Personalized rehabilitation system with real-time posture feedback, emotional state detection, and adaptive exercise adjustment

Abstract

This invention relates to a software-based personalized rehabilitation system that adapts in real-time to a user's physical and emotional needs during rehabilitation. The system collects posture and movement data using a device capable of capturing video and images, and provides corrective feedback based on detected deviations. An integrated emotional state detection module monitors stress and fatigue through facial expressions and physiological sensors. The system continuously updates a dynamic digital twin to track posture, alignment, and rehabilitation progress, adjusting exercises based on real-time feedback. Contextual data, such as biorhythms, religious practices, and demographic information, are used to further personalize the rehabilitation process. An adaptive AI module adjusts the intensity and type of exercises based on user data, while cloud-based analytics predict future progress and prevent injury. The system also integrates with Telehealth platforms for remote monitoring and intervention, improving overall recovery outcomes.

Description

Personalized Rehabilitation System with Real-Time Posture Feedback, Emotional State Detection, and Adaptive Exercise Adjustment

[0001] This invention relates to a personalized rehabilitation system designed to optimize recovery, improve patient engagement, and ensure a holistic approach to rehabilitation. The system integrates a digital twin engine for real-time monitoring of posture, alignment, and rehabilitation progress, an AI-driven adaptive exercise module that tailors exercises based on real-time feedback, and a continuous emotional state detection module that tracks fatigue and stress levels. It also includes a cloud-based analytics engine for predictive insights into rehabilitation outcomes, a real-time injury prevention module that adjusts exercises to avoid strain, and an AI-powered virtual coaching module that provides personalized feedback and motivation. Additionally, the system incorporates telehealth integration, enabling remote monitoring and adjustments by healthcare providers, and a contextual data integration module that personalizes rehabilitation based on factors like biorhythms, religious practices, and demographic information.

[0002] The invention enhances the rehabilitation process by providing adaptive, real-time adjustments based on both physical and emotional data, ensuring a more personalized and efficient recovery journey. It reduces the risk of injury, improves adherence to rehabilitation plans, and fosters a holistic, data-driven approach to patient care, transforming how rehabilitation is delivered and managed.

[0003] A61H, G06N, G06F

[0004] Applications of Artificial Intelligence-Based Patient Digital Twins in Decision Support in Rehabilitation and Physical Therapy

[0005] Emilia Mikołajewska, Jolanta Masiak, Dariusz Mikołajewski

[0006] Electronics, 2024

[0007] This article discusses how AI-based digital twins (DTs) can personalize rehabilitation by simulating therapy scenarios and predicting patient outcomes. It focuses on continuous monitoring, real-time therapy adjustments, and remote rehabilitation to improve patient outcomes and engagement. Unlike this system, which primarily focuses on physical progress and therapy optimization, our invention integrates emotional state detection (via facial expressions and physiological sensors) and adaptive AI that adjusts rehabilitation based on both physical and emotional data. Our system also uses contextual information like biorhythms and religious practices, providing a deeper level of personalization in the rehabilitation process.

[0008] Technologies in Home-Based Digital Rehabilitation: Scoping Review

[0009] Angela Arntz, Franziska Weber, Marietta Handgraaf, et al.

[0010] JMIR Rehabilitation and Assistive Technologies, 2023

[0011] This review explores existing and emerging digital technologies in home-based rehabilitation, including sensors, robotics, gamification, VR / AR, and AI. It highlights how the COVID-19 pandemic accelerated the use of remote rehabilitation but did not lead to new technological advancements. Key trends point to the increasing use of AI, machine learning, and mobile apps for personalized care and self-management. However, challenges remain regarding technology usability, safety concerns, and the need for long-term clinical validation. Unlike this review, which primarily discusses the integration of physical rehabilitation technologies like sensors, robotics, and VR / AR, our system incorporates emotion-based analysis through AI-driven models, adapting rehabilitation plans based on both physical and emotional states of the patient. We also enhance the user experience by integrating biorhythm and demographic data into the rehabilitation process, aiming for a deeper level of personalization.

[0012] Artificial Intelligence-Driven Virtual Rehabilitation for People Living in the Community: A Scoping Review

[0013] Ali Abedi, Tracey J. F. Colella, Maureen Pakosh, Shehroz S. Khan

[0014] npj Digital Medicine, 2024

[0015] This review examines the use of AI in virtual rehabilitation (VRehab) for home-based patients, highlighting its potential for real-time feedback and improved outcomes. It notes that while AI has shown promise, challenges remain in fully assessing its effectiveness in home settings. Unlike this review, which focuses on physical rehabilitation, our system integrates emotion-based AI models, biorhythm data, and demographic information for a more personalized rehabilitation experience that addresses both physical and emotional recovery.

[0016] WO2023164292A1 – Systems and Methods of Using Artificial Intelligence and Machine Learning in a Telemedical Environment to Predict User Disease States

[0017] This patent describes an AI-driven approach to telemedicine that uses user attribute data and real-time measurements from a treatment apparatus to predict disease states and generate treatment plans. It focuses on broad telemedicine diagnoses rather than specialized rehabilitation or real-time posture correction. However, it is limited in scope as it does not integrate dynamic feedback for posture correction, muscle fatigue tracking, or address emotional state and contextual factors. Our approach takes a step further by integrating mobile phone cameras for real-time posture feedback, a dynamic digital twin, and emotional state detection, ensuring adaptive, personalized rehabilitation exercises. This comprehensive approach addresses both the physical and emotional aspects of rehabilitation in real time, making it far more interactive and responsive compared to this patent's broader disease-state prediction framework.

[0018] US20220346703A1 – System and Method for Analyzing User Physical Characteristics and Prescribing Treatment Plans to the User

[0019] This patent uses camera-based body tracking to assess musculoskeletal health and generate treatment plans based on user form. While it offers posture analysis, it lacks advanced muscle fatigue detection or the real-time adaptive elements that would allow rehabilitation to evolve based on ongoing progress. Our solution is far more advanced, as it integrates continuous camera-based posture analysis with a dynamic digital twin for real-time tracking of posture and muscle activity. The system adapts exercises based on emotional states and contextual data such as biorhythms, religion, and demographics, allowing for personalized and holistic rehabilitation strategies that go far beyond static treatment prescriptions.

[0020] CN111789595A – Artificial Intelligence Scoliosis Real-Time Supervision Early Warning System Based on Cloud Platform

[0021] This patent focuses on spinal angle data captured from wearables to detect scoliosis and provide early warnings. It is limited to monitoring a specific condition and does not offer broad musculoskeletal rehabilitation or address the emotional state or muscle fatigue of the user. In contrast, our invention provides real-time feedback across the entire body, continuously updating a 3D digital twin and adapting rehabilitation exercises based on posture, alignment, fatigue, and emotional state. This integration of a dynamic digital twin, contextual data, and real-time AI-powered adjustments enables comprehensive musculoskeletal rehabilitation, extending far beyond the scoliosis-specific monitoring described in this patent.

[0022] US11839759B2 – Systems and Methods for Managed Training and Rehabilitation via Electrical Stimulation

[0023] This patent involves an electrical stimulation suit to collect biodata for rehabilitation and training, focusing on muscle engagement through pre-determined stimulation patterns. However, it does not incorporate posture correction or real-time adaptive exercise modifications. Our invention, on the other hand, utilizes camera-based posture tracking and wearable sensors to provide continuous, real-time feedback on muscle fatigue, posture, and emotional states, adjusting the rehabilitation process dynamically. By integrating AI to adapt the rehabilitation plan based on ongoing feedback, our system offers a much more comprehensive and personalized rehabilitation experience compared to the fixed patterns used in this patent.

[0024] US20220072381A1 – Method and System for Training Users to Perform Activities

[0025] This patent captures real-time video of a user and compares it to an expert's performance for feedback. While this system is effective for tracking motion, it does not incorporate real-time adjustments based on emotional states, fatigue levels, or contextual data, which are critical components in personalized rehabilitation. Our invention integrates advanced AI that not only provides real-time posture correction but also adjusts exercises dynamically based on real-time emotional and physical conditions, ensuring that each rehabilitation session is tailored to the user’s specific needs.

[0026] US20240001196A1 – Artificial Intelligence Assisted Personal Training System, Personal Training Device and Control Device

[0027] This patent introduces a wearable garment with EMG / IMU sensors to track muscle activity. While it monitors muscle engagement, it focuses on performance optimization through pre-set patterns and does not provide real-time rehabilitation adjustments based on emotional or physical states. Our system, in contrast, integrates AI-driven real-time posture correction, muscle fatigue monitoring, and emotional state detection, ensuring that rehabilitation exercises are continually adjusted based on the user’s progress and current condition, providing a much more comprehensive and responsive rehabilitation solution.

[0028] Proper management of rehabilitation processes, patient progress, and emotional well-being presents a significant challenge in the healthcare industry. The complexities of coordinating physical rehabilitation exercises, monitoring emotional states, and ensuring personalized care can lead to inefficiencies and hinder the recovery process. Additionally, factors such as fluctuating patient conditions, emotional stress, and fatigue can complicate rehabilitation planning, making it difficult to deliver optimal care. This invention provides a comprehensive system that integrates advanced technologies, including a digital twin engine for real-time monitoring of posture and rehabilitation progress, an AI-driven adaptive exercise module that dynamically adjusts exercises based on real-time feedback, and a continuous emotional state detection module to track stress and fatigue levels. Furthermore, the predictive analytics engine forecasts potential setbacks in recovery, while the real-time injury prevention module ensures exercises are modified to reduce risk. The AI-powered virtual coaching module provides personalized guidance and motivation, while the Telehealth integration enables remote monitoring and adjustments by healthcare providers. The system’s ability to integrate contextual data, such as biorhythms, religious practices, and demographic information, ensures a truly personalized approach to rehabilitation. By streamlining these processes, the invention improves the overall management of rehabilitation, reducing the risk of injury, enhancing patient engagement, and optimizing recovery outcomes.

[0029] In the current rehabilitation industry, patients, healthcare providers, and fitness professionals often rely on a fragmented array of tools for tracking patient progress, managing exercise routines, and monitoring emotional states during rehabilitation. These disconnected approaches lead to significant inefficiencies and missed opportunities for improving patient outcomes. First, rehabilitation exercises are often not personalized to the patient's emotional and physical condition. While some systems may track basic posture or movement data, there is typically no unified system that dynamically adjusts the intensity or type of rehabilitation exercises based on real-time emotional states such as fatigue or stress. As a result, patients may be overexerted or pushed too hard during periods of emotional distress, which hinders recovery or may even result in injury.

[0030] Second, the posture correction process itself is rarely based on real-time, accurate data. Current solutions often rely on static models of ideal posture or infrequent assessments of physical condition. If a patient’s posture deviates from the ideal model during an exercise, feedback is often delayed or inaccurate, making it difficult to correct the issue in real-time. Even when devices track posture, the feedback is typically isolated to the exercise session without considering the patient's overall progress or long-term recovery needs. Moreover, these systems often lack integration with data from wearable sensors, such as accelerometers or EMG sensors, which can track muscle fatigue or joint stability in real time. Without this data, rehabilitation may be based on incomplete or outdated information, reducing the effectiveness of the program.

[0031] Additionally, emotional state monitoring is frequently disconnected from rehabilitation systems. Many current platforms only address physical performance, leaving patients’ mental health or emotional well-being out of the equation. Emotional states, such as stress, anxiety, or depression, can significantly affect a patient’s willingness to engage in rehabilitation exercises, yet these factors are not consistently measured or integrated into the rehabilitation process. For example, a patient may show visible signs of stress or fatigue through facial expressions or changes in heart rate variability (HRV), but there is no automated system that adapts rehabilitation exercises in real-time based on these emotional states.

[0032] Furthermore, existing systems often fail to account for critical contextual factors, such as a patient’s biorhythm, religious practices, or demographic information. These factors can have a profound impact on both physical and emotional well-being during rehabilitation. For instance, patients may experience fatigue during certain periods of the day based on their biorhythms or be unable to fully engage in rehabilitation exercises during religious observances, like fasting. However, current systems do not dynamically adjust exercises based on these factors, leading to ineffective rehabilitation and poor patient engagement.

[0033] Another key issue is the lack of integration between real-time feedback systems and telehealth platforms. Many current systems track progress and provide feedback on individual exercises but do not allow healthcare providers to remotely monitor the patient’s rehabilitation progress. As a result, healthcare professionals are often unable to intervene promptly or make adjustments to the rehabilitation plan based on the patient’s evolving physical and emotional states. This limits the ability of telehealth professionals to ensure that patients are progressing as expected and makes it difficult to address problems that arise during the rehabilitation process, such as overexertion or misalignment.

[0034] Lastly, predictive analytics is often not utilized to proactively adjust rehabilitation exercises. When data is collected, it is frequently used for immediate corrections but not for long-term predictions of a patient’s recovery trajectory. For example, if a patient shows signs of muscle fatigue or postural misalignments, there is often no system in place to predict when they may face a plateau in their progress or when an injury might occur. Without predictive modeling to anticipate these issues, the rehabilitation process can be reactive instead of proactive, leading to potential setbacks that could have been avoided.

[0035] Taken together, these limitations create significant inefficiencies and missed opportunities in the rehabilitation industry. Without an integrated system to collect real-time posture and emotional state data, personalize rehabilitation exercises, adjust based on contextual information, and provide remote monitoring, the rehabilitation process remains fragmented and reactive. These issues result in ineffective rehabilitation, delayed recovery, and a lack of adaptability, increasing the overall cost of care and hindering patients’ ability to recover effectively.Solution of problem

[0036] The invention is a software-based personalized rehabilitation system designed to deliver adaptive, real-time rehabilitation exercises. It achieves this by collecting and analyzing data from a variety of sensors and sources, including posture and movement tracking, emotional state detection, contextual data (such as biorhythms, religion, and demographics), and predictive analytics. This enables the system to personalize rehabilitation exercises for the user’s physical, emotional, and cultural needs. A key feature of the system is its guidance system, which ensures that the device used to capture the necessary data (such as a mobile phone, tablet, or any other device capable of capturing video and images and having a processing unit) is placed in the correct position to maximize the accuracy and effectiveness of the rehabilitation process. The cloud-based infrastructure and modular architecture ensure scalability and adaptability, enabling seamless integration of new features, such as virtual coaching and telehealth integration, to improve the rehabilitation experience.

[0037] Posture and movement data are primarily gathered through the device’s camera, which is positioned at a distance from the user, ensuring that it captures the full range of the user’s body. Using advanced pose estimation algorithms (such as OpenPose, PoseNet, and DeepLabCut), the camera tracks key body landmarks, such as the shoulders, elbows, hips, knees, and ankles. These algorithms process the image in real time, comparing the user’s posture to an ideal reference model based on physiotherapy principles or personalized baseline data. Deviations from the ideal posture are detected, and corrective feedback is generated. This feedback is delivered through visual cues on the screen, auditory instructions (e.g., voice commands), or haptic signals (such as vibrations or pulses) based on whether the user is focusing on the screen or looking away. In addition to the camera data, wearable sensors (such as accelerometers and gyroscopes) are employed to track more detailed movements, particularly to monitor muscle activity and joint stability during exercise. These sensors measure the range of motion and muscle fatigue, providing data that the camera alone cannot capture. This combined data ensures that posture corrections are made in real time, with a high degree of accuracy.

[0038] A central component of the system is the creation of a dynamic digital twin, a 3D virtual representation of the user’s musculoskeletal system. This model is continuously updated with real-time data collected from the device camera and wearable sensors. The digital twin is created through a process of pose estimation and 3D model generation using multi-view stereo (MVS) and structure-from-motion (SfM) algorithms to reconstruct a 3D model from 2D camera images. For more precise joint tracking, the system uses graph convolutional networks (GCNs) and kinematic chain-based models to ensure accurate representation of the user's posture and movement. These techniques enable the system to continuously update the digital twin with data from the wearable sensors (accelerometers, gyroscopes) and camera, ensuring a dynamic, evolving view of the user’s body. As the user progresses in their rehabilitation, the system uses machine learning models such as long short-term memory (LSTM) and recurrent neural networks (RNNs) to assess and predict future misalignments or risk factors that may arise in the musculoskeletal system, adjusting the rehabilitation plan to prevent injury proactively. For example, if the digital twin detects a developing misalignment in the user’s spine or joints, the system can proactively adjust the rehabilitation plan to prevent injury. Additionally, by constantly updating the digital twin, the system ensures that the rehabilitation exercises are always tailored to the user’s current physical condition and recovery progress.

[0039] Emotional state detection is integrated into the system to ensure that rehabilitation exercises align with both the user’s physical and emotional needs. This module collects data from two primary sources: the device camera and physiological sensors embedded in wearable devices. The camera analyzes facial expressions, such as eye strain, brow furrowing, and lip tightening, which are indicative of stress, fatigue, or discomfort. These facial cues are processed by computer vision algorithms using Convolutional Neural Networks (CNNs) to assess the user’s emotional state. In addition, the system monitors heart rate variability (HRV), skin temperature, Galvanic Skin Response (GSR), and electromyography (EMG) from wearable sensors to measure physiological stress, emotional fluctuations, and muscle tension. Data is collected not only during rehabilitation sessions but continuously throughout the day whenever the user interacts with their device, enabling the system to track emotional state fluctuations in real time. This continuous monitoring allows the system to adjust exercises dynamically, reducing intensity or recommending relaxation techniques when the user is stressed or fatigued.

[0040] Contextual data is another important element in personalizing the rehabilitation process. Biorhythms, derived from the user’s birthdate, are analyzed to predict the user’s natural cycles of physical, emotional, and intellectual states. This data allows the system to adjust exercises according to the user’s energy levels or emotional state. For example, during periods of lower energy, the system may recommend gentler exercises or more frequent rest breaks. Religious practices, such as fasting during Ramadan or other culturally significant events, are also factored into the system’s adjustments. If the user is fasting, the system can lower exercise intensity to accommodate changes in the user’s physical state. Additionally, demographic data (e.g., age, gender, and cultural background) ensures that the system tailors the rehabilitation process to the user’s unique physiological and cultural needs. For female users, the system incorporates menstrual cycle tracking, adjusting exercise intensity based on hormonal fluctuations that affect energy levels, joint stability, and physical performance.

[0041] The adaptive AI module is at the heart of the system’s flexibility and responsiveness. Using reinforcement learning, the AI analyzes real-time data from posture tracking, emotional state detection, and contextual information to adjust the intensity, type, and frequency of rehabilitation exercises. The AI learns from the user’s behavior over time, adapting the rehabilitation program to optimize recovery. For instance, if the system detects a high level of fatigue or stress, the AI will adjust the exercises to be less intense, focusing on more restorative movements or relaxation techniques. Conversely, if the user demonstrates significant progress, the AI can increase the challenge of the exercises, introducing more complex movements or higher resistance. The AI’s adaptive nature ensures that the system continuously refines the rehabilitation process, ensuring it remains effective as the user’s physical and emotional states evolve.

[0042] The system features a personalized virtual coaching module, powered by AI. The AI-powered virtual coach provides users with real-time feedback, motivation, and guidance throughout their rehabilitation journey. It monitors the user’s performance and emotional responses, adjusting the rehabilitation exercises and providing real-time encouragement based on their progress. The integration of Natural Language Processing (NLP) allows users to interact with the virtual coach using voice commands, making the system more intuitive and user-friendly. This AI-powered coach serves as a personalized guide, helping users stay motivated and on track with their rehabilitation program.

[0043] Telehealth integration enables healthcare providers to monitor user progress remotely. The system uses APIs to securely share real-time data with telehealth platforms, ensuring that healthcare providers have access to up-to-date information about the user’s rehabilitation status. This integration allows healthcare professionals to intervene when necessary, making adjustments to the treatment plan based on real-time data. Secure data transmission ensures compliance with healthcare data privacy regulations, such as HIPAA, protecting sensitive user information.

[0044] The system employs advanced data analytics for predictive insights, utilizing predictive modeling techniques to analyze historical data and forecast future rehabilitation outcomes. This enables the system to proactively adjust exercises before potential issues, such as plateaus or injuries, arise. The progress dashboards offer users a clear view of their rehabilitation journey, displaying metrics such as posture improvement, muscle recovery, and emotional well-being, helping users stay engaged and motivated.

[0045] Cloud integration serves as the backbone for data storage, analysis, and long-term monitoring. All user data—collected from posture tracking, emotional state detection, wearable sensors, and contextual data—are uploaded to a cloud-based platform. The data is transmitted via secure protocols such as HTTPS and TLS encryption, ensuring the privacy and integrity of sensitive information. This platform provides centralized storage and real-time processing, ensuring seamless data flow and accessibility. The cloud infrastructure allows for scalable data processing, enabling the system to handle large volumes of data without performance degradation. Predictive analytics, powered by machine learning, are applied to this data in the cloud, allowing the system to forecast potential setbacks or recovery plateaus and adjust the rehabilitation plan accordingly. The cloud-based analytics also support healthcare providers by offering access to detailed, up-to-date patient data, enabling remote monitoring and timely intervention.

[0046] The system is compatible with wearable health devices, such as smartwatches and fitness trackers, enhancing data accuracy and providing a more comprehensive view of the user’s health. The system integrates data from these devices (e.g., step count, sleep patterns, and activity levels) into the rehabilitation program, creating a unified health profile. This integrated profile offers both users and healthcare providers a holistic view of the user’s physical and emotional health, facilitating more informed decision-making regarding the rehabilitation process.

[0047] The system’s modular architecture ensures that it remains adaptable and scalable. New features, such as predictive modeling, virtual coaching, and biofeedback, can be seamlessly integrated into the system without disrupting its core functionality. The cloud-based platform ensures that the system can grow with new technologies, continuously enhancing the user experience while maintaining the system's cohesion and unity of the invention.

[0048] The personalized rehabilitation system described herein offers several distinct advantages over existing solutions, transforming how rehabilitation exercises are tailored and delivered:

[0049] Personalized and adaptive rehabilitation

[0050] Unlike traditional systems that offer static or one-size-fits-all rehabilitation plans, this system adjusts exercises dynamically based on real-time data from multiple sources, including posture tracking, muscle activity, emotional state, and contextual factors such as biorhythms and religious practices. This continuous adaptation ensures that each user receives a rehabilitation plan uniquely suited to their current physical and emotional state, maximizing recovery potential while minimizing the risk of overexertion or injury.

[0051] Holistic approach to recovery

[0052] By integrating physical performance tracking with emotional state monitoring, the system accounts for both the body’s needs and the mind’s influence on rehabilitation. This dual-focus on emotional and physical health enables a more effective and sustainable recovery process, ensuring that patients are not only physically restored but also mentally supported throughout their rehabilitation journey.

[0053] Proactive injury prevention

[0054] The system continuously monitors muscle fatigue and posture alignment, providing real-time feedback to adjust exercises before injuries occur. Unlike reactive systems that only respond after a problem arises, this approach minimizes downtime and accelerates recovery by proactively addressing potential risks before they lead to injury or setbacks.

[0055] Seamless integration with healthcare providers

[0056] Through telehealth integration, healthcare providers can remotely monitor a patient’s progress and make adjustments to the rehabilitation plan as necessary. This ensures that care is not confined to in-person visits and that patients receive continuous oversight, improving treatment outcomes and reducing the need for costly in-person consultations.

[0057] Data-driven decision-making

[0058] The cloud-based analytics engine aggregates data from various sensors and user inputs, enabling predictive modeling of rehabilitation outcomes. By using this data, the system can anticipate potential setbacks or plateaus in recovery and modify the rehabilitation plan to address them proactively, ensuring a smoother, more efficient recovery process.

[0059] Enhanced user engagement and adherence

[0060] With features like AI-powered virtual coaching, real-time feedback, and motivational support, the system increases patient adherence to rehabilitation plans. Personalized feedback and adjustments to the exercise regimen ensure that users remain engaged and motivated throughout the recovery process, improving overall outcomes.

[0061] Scalable and adaptable technology

[0062] The system’s modular architecture allows for easy integration of new technologies, such as biofeedback, AR / VR components, or additional sensors. This scalability ensures that the system can evolve alongside advances in rehabilitation techniques and technologies, providing long-term value and future-proofing the solution.

[0063] Comprehensive data integration

[0064] The system consolidates data from wearable devices, smartphones, and other sensors into a unified health profile that offers a complete view of the user’s physical and emotional well-being. This integration provides a more accurate and comprehensive assessment of the user’s progress, enabling both users and healthcare providers to make better-informed decisions regarding the rehabilitation process.

[0065] : Overall System Architecture of the Personalized Rehabilitation System

[0066] : Detailed Digital Twin and Data Processing Flow

[0067] :shows the entire architecture of the personalized rehabilitation system as a series of interconnected blocks. Starting at (101) Input Devices, the device camera and wearable sensors capture both visual and physiological data. This raw data is simultaneously directed to (102) the Posture and Movement Tracking Module and (103) the Emotional State Detection Module.

[0068] The Posture and Movement Tracking Module (102) processes the video data using advanced pose estimation algorithms (e.g., OpenPose, PoseNet, DeepLabCut) to identify key body landmarks. The resulting data flows into (104) the Dynamic Digital Twin Module, where a real-time 3D model of the user’s musculoskeletal system is continuously updated. Meanwhile, the Emotional State Detection Module (103) utilizes facial analysis and physiological sensor inputs to determine stress, fatigue, and other emotional indicators. These data are then combined with additional external contextual inputs (e.g., biorhythm, religion, demographic data) in (105) the Contextual Data Integration Module.

[0069] Both streams of processed data are sent to (106) the Adaptive AI Module, which uses reinforcement learning to dynamically adjust the rehabilitation exercise regimen based on the combined input. The AI module sends the refined rehabilitation parameters to (107) the Cloud-Based Data Analytics and Injury Prevention Module, where long-term trends are monitored using predictive analytics. This module also sends feedback back to the AI module for continuous adjustment. Finally, the system integrates with (108) Telehealth Integration to allow remote monitoring and with (109) the AI-Powered Virtual Coaching Module to provide real-time motivational and corrective feedback to the user.

[0070] :provides a detailed view of the digital twin creation and data processing flow. At the top, (201) the Image Acquisition Block (device camera) and (202) the Wearable Sensor Data Input Block collect raw data. Arrows from both (201) and (202) converge into (203) the Pose Estimation Block, where specific algorithms (OpenPose, PoseNet, DeepLabCut) extract key body landmarks.

[0071] From (203), an arrow points to (204) the 3D Reconstruction Block, which applies MVS and SfM algorithms to generate a preliminary 3D model. The output then moves to (205) the Joint Tracking Block, where advanced models (GCNs, kinematic chain models) refine the joint positions and overall skeletal structure. The refined data is used to update (206) the Dynamic Digital Twin, a continuously updated 3D representation of the user’s musculoskeletal system.

[0072] An arrow from (206) directs data into (207) the Predictive Analytics Block, where LSTM and RNN models analyze historical and real-time data to forecast potential misalignments or injury risks. The results feed into (208) the Adaptive AI Module, which adjusts the rehabilitation program based on these predictions. Finally, (208) outputs are delivered as corrective actions to (209) the Feedback Generation Block, which then communicates real-time guidance to the user.Examples

[0073] Example of dynamic rehabilitation adjustment based on real-time data and emotional state

[0074] Imagine a user recovering from knee surgery who is following a rehabilitation program designed by the system. The user begins a set of knee exercises tracked by the posture and movement feedback module, which detects slight misalignments in the knee during squats. At the same time, the emotional state detection module analyzes the user’s facial expressions and HRV data, indicating fatigue and stress. The system integrates this information and dynamically adjusts the rehabilitation plan: the adaptive AI module reduces the intensity of the exercise, replacing it with more gentle movements and incorporating relaxation techniques. This real-time adjustment helps the user avoid overexertion, improve recovery, and ensure the exercises are appropriate for both their physical and emotional states.

[0075] Example of injury prevention with predictive analytics and telehealth integration

[0076] Consider a user undergoing rehabilitation for chronic shoulder pain. The system continuously monitors the user's posture, using the digital twin engine to track changes in alignment and movement patterns. The predictive analytics module, analyzing historical data, detects that the user is nearing a muscle fatigue threshold, while the real-time injury prevention module identifies minor deviations in shoulder posture that could lead to further injury. Simultaneously, the telehealth integration module allows the user's healthcare provider to remotely view these data points and intervene by modifying the rehabilitation plan, suggesting specific corrective exercises, and adjusting the intensity. This integrated response prevents a potential setback by proactively adjusting the rehabilitation program based on both physical and predictive data.

[0077] Example of personalized rehabilitation with contextual data integration and virtual coaching

[0078] Imagine a user who has been following a rehabilitation program for a back injury. The system takes into account contextual data such as the user’s biorhythms and menstrual cycle, which can influence energy levels and joint stability. The system’s adaptive AI module uses this contextual data to predict the user’s energy fluctuations and adjusts the program, recommending gentle stretching exercises during times of lower energy. At the same time, the AI-powered virtual coaching module provides motivational feedback, encouraging the user through these gentler exercises while explaining how these adjustments fit into their recovery plan. The system’s ability to incorporate contextual data and provide personalized coaching ensures the rehabilitation program remains effective and adaptable to the user’s changing needs.

[0079] Example of real-time adjustments during a stress episode with predictive insights

[0080] Imagine a user participating in rehabilitation for knee osteoarthritis, but during the exercise session, their facial expression analysis from the emotional state detection module and HRV data indicates high levels of stress. The adaptive AI immediately adjusts the planned rehabilitation session by reducing the intensity of the exercises. Simultaneously, the system uses predictive insights from the cloud-based analytics to forecast that the user's stress levels could interfere with muscle recovery and future progress. As a result, the system recommends a more restorative session, including breathing exercises and relaxation techniques, all while continuously monitoring the user’s emotional state to ensure they are ready for a return to more intense exercises. The real-time data synchronization ensures that the rehabilitation program remains aligned with both physical and emotional recovery, allowing for smoother transitions between high and low-intensity periods.

[0081] Example of integrating wearables with emotional state and physical recovery

[0082] Imagine a user recovering from a shoulder injury using both a fitness tracker and the system’s rehabilitation app. The wearable device continuously tracks the user's step count, sleep patterns, and heart rate variability (HRV). The emotional state detection module analyzes data from the tracker to assess signs of stress or fatigue. If the system detects high levels of stress or insufficient sleep, it adjusts the rehabilitation exercises accordingly, suggesting lighter joint mobility exercises and incorporating stress-relieving activities like mindfulness breathing. Meanwhile, the cloud-based analytics processes historical data to forecast potential fatigue levels and adapt future exercises, ensuring the recovery plan remains optimized and aligned with the user’s ongoing physical and emotional state.

[0083] Example of adaptive AI with long-term progress tracking and injury prevention

[0084] Consider a user undergoing long-term rehabilitation for a spinal injury. The system uses the digital twin to continuously track their spinal alignment and movement patterns. Predictive analytics from the cloud-based platform reveals potential issues with spinal misalignment that could lead to future complications. The system not only adjusts the rehabilitation exercises in real-time using the adaptive AI module but also suggests more complex movements and strengthening exercises based on long-term progress. Simultaneously, the real-time injury prevention module ensures that as the intensity of the exercises increases, the user’s posture and muscle fatigue are continually monitored to avoid strain. This integrated approach ensures that the rehabilitation plan evolves over time to address both immediate recovery needs and long-term health goals, keeping the user on track for a full recovery.

[0085] This invention can be applied across a wide range of industries related to healthcare, rehabilitation, and wellness. It is particularly useful for healthcare providers, physical therapy centers, telehealth services, fitness professionals, and individual users seeking personalized rehabilitation solutions. By integrating real-time posture correction, emotional state monitoring, muscle fatigue tracking, and adaptive exercise programs, the invention addresses the challenges faced in managing personalized rehabilitation across diverse user groups.

[0086] The system can be deployed in clinical environments such as hospitals, physical therapy clinics, and rehabilitation centers, where it helps healthcare providers monitor and adjust rehabilitation exercises in real time. For telehealth applications, the system enables remote monitoring of patients’ recovery, allowing healthcare professionals to intervene and adjust the rehabilitation plan based on real-time data. The system is scalable and can be used for individual patients recovering from injuries, chronic pain management, or post-surgery rehabilitation, as well as for athletes seeking personalized injury prevention and performance enhancement.

[0087] The invention is adaptable to different rehabilitation contexts, from low-intensity programs designed for elderly patients to more intensive regimens for athletes or patients recovering from surgery. It can be integrated with a range of existing technologies, including wearable devices, smartphones, and fitness trackers, creating a unified health profile for the user. This enables a comprehensive, data-driven approach to rehabilitation, tailored to each individual’s physical, emotional, and cultural needs.

[0088] Whether deployed through cloud-based systems, on-premise servers, or hybrid infrastructures, the system can easily integrate with existing healthcare networks, electronic health records (EHR), and telehealth platforms. Its flexible architecture ensures that it can scale from small private clinics with a few users to large-scale, multi-user healthcare facilities with extensive patient data and real-time rehabilitation monitoring needs. The system's ability to collect and analyze a wide range of sensor data, coupled with its predictive analytics and adaptive feedback mechanisms, ensures that it is a versatile and valuable tool for improving rehabilitation outcomes across the healthcare industry.

Claims

A software-based personalized rehabilitation system for providing dynamic rehabilitation to a user, comprising: The system collects real-time posture and movement data through a device capable of capturing video and images, which is processed to detect deviations from ideal posture and provide corrective feedback through visual, auditory, or haptic signals. The system integrates an emotional state detection module that analyzes the user’s emotional state using facial expressions and physiological sensor data, and adjusts rehabilitation exercises based on real-time emotional states such as stress or fatigue; A dynamic digital twin is created and continuously updated to model the user’s musculoskeletal system, reflecting posture, alignment, and progress. The system uses contextual information such as biorhythm, religion, and demographic factors to adapt the rehabilitation process to each user’s unique emotional and physical conditions. The system integrates an adaptive AI module that dynamically adjusts the intensity and type of rehabilitation exercises based on the user’s ongoing progress and emotional state; The system further includes a cloud-based data analytics and injury prevention module to monitor long-term rehabilitation progress and predict potential injuries based on posture deviation, muscle fatigue, and performance metrics, providing real-time corrective feedback and modifying exercises to optimize the rehabilitation process; The system integrates telehealth platforms through APIs, allowing healthcare providers to remotely monitor the user's progress and adjust the rehabilitation plan as needed; The system includes an AI-powered virtual coaching module that provides real-time feedback, guidance, and encouragement to the user, adjusting rehabilitation exercises and offering motivational support based on the user’s performance and emotional state.The system of claim 1, wherein the posture and movement feedback module tracks real-time body movements using pose estimation algorithms to compare the user’s posture with an ideal reference model and provides corrective feedback when deviations from the ideal model are detected. Feedback is prioritized based on user gaze, where haptic feedback is used if the user is not looking at the screen and visual feedback is used when the user is focused on the screen.The system of claim 1, wherein the dynamic digital twin module continuously updates a 3D virtual representation of the user’s body using real-time data from a device capable of capturing video and images and sensor data from accelerometers and gyroscopes, reflecting changes in posture, alignment, and rehabilitation progress. The digital twin is used to predict future postural alignment and recommend preemptive adjustments to the rehabilitation plan.The system of claim 1, wherein the emotional state detection module analyzes facial muscle movements, including eye strain, brow furrowing, and lip tightening, alongside physiological data (e.g. heart rate variability (HRV), skin temperature, Galvanic Skin Response (GSR), and electromyography (EMG)) to detect stress, fatigue, or emotional fluctuations, and adjusts the rehabilitation plan by reducing exercise intensity or introducing relaxation techniques when a high emotional or fatigue level is detected.The system of claim 1, wherein the system incorporates contextual data, including the user’s biorhythm (derived from birthdate), religious practices (such as fasting during Ramadan or other observances), and demographic data (age, gender, cultural background, and menstrual cycle tracking for female users), to dynamically adjust rehabilitation exercises based on emotional fluctuations and physical condition predicted from these factors. This ensures that the rehabilitation process is tailored to the user’s emotional and physical state at any given time.The system of claim 1, wherein the adaptive AI module incorporates reinforcement learning to dynamically adjust the intensity and type of rehabilitation exercises based on real-time feedback from posture tracking, emotional state detection, and contextual data inputs. The AI uses performance feedback, including posture corrections and emotional responses, to refine the rehabilitation plan and optimize the user’s recovery trajectory.The system of claim 1, wherein the cloud-based data analytics and injury prevention module stores and analyzes long-term user performance data to track rehabilitation progress, identify trends in posture, fatigue, and recovery, and use predictive analytics (e.g., ARIMA or LSTM neural networks) to forecast potential plateaus or setbacks. Based on this data, the system provides real-time alerts and preemptively adjusts rehabilitation exercises to prevent injuries by recommending modifications to posture, movement range, or intensity.The system of claim 1, wherein the real-time injury prevention module continuously monitors muscle fatigue and posture deviation in real-time by combining accelerometer and HRV sensor data. The module detects overexertion or misalignments that may cause injury and provides corrective feedback or adjusts the rehabilitation exercises accordingly to mitigate injury risk. Additionally, the system utilizes reinforcement learning to track user performance, rewarding correct posture or consistent execution and adjusting exercise difficulty in subsequent sessions to ensure optimal recovery and minimize future injury risks.

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