Movement Phase Estimation via Reliability-Weighted Range Comparison
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Solution Overview
Problem
Existing methods for determining the phase of a periodic movement, such as those used in prostheses, fail to provide satisfactory precision in real-time with low latency, making it difficult to characterize postures like walking, running, or standing for applications like fall prevention or sporting gesture analysis.
Innovation Solution
The method involves multiple estimations of movement phases using different methods with predetermined reliability and precision, where each additional estimation is compared to the previous one, and only accepted if it falls within the range of the more reliable methods, ensuring a final result that balances precision and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple estimation methods with different reliability levels are used sequentially, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The estimation process is segmented into multiple sequential steps, each using a different estimation method with varying reliability characteristics. The first step uses a highly reliable but less precise method to establish a broad acceptable range, while subsequent steps use progressively less reliable but more precise methods to narrow down the estimate. This segmentation allows the system to leverage the strengths of each method while mitigating their individual weaknesses.
Solution Approach 2:
The system changes the reliability parameter across different estimation steps by selecting methods with different predetermined reliability levels. Each subsequent estimation method has lower reliability but higher precision compared to the previous method. The acceptable range from previous steps is used as a constraint parameter to guide subsequent estimations, ensuring that precision improvements do not compromise overall reliability.
2Productivity
If real-time processing is implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The first estimation step performs a preliminary action by establishing an acceptable range using a highly reliable method before proceeding to more precise but less reliable methods. This preliminary range acts as a constraint that guides subsequent estimations, ensuring that even if later methods produce less reliable results, the final estimate remains within a trustworthy boundary. This preliminary action enables real-time processing while maintaining precision guarantees.
Solution Approach 2:
The system implements feedback by using the acceptable range from each estimation step as input constraints for the next step. The predetermined reliability levels and corresponding acceptable ranges create a feedback mechanism where each estimation informs and constrains the next, ensuring that precision improvements in later steps do not compromise the reliability established in earlier steps. This feedback loop maintains measurement precision while enabling real-time processing.
Data Source
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AI summary
The method includes the acquisition of experimental data (D) from measurements of physical quantities via at least one sensor (C) associated with the object. The method includes a first reliable estimate (E1) of a first range of values (R1) using a first method. The method includes at least one additional estimate of a further range of values (R2, R3, R4) using a different method. Each method has a predetermined reliability. The ranges of values (R1, R2, R3, R4) are observed successively in decreasing order of reliability of the corresponding methods. Each additional range of values (R2, R3, R4) is compared with the range of values (R1, R2, R3) corresponding to the previous method in that order. The additional range (R2, R3, R4) is chosen as the result (Rint) when the additional range (R2, R3, R4) is within the range (R1, R2, R3) corresponding to one of the previous methods.