Cycle Recognition in Repeated Activities via Salient Feature Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions for recognizing cycle durations in repeated human activities are either labor-intensive or non-scalable, requiring high standardization and consistency in motion performance, making them ineffective for inconsistent scenarios.
Innovation Solution
A method and system that utilize a motion sensing system and processing system to identify salient segments in motion data, allowing for the recognition and measurement of cycle durations even with inconsistent performance, by detecting similar segments in new data using labeled metadata from a limited set of manually labeled cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing solutions use manual measurement or specialized devices for cycle duration recognition, then measurement precision is improved, but device complexity and labor intensity increase significantly
Solution Approach 1:
The system creates a reference model from manually labeled motion data that captures the essential pattern of the repeated activity. This reference model is then used to automatically identify cycles in new motion data through similarity comparison, eliminating the need for continuous manual measurement or specialized monitoring devices while maintaining measurement precision
Solution Approach 2:
The system performs preliminary manual labeling of a small subset of motion data to create the reference model before deploying automatic cycle recognition. This preliminary action enables the system to subsequently process large volumes of motion data automatically without requiring specialized devices or ongoing manual intervention
2Measurement precision
If existing solutions require high standardization and consistency in motion performance, then measurement precision is improved, but adaptability to real-world scenarios deteriorates
Solution Approach 1:
The system extracts only the essential salient features from the manually labeled motion data that define the core pattern of the repeated activity. By focusing on these key features rather than requiring complete consistency across all motion parameters, the system achieves both measurement precision and adaptability to variations in real-world performance
Solution Approach 2:
The system transforms the motion data into a feature space where similarity comparison can be performed. By changing the parameters from raw motion data to extracted features, the system can accurately detect cycles even when there are variations in speed, orientation, or other motion parameters, thereby improving adaptability while maintaining detection accuracy
3Measurement precision
If existing solutions use specialized devices for monitoring, then measurement precision is improved, but productivity and scalability deteriorate
Solution Approach 1:
The system replaces manual measurement processes and specialized monitoring devices with an automated computational approach. Motion data is processed algorithmically to identify cycles based on similarity to the reference model, enabling high-speed processing that maintains measurement precision while dramatically improving productivity and scalability
Data Source
AI summary
A system and method for monitoring performance of a repeated activity is described. The system comprises a motion sensing system and a processing system. The motion sensing system includes sensors configured to measure or track motions corresponding to a repeated activity. The processing system is configured to process motion data received from the motion sensing system to recognize and measure cycle durations in the repeated activity. In contrast to the conventional systems and methods, which may work for repeated activities having a high level of standardization, the system advantageously enables recognition and monitoring of cycle durations for a repeated activity, even when significant abnormal motions are present in each cycle. Thus, the system can be utilized in a significantly broader set of applications, compared conventional systems and methods.