Cycle Duration Measurement in Repeated Activity Sequences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in accurately measuring cycle duration in repeated physical human activities, especially those with abnormalities, as they are often labor-intensive and less effective for activities with varying motion differences in orientation and speed.
Innovation Solution
A system utilizing a processor and memory to detect cycles within a frame buffer using global optimization, creating cycle segmentations, computing segmentation errors, and generating cycle duration data, which improves accuracy and reduces response delay by optimizing all activities stored in the buffer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used for cycle duration in repeated physical activities, then the measurement process is simple to implement, but the measurement accuracy decreases and labor intensity increases
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated computational system that uses motion capture data and global optimization algorithms to detect cycles and calculate durations, thereby improving measurement accuracy while reducing labor intensity
Solution Approach 2:
The system automatically processes motion capture data through recursive iteration and segmentation error computation to identify cycles without human intervention, making the measurement process self-executing and improving both accuracy and efficiency
2Measurement precision
If existing measurement systems are used for activities with varying motion differences in orientation and speed, then the system is easier to operate, but the measurement accuracy decreases
Solution Approach 1:
The patent dynamically adjusts measurement parameters through global optimization that considers varying motion differences in orientation and speed, allowing the system to adapt to different activity patterns while maintaining high measurement accuracy
Solution Approach 2:
The system uses dynamic recursive iteration through frame buffers and adaptive segmentation error computation that responds to varying motion patterns, enabling accurate cycle detection across different orientations and speeds
3Measurement precision
If global optimization with recursive iteration is used to detect cycles, then the measurement accuracy improves, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing motion capture data into frame buffers and establishing segmentation criteria before actual cycle detection, which streamlines the optimization process and reduces computational time while maintaining accuracy
Solution Approach 2:
The system segments the frame buffer into candidate cycles through recursive iteration, computing segmentation errors for each segment. This segmentation approach allows efficient parallel processing and reduces overall computational time while achieving high measurement accuracy
4Productivity
If frames are removed after cycle detection, then the productivity improves by automating the process, but the system complexity increases
Solution Approach 1:
The patent implements continuous automated processing by removing detected cycle frames from the buffer and immediately processing remaining frames, maintaining continuous useful action that improves productivity through full automation of the measurement workflow
Data Source
AI summary
Using a global optimization, a cycle within a frame buffer including frames corresponding to one or more cycles of query activity sequences is detected. The detection includes creating a plurality of cycle segmentations by recursively iterating through the frame buffer to identify candidate cycles corresponding to cycles of a reference activity sequence until the frame buffer lacks sufficient frames to create additional cycles, computing segmentation errors for each of the plurality of cycle segmentations, and identifying the detected cycle as the one of the plurality of cycle segmentations having a lowest segmentation error. Cycle duration data for the detected cycle is generated. Frames belonging to the detected cycle are removed from the frame buffer. The cycle duration data is output.