Automated Movement Segmentation for Exergaming Content Authoring
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Solution Overview
Problem
Existing methods for creating human movement examples for exergaming platforms are time-consuming and require significant technical expertise, involving complex programming and video production processes to produce scored movements that work well for all users.
Innovation Solution
A computer-implemented method and system that pre-processes and post-processes human movement data using a 3D sensor to create a Standard Movement Library, allowing for efficient authoring of animated human movement examples with scored segments, where start and end frames are automatically defined, and routines are segmented and concatenated to produce score-able virtual exercise classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional video production or 3D avatar animation methods are used to create human movement examples, then the quality and realism of movement representation is improved, but the time and effort required to produce content increases significantly
Solution Approach 1:
The system performs preliminary action by automatically segmenting movement data into standardized components and pre-defining start/end frames during data collection. This preprocessing creates a ready-to-use movement library that eliminates the need for time-consuming post-production work, allowing rapid assembly of exercise routines without manual animation or video editing
Solution Approach 2:
The system creates digital copies of real human movements captured by 3D sensors, converting physical movement into standardized data representations. These copied movements are stored in a library and can be reused multiple times without requiring additional capture sessions, significantly reducing content production time while maintaining movement accuracy
2Measurement precision
If manual programming of pose recognition rules and video production techniques are used, then the accuracy of movement scoring is improved, but the technical expertise and complexity of the process increases
Solution Approach 1:
The system implements self-service by automatically segmenting movement data into standardized components and generating scoring-ready routines without requiring manual programming. The automated segmentation algorithm independently identifies movement boundaries and creates standardized representations, eliminating the need for developers to write custom pose recognition code for each movement
Solution Approach 2:
The system applies segmentation by automatically dividing continuous movement data into discrete, standardized movement segments with clearly defined start and end frames. This automated segmentation creates consistent, scoreable movement units that work across different users and contexts, maintaining scoring accuracy without requiring manual rule programming for each segment
3Adaptability or versatility
If custom exercise classes are created with personalized routines, then the adaptability and user satisfaction is improved, but the effort required to author new content increases
Solution Approach 1:
The system segments exercise routines into standardized, reusable movement components that can be independently selected and combined. This segmentation allows instructors to easily assemble customized exercise classes by choosing from pre-segmented movements, reducing the effort required to create personalized content while maintaining high adaptability to different user needs
Solution Approach 2:
The system merges pre-segmented movement segments into complete exercise routines and allows further combination of routines to form full exercise classes. This hierarchical merging process enables efficient content creation where standardized components are combined to create customized programs, reducing authoring effort while maintaining versatility
Data Source
AI summary
A computer-implemented method and system for authoring animated human movement examples with scored movement segments. The computer-implemented method includes pre-processing with pre-segmented movement wherein start and end frames of a movement segment have been defined automatically to create a Standard Movement Library. The computer-implemented method also includes post-processing with pre-segmented routine wherein start and end frames of each individual movement has been defined to produce a virtual exercise class. Further, the computer-implemented method includes segmenting a routine into individual movements to create a timeline of movements that produces a score for the players, wherein the routine is recorded by the instructor. Furthermore, the computer-implemented method includes concatenating a plurality of routines in any order to create a full exercise class and creating score-able virtual exercise classes in the pre-processing and post-processing phases. Moreover, the computer-implemented method includes verifying the virtual exercise with an instructor; and finalizing the virtual exercise.