Gait Analysis System for Uncontrolled Kinetic Parameter Estimation
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Solution Overview
Problem
Current gait analysis methods are limited as they primarily operate in controlled environments, producing unrepresentative and cautious motion data, and existing analytical models are insufficient for accurately calculating kinetic parameters from kinematic data, especially in uncontrolled settings like sports fields or real-life scenarios.
Innovation Solution
A computer-implemented method that receives sensory data from uncontrolled environments, extracts kinematic parameters, classifies gait types, and uses these classifications to estimate kinetic parameters, enabling the creation of specific models for each gait type, which can be used to improve gait performance and reduce injuries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gait analysis is performed in controlled environments using traditional methods, then measurement reliability is improved, but adaptability to real-world scenarios deteriorates
Solution Approach 1:
The system dynamically adapts to different gait types (walking, running, jumping) detected in real-time during uncontrolled environments. The model selectively applies appropriate kinetic parameter calculation methods based on the detected motion type, enabling reliable measurements across varying conditions rather than requiring fixed controlled environments.
Solution Approach 2:
The patent changes the approach from requiring controlled environmental parameters to adapting to varying motion parameters. By detecting gait type and selecting appropriate calculation models based on motion characteristics (velocity, acceleration patterns), the system maintains measurement reliability while operating in diverse uncontrolled environments.
2Device complexity
If general gait analysis models are used without classification, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments gait analysis into distinct gait types (walking, running, jumping, etc.) through classification. Each gait type has its own specialized calculation model for kinetic parameters, allowing precise measurements tailored to specific motion patterns while maintaining overall system manageability through modular model selection.
Solution Approach 2:
The patent replaces a single complex mechanical calculation model with a intelligent selection system that chooses appropriate simplified models based on detected gait type. This substitution using classification and model selection achieves high precision without requiring all complex calculation capabilities to be active simultaneously.
3Productivity
If kinetic parameters are calculated without gait type classification, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary gait type classification before kinetic parameter calculation. By detecting and classifying the motion type first (walking, running, jumping), the system prepares the appropriate calculation model in advance, ensuring both rapid processing and high precision by avoiding unnecessary complex calculations for simple gaits while applying appropriate methods for complex motions.
Data Source
AI summary
A computerized method performed by a processor, and computer program product the method comprising: receiving sensory data of motion by a human subject, the sensory data obtained during motion in an uncontrolled environment; extracting features from the sensory data; identifying a plurality of strides from the features extracted from the sensory data; calculating kinematic parameters of each stride from the plurality of strides from the sensory data; classifying each stride from the plurality of strides in accordance with the kinematic parameters to obtain a stride type; and outputting the stride type for the plurality of strides.