Mobile Pose Comparison Using Computer Vision for Real-Time Form Feedback
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Solution Overview
Problem
Existing fitness training and physical gaming systems lack the ability to provide real-time form monitoring and feedback, leading to potential injuries and suboptimal performance.
Innovation Solution
A method and system using machine learning-based computer vision algorithms on mobile computing devices to enable pose comparison and form training, allowing users to replicate movements from a reference video and receive instant feedback on their form.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-recorded workout videos are used for home fitness training, then cost and scheduling flexibility are improved, but real-time feedback on workout form is lost
Solution Approach 1:
The system captures video of the user's movements and provides real-time feedback by comparing the user's pose to the reference exercise video, delivering form correction information during the workout session
Solution Approach 2:
A computer vision system acts as an intermediary between the user and the reference video, automatically analyzing user posture and providing feedback without requiring a human coach present
2Measurement precision
If specialized equipment with embedded sensors and large projector screens is used for interactive fitness games, then real-time motion tracking capability is improved, but cost and portability are worsened
Solution Approach 1:
The system uses the mobile device's own camera and processing capabilities to perform pose estimation and provide feedback, eliminating the need for external specialized equipment
Solution Approach 2:
A general-purpose mobile computing device is made to perform the specialized function of motion tracking and form analysis through computer vision algorithms, replacing dedicated fitness equipment
3Measurement precision
If wearable sensors are used for monitoring physical activities, then measurement capability is improved, but cost and portability are worsened
Solution Approach 1:
Physical wearable sensors are replaced with optical computer vision-based measurement, using the mobile device camera to estimate user pose and provide feedback without contact with the user's body
Data Source
AI summary
Methods and systems are disclosed for pose comparison, interactive physical gaming, and remote fitness training on a user computing device. The methods and systems are configured to first receive a reference feature generated from a frame of a reference video, the reference feature computed from a reference posture of a reference person in the frame of the reference video. Next, receive a frame of a user video, the frame of the user video comprising a user. Next, extract a user posture from the frame of the user video, by performing a machine learning-based computer vision algorithm that detects one or more body key points of the user in an image plane of the user video. Finally, generate a user feature from the user posture; and determine an output score based on a distance between the reference feature and the user feature.