Automated Running Gait Analysis via Machine Learning Video
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Solution Overview
Problem
Current gait analysis technologies are limited in their ability to provide quantitative, dynamic evaluation of running gait, often relying on expensive and complex hardware, and fail to offer actionable insights for improving athletic performance or preventing injuries, as they focus on walking gait analysis and lack automated detection of camera perspective, joint angle measurements, and suggestions for corrective exercises.
Innovation Solution
A system utilizing machine learning and digital video data to analyze running gait, identifying anatomical landmarks, generating gait metrics and characteristics, and providing personalized suggestions for improving form and preventing injuries, which can be accessed through mobile devices without the need for specialized equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gait analysis equipment is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses digital video copies of gait movement instead of direct physical measurement equipment. The video data serves as a copy that can be analyzed computationally to extract gait metrics, eliminating the need for complex hardware while maintaining measurement capability through image processing algorithms.
Solution Approach 2:
The patent replaces mechanical gait analysis equipment with a computational system that processes video data. Instead of using physical sensors and mechanical measurement devices, the system uses machine learning models and image processing to extract gait characteristics from video recordings, substituting mechanical systems with computational ones.
2Productivity
If automated gait analysis is implemented, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs automated self-service analysis by processing video data through machine learning models without requiring manual intervention. The automated pipeline extracts gait metrics, identifies anatomical landmarks, and generates recommendations autonomously, maintaining high throughput while preserving precision through sophisticated algorithms.
Solution Approach 2:
The patent implements feedback mechanisms where the system analyzes gait data, compares it against normative standards, and provides corrective recommendations. This feedback loop allows the system to continuously refine its measurements and improve precision while maintaining automated high-speed processing capability.
3Measurement precision
If comprehensive gait analysis is provided, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent creates a universal system that can analyze various gait metrics and provide multiple types of recommendations through a single integrated platform. The system handles diverse input video data and delivers comprehensive analysis including anatomical landmark detection, joint angle measurement, and personalized exercise recommendations, making complex analysis accessible to users.
Solution Approach 2:
The system acts as an intermediary between the user and complex gait analysis computations. It provides a user-friendly interface that accepts simple video input and automatically handles the complex processing, data extraction, and analysis, presenting results in an accessible format without requiring users to understand the underlying technical complexity.
Data Source
AI summary
Various embodiments are disclosed for automated gait evaluation for retraining of running form using machine learning and digital video data. At least one machine learning routine is executed using video data to generate positional data of anatomical landmarks of a human or bipedal non-human subject in a video. Gait metrics and/or characteristics are determined for multiple stages of a gait cycle based on the positional data. An optimal gait cycle for a given body part or gait metric of the subject is determined and may be adjusted based on at least one covariable over time. A suggested change to movement patterns of the subject at specific timepoints or stages of the gait cycle that minimize a difference between the gait cycle of the subject and an optimal gait cycle are shown on a display device.