendly interface, such as hydration tips, through a user-friendly interface. The output module 160 ensures that the feedback is provided in real- time, allowing the user to take immediate action based on the insights.

[0216] The user receives the personalized recommendations and takes appropriate actions to improve their skin health and general wellness, involving active engagement with the provided insights and recommendations to manage their health proactively.

[0217] The preprocessing and analyzing of the captured images and videos are performed locally on the user's device using edge computing. This ensures low-latency processing, providing real-time feedback to the user while enhancing data privacy by avoiding the need for cloud-based.

[0218] The system 100 periodically updates the AI analysis model 120 using data that has been processed to remove personal identifiers to ensure privacy. This continuous learning process helps improve the accuracy and performance of the AI algorithms over time.

[0219] The system 100 provides the user with guidance through the scanning process using an intuitive interface. The system 100 offers real-time feedback to ensure accurate data capture and a smooth user experience.

[0220] The AI analysis module 130 is integrated with both iOS and Android platforms, allowing users to access the skin health monitoring and wellness assessment features on their preferred devices.

[0221] The various actions in method 900 may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments, some actions listed in FIG. 9 may be omitted.

[0222] Therefore, the present invention relates to a comprehensive system 100 for real-time skin health monitoring and general wellness assessment.

[0223] The system 100 captures high-resolution images and live video of a user's skin using an image-capturing module 110. The captured images are then enhanced by a preprocessing module 120 to handle variations in lighting and environmental conditions. The AI analysis module 130 analyzes the preprocessed images to detect various skin characteristics and integrate contextual metadata for accurate analysis. This includes identifying wellness indicators such as hydration levels, stress markers, and nutritional deficiencies.

[0224] Based on the detected wellness indicators, the system 100 generates personalized insights and recommendations for the user, providing real-time feedback through an output module 160.

[0225] Each component of the system 100 plays a crucial role in delivering accurate, personalized, and actionable health insights to the user. ADVANTAGEOUS EFFECT OF THE INVENTION

[0226] In various embodiment of the present invention, the system 100 provides instant feedback on hydration levels, stress markers, and nutritional deficiencies by analyzing skin characteristics. This allows users to take proactive measures to maintain their health.

[0227] Further, the system 100 is designed to work with standard devices like smartphones and webcams, the system 100 eliminates the need for expensive diagnostic equipment, making health monitoring accessible and affordable for a wide audience.

[0228] Unlike traditional dermatological tools that focus on specific skin diseases, this system 100 offers comprehensive health assessments by analyzing general wellness indicators reflected through the skin.

[0229] By leveraging edge computing, the system 100 processes all data locally on the user device. This ensures that sensitive health information remains secure and private, addressing concerns about data privacy and security.

[0230] In the embodiment of the present invention, system 100 features an intuitive interface that guides users through the scanning process and presents results in a clear, actionable format. Personalized recommendations empower users to make informed decisions about their health.

[0231] Furthermore, the system 100 eliminates the need for clinical visits and specialized equipment, reducing the cost barrier to regular health monitoring. Users can conveniently monitor their health using everyday devices.

[0232] By identifying subtle changes in skin condition, the system 100 acts as an early warning tool, encouraging users to take timely actions to prevent more significant health issues.

[0233] Further, the technology can be expanded to include additional health metrics and can integrate with other health monitoring systems and wearables, ensuring its applicability in various health contexts.

[0234] Further, the system 100 is optimized for diverse skin types, tones, and age groups, making it reliable and inclusive for users worldwide.

[0235] In a nutshell, the system 100 reduces reliance on physical diagnostic tools, minimizing waste and environmental impact by utilizing digital technologies.

[0236] It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the spirit or scope of the present invention.

[0237] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of description. They are not intended to be exhaustive or to limitthe present invention to the precise forms disclosed, and obviously, many modifications and variations are possible in light of the above teaching.

[0238] Further, the embodiments were chosen and described in order to best explain the principles of the present invention and its practical application, and thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

CLAIMS:

1. A method (900) for real-time skin health monitoring and wellness assessment, the method (900) comprising: capturing (at step 902) a set of data comprising at least one of high-resolution images and video of the skin of a user on a real-time basis by an image-capturing module (110); preprocessing (at step 904) the captured data by a preprocessing module (120) to enhance the quality of the captured images; analyzing (at step 906) the images and video by an Artificial Intelligence (AI) analysis module (130) to detect plurality of skin characteristics, wherein the said AI analysis module (130) comprises one or more AI routines / algorithms and one or more training routines for conducting the said analysis; integrating (at step 908) the analyzed image data with contextual metadata of the user by a contextual data integration module (140) to improve assessment accuracy; and providing (at step 910) real-time feedback and personalized wellness recommendations via an output module (160) to the user.

2. The method (900) as claimed in claim 1, wherein the preprocessing module (120) comprises routines for handling variations in at least one of lighting and environmental conditions by dynamically adjusting brightness and contrast, and applying color normalization techniques over the captured data comprising images and video to ensure consistent image quality.

3. The method (900) as claimed in claims 1-2, wherein the preprocessing module (120) applies noise reduction algorithms, including at least one of gaussian and median filtering, and applies histogram equalization to improve image contrast and minimize image artifacts.

4. The method (900) as claimed in claims 1-3, wherein the preprocessing module (120) employs edge detection techniques comprising laplacian filters to highlight contours and finer details of the skin in the said captured images and video.

5. The method (900) as claimed in claims 1-4, wherein the preprocessing module (120) employs machine learning models to dynamically adjust preprocessing parameters based on specific characteristics of the user's skin.

6. The method (900) as claimed in claims 1-5, wherein the preprocessing and analyzing of the captured data comprising images and video are performed locally on the user device by edge computing, thereby ensuring low-latency processing and enhanced data privacy.

7. The method (900) as claimed in claim 1 further comprising periodically updating the AI analysis model (130) via anonymized data, wherein the said anonymized data comprises data which has been processed to remove personal identifiers to ensure data privacy.

8. The method (900) as claimed in claim 1, wherein the AI analysis module (130) includes a Convolutional Neural Network (CNN) for extracting high-level features from the captured skin images and video to detect multiple skin health indicators.

9. The method (900) as claimed in claim 1, wherein the training routines comprises transfer learning techniques to adapt pre-trained models to preset specific task of skin health monitoring, ensuring high accuracy across diverse skin types and conditions.

10. The method (900) as claimed in claim 1, wherein the AI analysis module (130) comprises one or more anomaly detection algorithms to identify unusual skin patterns which may indicate underlying health issues.

11. The method (900) as claimed in claim 1, wherein the AI analysis module (130) integrates multi-modal data, including environmental data and user-specific metadata, to enhance the accuracy of skin health assessment.

12. The method (900) as claimed in claim 1, wherein the AI analysis module (130) comprises routines to provide confidence scores for its assessments, thereby allowing users to understand reliability of the health insights provided, and one or more explainable AI techniques to provide users with understandable justifications for the health insights and recommendations generated.

13. The method (900) as claimed in claim 1 further comprising guiding the user through the scanning process via an intuitive interface and providing real-time feedback.

14. The method (900) as claimed in claim 1 further comprising integrating AI analysis module (130) with operating platforms comprising iOS and Android platforms.

15. A system (100) for real-time skin health monitoring and general wellness assessment, the said system (100) comprises: an image capturing module (110) configured to capture data comprising high- resolution images and video of the skin of a user on a real-time basis; a preprocessing module (120) configured to preprocess the captured data to enhance the quality of the captured images; an Artificial Intelligence (AI) analysis module (130) configured to detect skin characteristics and provide health insights, the said AI analysis module (130) comprising one or more AI routines / algorithms and training routines for conducting the analysis; anda contextual data integration module (140) configured to integrate analyzed image data with contextual metadata of the user to improve assessment accuracy, wherein the system (100) comprises: an edge computing processor (150) configured to perform on-device analysis, ensuring low-latency processing and data privacy; and an output module (160) configured to present real-time feedback and personalized wellness recommendations through a user interface.

16. The system (100) as claimed in claim 16, wherein the preprocessing module (120) is configured to handle variations in lighting and environmental conditions, and incorporates noise reduction algorithms to minimize image artifacts, and employs histogram equalization to improve image contrast and edge detection techniques to highlight the contours and fine details of the skin.

17. The system (100) as claimed in claim 16-17, wherein the preprocessing module (120) utilizes machine learning models to dynamically adjust preprocessing parameters based on the specific characteristics of the user's skin, and incorporates color normalization techniques to standardize the color profile of images captured under varying lighting conditions.

18. The system (100) as claimed in claim 16-18, wherein the AI analysis module (130) is configured to identify indicators comprising at least one or more of dehydration, stress, and nutritional deficiencies, and includes a Convolutional Neural Network (CNN) for extracting high-level features from the captured skin images.

19. The system (100) as claimed in claim 16-18, wherein the AI analysis module (130) integrates multi-modal data, including environmental data and user-specific metadata, to enhance the accuracy of the skin health assessment.

20. The system (100) as claimed in claim 16-19, wherein the AI analysis module (130) employs transfer learning techniques to adapt pre-trained models to the specific task of skin health monitoring, and incorporates anomaly detection algorithms to identify unusual skin patterns that may indicate underlying health issues.

21. The system (100) as claimed in claim 16, wherein the AI analysis module (130) utilizes a federated learning approach to continuously update the AI models using data from multiple users without compromising individual privacy.

22. The system (100) as claimed in claim 16, wherein the AI analysis module (130) provides confidence scores for its assessments, allowing users to understand the reliability of thehealth insights provided, and includes explainable AI techniques to provide users with understandable justifications for the health insights and recommendations generated.

23. The system (100) as claimed in claim 16 further comprising an alert mechanism configured to notify users of potential critical skin health conditions, prompting them to seek professional medical advice.

24. The system (100) as claimed in claim 16 further comprising an AI system integration module that allows seamless integration with iOS and Android platforms.

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