Joint Stress Analysis Using Sensor Data and Range of Motion Limits
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Solution Overview
Problem
Current methods for analyzing joint stress in athletes, particularly in high-speed movements, fail to account for arthrokinematic effects, leading to inadequate injury risk assessment and performance analysis, as they primarily focus on osteokinematic movements without considering passive joint stresses.
Innovation Solution
A method that uses sensor data to determine the relative contributions of active and passive forces on joint stress by tracking joint angles and calculating stress contributions based on time spent outside the normal range of motion, integrating functions to model arthrokinematic forces, and combining these across multiple movements to estimate joint fatigue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion capture techniques are used to analyze osteokinematics, then gross movements of skeletal bones can be tracked, but joint stress analysis is incomplete because arthrokinematic effects are not captured
Solution Approach 1:
The patent segments joint motion analysis into two distinct components: osteokinematics (gross bone movements) and arthrokinematics (small surface movements). By separating these components and measuring them independently using sensor data, the system achieves comprehensive joint stress analysis without requiring complex integrated measurement systems.
Solution Approach 2:
The patent uses sensor data as an intermediary to indirectly measure arthrokinematic effects. Instead of directly measuring small joint surface movements with complex equipment, the system uses sensors to capture motion data and processes this information to derive joint stress metrics, simplifying the measurement approach while maintaining accuracy.
2Loss of information
If sensor data is used to track joint angles and calculate stress contributions, then comprehensive joint stress analysis including arthrokinematic effects is achieved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of joint motion into active and passive phases before detailed stress calculation. By identifying when a joint is within versus outside its normal range of motion beforehand, the system can apply appropriate stress models and integrate forces more efficiently, reducing computational complexity while maintaining information completeness.
Solution Approach 2:
The patent changes the parameter space by transforming raw sensor data into joint angle time series, then into stress contribution metrics. This parameter transformation simplifies the computational process by working with derived variables that directly relate to joint stress, rather than processing raw sensor signals through complex algorithms.
3Measurement precision
If the joint angle time series is compared to range of motion limits to identify time intervals outside normal range, then passive joint stress contribution can be calculated, but the analysis requires multiple processing steps
Solution Approach 1:
The patent performs preliminary comparison of joint angles to range of motion limits to identify time intervals outside normal range before detailed stress integration. This preliminary action organizes the data structure and identifies critical time periods, enabling more efficient subsequent integration calculations while maintaining accuracy.
Solution Approach 2:
The patent maintains continuous monitoring and comparison of joint angles against range of motion limits throughout the motion cycle. This continuous analysis ensures no stress-contributing intervals are missed while using efficient algorithms to process the continuous data stream, balancing accuracy with processing efficiency.
Data Source
AI summary
A method of calculating the stress on a joint by determining when a joint angle is outside the normal range of motion for the joint. Applications include analysis of elbow joint stress for baseball pitchers, for example to mitigate the risk of injury to the ulnar collateral ligament (UCL). During a movement such as a baseball pitch, sensor data is collected to track the position, orientation, or motion of body segments; joint angles are derived from this sensor data. Joint angles are then compared to a range of motion, which may be measured for each individual. Joint stress contributions from movements that exceed the range of motion may be combined over time to calculate cumulative joint fatigue. This joint fatigue measure may for example be used for pitchers to quantify recovery periods (days off) and future workloads (such as maximum pitch counts).