Augmented Neuromuscular Training System for ACL Injury Prevention
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Solution Overview
Problem
Current injury prevention programs for anterior cruciate ligament (ACL) injuries in athletes are ineffective due to noncompliance and limited reduction in injury risk, and they fail to adequately address the rising rates of ACL injuries, particularly in adolescent females.
Innovation Solution
An augmented neuromuscular training system that provides real-time, objective, and personalized biofeedback to athletes, using kinematic and kinetic data to adjust a visual stimulus shape in response to their movements, allowing athletes to intuitively correct biomechanical variables associated with ACL injury risk, thereby promoting implicit motor learning and reducing injury risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional injury prevention programs are used, then athletes receive standard training, but compliance is low and injury risk reduction is limited
Solution Approach 1:
The system provides real-time visual feedback through a display device that shows a stimulus shape changing in response to the athlete's biomechanical variables during exercise. This immediate feedback loop allows athletes to see the direct effect of their movements on the displayed stimulus, enabling self-correction and improving compliance with the training program while effectively reducing injury risk through targeted biomechanical correction.
Solution Approach 2:
The system enables athletes to autonomously correct their own biomechanics by observing how their movements affect the displayed stimulus shape. Rather than requiring constant supervision or complex instruction, athletes self-regulate their form based on the visual feedback, making the training program easier to comply with while maintaining effectiveness in reducing injury risk.
2Reliability
If real-time biofeedback is implemented, then sensorimotor learning is enhanced, but system complexity increases
Solution Approach 1:
The system extracts only the essential biomechanical variables related to injury risk from complex motion data and uses these to modulate the stimulus shape. By focusing on key variables rather than processing all movement data, the system achieves enhanced sensorimotor learning while keeping the feedback mechanism relatively simple and manageable.
Solution Approach 2:
The stimulus shape serves as an intermediary visual representation that translates complex biomechanical data into an intuitive form athletes can understand and respond to. This intermediary simplifies the interface between the complex measurement system and the athlete, enabling effective sensorimotor learning without requiring the athlete to directly interpret complex technical data.
3Measurement precision
If multiple biomechanical variables are monitored, then injury risk factors are better identified, but measurement complexity increases
Solution Approach 1:
The system combines multiple biomechanical variables (knee abduction moment, trunk lean, hip moment ratio, ground reaction force) into a single integrated visual stimulus that responds to the collective state of these variables. This merging approach maintains precise measurement of individual variables while simplifying the overall assessment into one coherent visual feedback display, reducing the complexity of interpretation.
Solution Approach 2:
The stimulus shape serves multiple functions simultaneously: it reflects the state of multiple biomechanical variables, provides guidance for correction, and serves as a universal interface for feedback. This multi-functionality allows comprehensive biomechanical assessment without requiring separate displays or complex interpretation for each variable.
Data Source
AI summary
An augmented neuromuscular training system and method for providing feedback to a user in order to reduce movement deficits associated with injury risk, prior injury or disease pathology.