Anterior Cruciate Ligament Protector With IMU/EMG Risk Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional knee braces lack the ability to detect real-time biomechanical risks, adapt to dynamic user movements, and respond to imminent injury scenarios, primarily offering static support or post-injury stabilization, and existing sensor-enabled systems are limited in their capability for on-the-fly intervention or fail to account for individual biomechanical variability.
Innovation Solution
A sensor-integrated knee brace system that incorporates inertial measurement units (IMUs) and electromyography (EMG) sensors to collect real-time biomechanical and neuromuscular data, using machine learning models to predict high-risk movements, and employs actuators for real-time feedback or intervention, with a hybrid actuation system and federated learning to adapt to individual user biomechanics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional passive knee braces are used, then structural support and stability are provided, but real-time injury prevention capability and adaptability to dynamic movements are lost
Solution Approach 1:
The knee brace transitions from a static passive structure to a dynamic active system through the integration of sensors, machine learning models, and actuators. The system continuously monitors biomechanical data and adjusts support forces in real-time based on detected movement patterns and injury risk assessments, enabling adaptive response to dynamic user movements while maintaining structural support.
Solution Approach 2:
The system implements closed-loop feedback by using sensors to detect real-time knee joint forces and movement patterns, processing this data through machine learning models to assess injury risk, and then activating actuators to apply corrective forces when high-risk movements are detected. This feedback mechanism enables the brace to actively prevent injuries while adapting to the user's dynamic movements.
2Measurement precision
If sensor-enabled systems are integrated, then real-time biomechanical detection capability is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system segments the complex injury prevention function into distinct modular components: sensor modules for data acquisition, machine learning models for risk assessment, and actuator modules for intervention. This segmentation allows each component to be optimized independently while working together as an integrated system, managing complexity through functional decomposition.
Solution Approach 2:
The knee brace system is designed as a multi-functional platform that combines structural support, real-time sensing, machine learning-based risk assessment, and active intervention capabilities. This universal design allows the same system to perform multiple functions (detection, analysis, and prevention) rather than requiring separate dedicated systems for each function.
3Reliability
If existing knee braces are used post-injury, then stabilization is provided, but proactive injury prevention and real-time intervention capability are lost
Solution Approach 1:
The system performs preliminary action by continuously monitoring biomechanical parameters and assessing injury risk before actual injury occurs. The machine learning model predicts high-risk movements in advance, allowing the actuators to apply preventive forces before the injury-causing movement completes, thereby preventing injury rather than merely stabilizing after injury occurs.
Solution Approach 2:
The knee brace provides continuous stabilization and monitoring rather than intermittent or passive support. The system continuously collects sensor data, processes it through machine learning models, and maintains ready-to-activate actuators, ensuring uninterrupted protective action throughout the user's activity rather than only during known high-risk moments.
Data Source
AI summary
A wearable knee brace system integrates inertial measurement unit (IMU) and electromyography (EMG) sensors with machine learning models to prevent anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injuries. The system collects real-time biomechanical and neuromuscular data and analyzes the data using supervised, personalized, or federated learning techniques to identify high-risk movement patterns. Upon detecting elevated injury risk, the system may issue real-time alerts or activate a hybrid actuation system comprising high-force, low-displacement actuators and low-force, high-displacement actuators to reduce joint loading. The system further supports personalized model adaptation using calibration activities and transfer learning, as well as privacy-preserving performance improvements through federated learning. Feedback is provided through visual, auditory, or haptic interfaces and may be integrated with rehabilitation tools or mobile applications. The system may be used in athletic, dance, clinical, or rehabilitative environments to enhance performance, optimize recovery, and reduce the risk of knee ligament injuries.