Movement Intelligence Analytics Using Vision-Based Tracking
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Solution Overview
Problem
Existing systems struggle to accurately identify movements of multiple users performing dissimilar movements in different active regions without the use of complex indicator systems, which can hinder user movement and limit freedom of observation.
Innovation Solution
A system employing a suite of machine learning algorithms to identify active areas, key-points, and movements, using image analysis to determine when a user enters an active region and identifying movements by analyzing temporal changes in key-points within those regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex indicator systems are used to identify movements of multiple users, then measurement precision is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent extracts and removes the complex indicator system from the observation volume, replacing it with a camera-based visual identification system. The system processes video feeds to automatically detect and track users and their movements without requiring physical indicators on users or electronic indicators in the environment, thereby simplifying the overall system while maintaining measurement precision
Solution Approach 2:
The patent replaces the mechanical indicator system (physical markers on users, electronic indicators in space) with an optical-computational system using cameras and image processing algorithms. The mechanical detection method is substituted with visual capture and computational analysis, eliminating the need for physical or electronic indicators while achieving accurate movement identification
2Measurement precision
If indicator devices are attached to users, then measurement precision is improved, but ease of operation deteriorates due to hampered movements
Solution Approach 1:
The patent removes the indicator devices from the users entirely, extracting the identification function from the user's body. Instead of attaching markers or sensors to users, the system uses external cameras to capture and process visual information, allowing users to move freely without any physical attachments that could impede their natural movements
Solution Approach 2:
The patent substitutes the mechanical attachment of indicator devices to users with an optical field-based detection system. Cameras capture images of users in the observation volume, and computational algorithms identify users and track their movements through image processing, eliminating the need for physical contact or attachment to the user's body
3Measurement precision
If visual indicators indicating observation volume are used, then measurement precision is improved, but ease of operation deteriorates due to limited freedom of movement
Solution Approach 1:
The patent extracts and removes the visual indicators that define the observation volume boundaries. Instead of displaying electronic or physical markers to indicate the observation space, the system uses computational methods to process video feeds and automatically determine which users are within the observation volume based on their detected positions and tracking data
Solution Approach 2:
The patent replaces the visual indicator system with a computational field-based approach. The system uses image processing and spatial analysis algorithms to virtually define and monitor the observation volume, eliminating the need for physical or visual boundary markers that could psychologically or physically constrain user movement while maintaining precise tracking of users within the designated space
Data Source
AI summary
A system identifies a movement and generates prescriptive analytics of that movement. To identify a movement, a system accesses an image of an observation volume where users execute movements. The system identifies a location including an active region in the image. The active region includes a movement region and a surrounding region. The system identifies musculoskeletal points of a user in the location and determines when the user enters the active area. The system identifies a movement of a user in the active region based on the time evolution of key-points in the active region. The system determines descriptive analytics describing the movement. Based on the descriptive analytics, the system generates prescriptive analytics for the movement and provides the prescriptive analytics to the user. The prescriptive analytics may inform future and/or current movements of the user.