Motion Sensor System for ACL Injury Risk Assessment
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Solution Overview
Problem
Current systems fail to effectively monitor and assess the position, motion, and forces transferred to joints, muscles, and limbs in real-time, leading to a high risk of ACL injuries and inadequate tracking of fitness performance, particularly in athletes, where non-contact mechanisms account for over 70% of ACL tears and subsequent surgeries.
Innovation Solution
A computer-implemented method and system that utilizes sensors to capture objective motion data and combine it with subjective user feedback, analyzing neuromuscular efficiency to evaluate injury risk and fitness levels, providing a BaziFit score for rehabilitation progress and activity readiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used to capture objective motion data and analyze neuromuscular efficiency, then injury risk assessment and fitness evaluation are improved, but device complexity and cost increase
Solution Approach 1:
The system divides the monitoring function into multiple independent sensor components (accelerometers, gyroscopes, magnetometers) that can be separately implemented and combined. Each sensor captures specific motion parameters, and the computing device processes these segmented data streams independently before integrating them for comprehensive injury risk assessment.
Solution Approach 2:
The sensor system is designed to perform multiple functions: tracking position, measuring motion, assessing neuromuscular efficiency, evaluating fitness levels, and determining injury risk. The same hardware platform supports various assessment protocols and can be applied to different body parts and athletic activities, reducing overall system complexity through multi-functionality.
2Reliability
If real-time monitoring of position, motion, and forces is implemented, then ACL injury prevention is improved, but loss of time for data processing and analysis increases
Solution Approach 1:
The system pre-establishes injury risk thresholds and assessment criteria before monitoring begins. The computing device is pre-configured with protocols for analyzing neuromuscular efficiency and detecting dangerous motion patterns. This preliminary setup allows real-time data to be quickly compared against known risk parameters without requiring complex on-the-fly analysis.
Solution Approach 2:
The system continuously provides feedback by comparing real-time sensor data against established safety thresholds and injury risk models. When motion patterns indicate potential ACL stress or neuromuscular inefficiency, the system immediately alerts the user or coach, enabling rapid corrective action. This closed-loop feedback minimizes the time between data collection and actionable insights.
Data Source
AI summary
Systems, methods, and computer program products which facilitate the ability of a user to monitor and assess the location of and forces transferred to various joints, muscles, and limbs and their relative positions at each and every moment during normal daily activities, training loads of an exercise, or a competitive or high intensity athletic endeavors, in order to mitigate and reduce the risk of injury as well as to track fitness performance elements are disclosed. In an aspect, systems, methods, and computer program products are disclosed which utilize at least one sensor in order to capture a user's movement information during various tasks and exercises. This movement information may then be analyzed in order to determine quantifiable values for the user's likelihood of experiencing an injury and/or the user's overall fitness, generally. The systems, methods, and computer program products of the present disclosure may also be used to measure a user's neuromuscular efficiency and help the user make improvements thereto.