Longitudinal Gait Analysis System for Runner Biomechanics
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Solution Overview
Problem
Existing frameworks in biomechanics have not fully modeled variations in biomechanical or physiological relationships within a runner over greater periods of time, such as series of races or running sessions, nor considered the implications of these variations towards health and performance.
Innovation Solution
A computer-implemented method and system that obtain sensor data from multiple running sessions, determine values of variables of interest and predictor variables, model statistical relationships, and identify changes in these relationships across sessions, providing personalized analysis of biomechanical and physiological characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional discrete observation methods are used to model runner biomechanics, then the measurement process is simple, but the model cannot capture variations in biomechanical relationships over greater periods of time
Solution Approach 1:
The system transitions from static discrete observations to dynamic continuous monitoring, collecting biomechanical data throughout entire running sessions to capture temporal variations in gait parameters
Solution Approach 2:
The system implements continuous data collection across multiple running sessions rather than isolated discrete measurements, enabling longitudinal analysis of biomechanical relationship variations over time
2Loss of information
If comprehensive sensor data collection is implemented across multiple running sessions, then longitudinal analysis capability is improved, but data processing complexity increases
Solution Approach 1:
The system extracts and isolates specific biomechanical relationship parameters from comprehensive sensor data, focusing analysis on key variables such as vertical ground reaction force characteristics and gait parameters
Solution Approach 2:
The system introduces statistical modeling as an intermediary layer between raw sensor data and biomechanical insights, using regression analysis to transform complex multi-session data into interpretable relationship patterns
3Measurement precision
If statistical modeling is applied to each running session to identify relationships between variables, then performance insight accuracy is improved, but computational time increases
Solution Approach 1:
The system applies statistical modeling selectively to identify the most significant biomechanical relationships rather than analyzing all possible variable combinations, reducing computational burden while maintaining insight accuracy
Data Source
AI summary
Systems and methods relate to personalized analysis of physiology and/or biomechanical behaviors of a runner across one, two, three, four or more running sessions. Particularly, the personalized analysis may examine the relationships between variables of interest (VOIs) and their predictors, both within a single running session and across multiple running sessions. These relationship modeling techniques may be used, for example to gain insights into running performance, injury, training adaptation, and/or external effect attribution.