DESC:AI-Enhanced Wearable Walking Aid for Adaptive Mobility and Fall Prevention
Field of the Invention:
The present invention pertains to the field of assistive technology, specifically to wearable devices that incorporate artificial intelligence (AI) for enhancing mobility and stability. The invention is designed to assist individuals with walking impairments by providing real-time gait analysis, adaptive support, and fall prevention through the integration of sensors, machine learning algorithms, and mechanical assistive systems. It is particularly applicable to the fields of medical rehabilitation, elderly care, and mobility assistance for individuals with neuromuscular disorders.
Background:
Walking aids, such as canes, walkers, and wheelchairs, have long been used to assist individuals with mobility impairments due to aging, injury, or neuromuscular disorders. While these devices provide basic support, they are often limited in their ability to dynamically adapt to the user's specific needs or changing conditions. Traditional walking aids lack real-time feedback, which can lead to inadequate support, increased fall risk, and reduced user independence.
Recent advancements in sensor technology and wearable devices have introduced limited improvements, but most current solutions remain passive or semi-active, offering little or no personalization based on the user’s evolving gait patterns or environmental conditions. Moreover, these devices do not provide adequate predictive assistance, such as fall prevention, or actively assist users with correcting their movements.
One of the primary challenges for individuals using traditional mobility aids is the increased risk of falling, which can result in serious injury, particularly for the elderly. Falls are often caused by loss of balance or missteps, which traditional aids cannot preemptively address. Additionally, users with conditions like Parkinson’s disease, multiple sclerosis, or post-stroke limitations require varying degrees of support, which conventional aids fail to accommodate dynamically.
The existing state of the art does not offer the intelligent, adaptable, and proactive support that is needed to provide users with confidence, independence, and safety while walking. There is a need for advanced, AI-driven solutions that can not only assist but also learn from the user’s movements and respond accordingly to prevent falls, improve gait, and adapt to individual needs in real-time.
The present invention addresses these limitations by introducing a wearable walking aid system enhanced with AI and sensor technology. This system provides real-time gait analysis, adaptive assistance, predictive fall prevention, and long-term mobility monitoring, offering a significant improvement over existing devices.
Summary of the Invention:
The present invention is an AI-Enhanced Wearable Walking Aid designed to provide personalized and adaptive mobility assistance for individuals with walking impairments. Integrating advanced sensors, artificial intelligence, and real-time data processing, the invention offers a dynamic and intelligent solution for enhancing gait, preventing falls, and improving overall stability during movement.
The key innovation lies in its adaptive assistance, where the AI system continuously analyzes the user's gait and surrounding environment, automatically adjusting the level of support based on real-time conditions. The wearable aid can predict when a fall is likely to occur and respond by providing mechanical support or alerts to prevent injury.
The wearable design allows for ease of use, comfort, and mobility, while the sensor suite, including accelerometers, gyroscopes, and pressure sensors, monitors the user's movements in real time. The collected data is processed by machine learning algorithms, which identify irregularities in gait patterns and adjust the device’s support accordingly. Over time, the system learns the user’s unique movements and behavior, further optimizing the level of assistance.
Additionally, the invention features predictive fall prevention, where the AI can forecast potential falls by analyzing balance disruptions, terrain changes, or physical fatigue. In response, the device can engage mechanical stabilizers, alert the user through auditory or haptic feedback, or automatically correct posture to avoid a fall.
Another advantage of the invention is its long-term health monitoring capability. By collecting and analyzing movement data, the device can track the user's progress over time, providing valuable insights to healthcare providers for ongoing mobility management and rehabilitation.
This AI-based walking aid represents a significant leap forward in assistive technology by offering customizable, intelligent, and proactive support, improving user confidence, independence, and safety.
Detailed Description of the Invention:
The AI-Enhanced Wearable Walking Aid comprises a system designed to provide real-time adaptive mobility assistance through the integration of sensors, artificial intelligence (AI), and mechanical support mechanisms. This section provides a detailed explanation of its components, functions, and operational methods, along with potential embodiments and applications.
1. Core Components
a. Wearable Framework:
The wearable walking aid is designed to be worn comfortably on the lower limbs, waist, or as part of a lightweight exoskeleton. The framework is composed of flexible, lightweight materials (e.g., carbon fiber, polymer composites) that allow for comfortable long-term use while providing stability and support. It can be worn discreetly under clothing, or as an external support structure, depending on the specific embodiment.
b. Sensor Suite:
The wearable aid is equipped with a variety of sensors that collect data in real-time to monitor the user’s movements, environment, and balance. These include:
• Inertial Measurement Units (IMUs): Accelerometers, gyroscopes, and magnetometers measure acceleration, angular velocity, and orientation.
• Pressure Sensors: Located in the soles or legs, these sensors detect pressure distribution, weight shifts, and footfall during walking.
• Proximity Sensors: These monitor obstacles in the environment, ensuring safe navigation.
• Biometric Sensors (optional): Heart rate, body temperature, and muscle fatigue can be monitored to assess the user’s physical condition.
c. AI-Powered Control Unit:
The control unit, embedded within the wearable structure or worn separately (e.g., on a belt or harness), processes data from the sensors in real time. It houses the AI algorithms and machine learning models that analyze the user’s gait, balance, and posture. The control unit consists of:
• Processor: A lightweight, low-power AI processor optimized for continuous, real-time data processing.
• Machine Learning Algorithms: Trained on vast datasets of gait and movement patterns, the algorithms are capable of analyzing individual user behaviors and adapting the support system accordingly.
• Data Storage: For short-term storage of movement data, enabling analysis over time to fine-tune the system’s responses.
d. Mechanical Support Mechanisms:
Depending on the embodiment, the wearable walking aid may include mechanical actuators or hydraulic systems that assist with mobility by applying controlled force to the limbs, aiding in walking, standing, or sitting. These mechanisms are integrated into the wearable frame and provide:
• Active Support: Controlled by AI, actuators respond to movement irregularities, providing additional strength or balance as needed.
• Fall Prevention: If an imminent fall is detected, the system can engage mechanisms that either brace the user or stabilize them to prevent injury.
2. Operation and Adaptive Assistance
The AI-enhanced walking aid operates through a continuous feedback loop between the sensor suite and the AI-powered control unit. Key functions of the system include:
a. Real-Time Gait Analysis:
As the user walks, the sensors continuously monitor their movement, collecting data on leg position, stride length, joint angles, and weight distribution. The AI system processes this information to assess the user’s gait and identify any irregularities, such as uneven steps, poor posture, or signs of physical fatigue.
b. Adaptive Assistance:
Based on the real-time analysis, the AI adjusts the level of mechanical support to suit the user’s needs. For instance:
• If the system detects instability or weakness in one leg, it can increase the support provided by the mechanical actuators to help maintain balance.
• When the user is walking on uneven terrain, the system can increase stiffness in the frame to offer more stability.
• In rehabilitation scenarios, the device can gradually reduce assistance as the user’s strength improves, promoting muscle recovery.
c. Predictive Fall Prevention:
A key feature of the invention is its ability to prevent falls. The AI uses historical data, combined with real-time analysis, to predict potential falls by identifying early signs of instability, such as:
• Shifting center of gravity.
• Sudden changes in gait pattern.
• Obstacle detection or uneven surfaces.
When a fall risk is detected, the system responds by:
• Activating vibrational alerts or auditory warnings to notify the user.
• Engaging mechanical stabilizers or adjusting the user's posture to prevent the fall.
• In severe cases, it may automatically brace the user to minimize the impact.
d. User Feedback and Interaction:
The invention provides a user-friendly interface for interaction. Through a mobile application or device interface, users can:
• Monitor their walking data and progress over time.
• Adjust settings for the level of assistance, tailored to their condition or preferences.
• Receive feedback on their posture, gait, or fall risk alerts.
• Provide input using voice commands or gesture controls, enhancing ease of use for individuals with limited dexterity.
3. Embodiments
a. Lower-Limb Exoskeleton:
One embodiment is a lightweight exoskeleton worn on the legs and hips. It provides powered assistance for users who have difficulty walking due to muscle weakness or neurological disorders. The exoskeleton adjusts dynamically based on the user’s needs and environment, providing more strength on uneven ground or reducing support on flat surfaces.
b. Smart Cane or Walker:
Another embodiment involves traditional mobility aids, such as canes or walkers, enhanced with sensors and AI capabilities. These devices analyze the user’s gait and predict falls, offering stabilization when necessary. Unlike traditional aids, they adapt their level of assistance in real-time and provide alerts or corrective feedback for better posture and balance.
c. Rehabilitation and Therapeutic Devices:
For individuals undergoing physical therapy, an embodiment of the system may focus on progressive rehabilitation. It can be used to train and retrain muscles, adjusting the level of support based on the user’s improvement over time. The AI tracks recovery progress and optimizes assistance to encourage muscle engagement and coordination.
4. Applications and Advantages
a. Elderly Care:
This invention is particularly suited for elderly individuals at risk of falls. It offers them enhanced stability and confidence while walking, significantly reducing the likelihood of injury from falls.
b. Post-Surgery Rehabilitation:
Patients recovering from surgeries (e.g., knee replacements) can use the AI-enhanced walking aid to regain mobility, with the system adjusting support based on recovery stages.
c. Neurological and Musculoskeletal Conditions:
For individuals with conditions like Parkinson’s disease, cerebral palsy, or stroke-related impairments, the device provides customized, intelligent support that adapts to their unique needs, offering greater independence and mobility.
5. Methods of Implementation
The wearable device can be customized and calibrated for each user, beginning with a baseline gait analysis. After wearing the device for the first time, the AI will learn and adapt to the user’s specific walking style, storing this data for future reference. As the user continues to wear the device, it adjusts in real-time to changes in gait patterns or environmental conditions, offering tailored assistance.
Additionally, long-term data can be shared with healthcare professionals through cloud integration, aiding in diagnostics and treatment planning.
This AI-based wearable walking aid represents a significant improvement in assistive technology by providing intelligent, adaptive, and predictive support for individuals with mobility challenges, ultimately enhancing user safety, independence, and quality of life.
,CLAIMS:Claims:
1. Independent Claims
1. An AI-enhanced wearable walking aid comprising:
? A wearable exoskeleton frame configured to be worn on a user's lower limbs and hips;
? A sensor suite integrated into the exoskeleton frame, including inertial measurement units (IMUs), pressure sensors, and proximity sensors for collecting real-time data on the user's movement, gait, and environment;
? An AI-powered control unit that processes sensor data to analyze the user's gait, posture, and balance in real-time, and dynamically adjusts the assistance provided by the device;
? Mechanical support mechanisms embedded within the exoskeleton frame that provide active assistance in response to the AI’s analysis, aiding the user in walking, maintaining balance, and preventing falls;
? Predictive fall prevention functionality wherein the AI identifies fall risks based on sensor data and initiates corrective actions, including mechanical stabilization, posture adjustment, or user alerts.
2. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the AI-powered control unit includes machine learning algorithms that adapt to the user’s unique gait patterns over time, improving personalized support and assistance.
3. The AI-enhanced wearable walking aid as claimed in claim 1, further comprising a user interface for controlling and monitoring the device, wherein the interface allows the user to customize the level of assistance, receive feedback on gait and balance, and access historical data on mobility performance.
4. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the sensor suite further includes biometric sensors for monitoring the user’s physiological parameters, including heart rate and muscle fatigue, to adjust support based on the user’s physical condition.
2. Dependent Claims
1. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the mechanical support mechanisms include actuators positioned at the user’s knee and ankle joints to assist with movement, balance, and fall prevention.
2. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the AI-powered control unit predicts changes in terrain based on proximity sensor data and adjusts the mechanical support to compensate for uneven surfaces or obstacles.
3. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the AI-powered control unit provides auditory, visual, or haptic feedback to the user when fall risks are detected, allowing the user to take corrective action.
4. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the mechanical support mechanisms are configured to reduce their assistance over time as part of a rehabilitation program, promoting the user's muscle development and recovery.
5. The AI-enhanced wearable walking aid as claimed in claim 3, wherein the user interface is a mobile application connected wirelessly to the control unit, allowing remote monitoring and control of the walking aid’s functions.
6. The AI-enhanced wearable walking aid as claimed in claim 1, wherein the AI-powered control unit stores movement and gait data over time for analysis by healthcare professionals, providing long-term monitoring of the user's mobility condition.