Reaction Force Data Symmetry Analysis for Movement Performance

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Solution Overview

Problem

Existing methods for analyzing performance data from reaction forces during movements by living beings, such as humans and ungulates, are inefficient and require a large number of sensors for accurate evaluation, making them cumbersome and resource-intensive.

Innovation Solution

A computer-implemented method that structures reaction force data using a data structure with an imaginary axis of symmetry, allowing for the symmetrical arrangement of sensor data sets, enabling clear recognition of symmetry or asymmetry in the data, which correlates with the symmetry or asymmetry of the sensor device, facilitating efficient evaluation with a small number of sensors.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional reaction force sensor devices are used to analyze movement performance, then measurement precision can be achieved, but device complexity and the number of sensors required increase

Engineering Contradiction:
Improvemeasurement precisionVSAvoiddevice complexity
Core Design Contradiction:
Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies asymmetry by intentionally introducing asymmetric perturbations to symmetric sensor data. The system creates asymmetric test cases by applying forces in specific directions and measuring how the symmetric sensor array responds, thereby extracting more information from the same physical sensors. This allows the system to achieve higher measurement precision without adding more sensors, effectively resolving the contradiction between measurement precision and device complexity.

Inventive Principle:
Principle #4Asymmetry

Solution Approach 2:

The patent transforms the analysis from a single-dimension temporal analysis to a multi-dimensional analysis by incorporating spatial relationships between symmetric sensors. By analyzing data along multiple axes (temporal, spatial, and directional dimensions), the system extracts more information from the same sensor set, achieving improved measurement precision without increasing the number of sensors or device complexity.

Inventive Principle:
Principle #17Another dimension (Dimensionality change)

2Measurement precision

If more sensors are used to improve analysis accuracy, then measurement precision improves, but device complexity and resource requirements increase

Engineering Contradiction:
Improveanalysis accuracyVSAvoidnumber of sensors
Core Design Contradiction:
Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent merges data from symmetric sensor positions by analyzing their combined response to applied forces. Instead of treating each sensor independently, the system combines information from symmetric pairs of sensors to extract more robust measurement signals. This merging approach allows the system to achieve higher analysis accuracy while using fewer physical sensors, directly addressing the contradiction between measurement precision and quantity of sensors.

Inventive Principle:
Principle #5Merging (Combining)

Solution Approach 2:

The patent uses the symmetric arrangement of sensors as a form of copying, where each sensor has a symmetric counterpart that provides redundant but complementary information. By analyzing both the original and symmetric copies of sensor data, the system achieves improved analysis accuracy through comparative analysis without needing to deploy additional unique sensors, thus reducing the total quantity of sensors required.

Inventive Principle:
Principle #26Copying

3Ease of manufacture

If symmetric sensor arrangements are used, then ease of manufacture improves, but the ability to detect asymmetries in movement performance decreases

Engineering Contradiction:
Improveease of manufactureVSAvoiddetection of asymmetries
Core Design Contradiction:
Ease of manufactureVSDifficulty of detecting and measuring

Solution Approach 1:

The patent applies preliminary anti-action by pre-programming the system to look for asymmetric patterns in the data from symmetric sensors. The analysis algorithm is designed in advance to specifically detect and highlight asymmetric responses, counteracting the potential blindness that might arise from the symmetric physical arrangement. This allows the system to maintain ease of manufacture through symmetric sensor placement while effectively detecting asymmetries in movement performance.

Inventive Principle:
Principle #9Preliminary anti-action

Solution Approach 2:

The patent implements feedback mechanisms where the system continuously monitors the symmetry of responses from symmetric sensors and uses this information to detect asymmetries in movement. By feeding back the comparison results from symmetric sensor pairs, the system can identify performance asymmetries even though the physical sensor arrangement remains symmetric and easy to manufacture. This feedback loop resolves the contradiction by enabling asymmetry detection without compromising manufacturing simplicity.

Inventive Principle:
Principle #23Feedback

Data Source

PatentEP4473907A1Method for detecting a performance dataset for a movement
Publication Date: 2024.12.11 CONTITECH DEUTSCHLAND GMBH
  • EP4473907A1 patent drawingFigure 1a~1b
  • EP4473907A1 patent drawingFigure 2a~2b
  • EP4473907A1 patent drawingFigure 3

AI summary

The invention relates to a method (100) for acquiring a performance data set for a movement performed by a living being, comprising the following steps: - Receiving a reaction force data set (step 120), - Structuring the reaction force data set in a data structure (10) (step 140), wherein the measurement times of the reaction force are ordered along a first dimension (d1) and the different sensor data sets (14a, 14b; 16a, 16b) are ordered along a second dimension (d2) such that sensor data sets (14a, 14b; 16a, 16b) assigned to symmetrically arranged pairs of sensors (4a, 4b; 6a, 6b) are arranged symmetrically within the data structure (10), - Determining performance data based on the data structure (10) (step 160) and storing it in a performance data set (step 170).