Musculoskeletal Model for Real-Time Stiffness Ellipse and Equilibrium Point Calculation
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Solution Overview
Problem
Current methods for estimating movement features like impedance, equilibrium point, and muscle synergies require high-precision robots, are limited in measurable tasks, and lack clarity in physical significance, especially when using neural networks or statistical methods.
Innovation Solution
A movement analysis apparatus and method that measures muscle-group activity and body movement to calculate stiffness ellipse, equilibrium point, and muscle synergies using a musculoskeletal model, allowing for real-time algebraic calculations and clear physical significance without the need for large-scale apparatuses or complex data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-precision robot and perturbation method are used to measure movement features, then measurement precision is improved, but device complexity and scale increase
Solution Approach 1:
The patent replaces the mechanical perturbation method requiring high-precision robots with an analytical calculation method based on musculoskeletal models. Instead of physically perturbing the system and measuring responses, the invention uses mathematical models to calculate movement features directly from muscle activity and body movement data, thereby eliminating the need for complex robotic apparatus.
Solution Approach 2:
The patent introduces a musculoskeletal model as an intermediary between measurement data and movement feature calculation. This model acts as a mediator that processes muscle-group activity and body movement measurements to derive impedance, equilibrium point, and muscle synergy information without requiring direct mechanical interaction or high-precision robotic systems.
2Device complexity
If neural-network model is used to estimate movement features, then calculation simplicity is improved, but physical significance becomes unclear
Solution Approach 1:
The patent changes the approach from using neural network parameters (which lack physical meaning) to using musculoskeletal model parameters that have clear physical significance. By expressing movement features in terms of muscle synergies, equilibrium points, and impedance derived from physiological models, the invention maintains both calculation simplicity and physical interpretability.
Solution Approach 2:
The patent segments the movement analysis into distinct physiological components: muscle-group activity, body movement, and their relationships through the musculoskeletal model. This segmentation allows each component to be analyzed and interpreted independently, preserving physical significance while maintaining calculation simplicity through modular algebraic equations.
3Device complexity
If statistical method is used to extract muscle synergies, then calculation simplicity is improved, but physical significance is limited to suggestion
Solution Approach 1:
The patent replaces statistical extraction methods with an analytical approach based on musculoskeletal mechanics. Instead of using statistical algorithms that provide only suggestive results, the invention uses physical models of muscle mechanics and body dynamics to derive muscle synergies with clear physical meaning and reliability.
Solution Approach 2:
The patent employs the system's own measured data (muscle-group activity and body movement) within the musculoskeletal model to self-determine movement features. The model uses the input measurements to calculate impedance, equilibrium point, and muscle synergies without requiring external statistical analysis, thereby ensuring physical significance and reliability.
4Measurement precision
If large amount of data is collected and analyzed to create model, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary action by pre-establishing the musculoskeletal model with anatomical parameters and muscle properties. This pre-configured model eliminates the need for time-consuming data collection and model creation during actual movement analysis, allowing for rapid calculation of movement features using algebraic equations based on the pre-built model.
Solution Approach 2:
The patent substitutes time-consuming statistical data analysis with direct analytical calculation using the musculoskeletal model. Instead of collecting and analyzing large datasets to create models, the invention uses pre-established physiological models to directly calculate movement features from minimal input data, significantly reducing analysis time while maintaining precision.
Data Source
AI summary
In order to estimate a movement command that a central nervous system is to select to implement a desired movement based on three feature amounts under the concepts of an antagonistic muscle ratio and an antagonistic muscle sum and based on a musculoskeletal model, a movement analysis apparatus includes: a myoelectric potential measurement unit to measure a myoelectric potential of a person who performs a movement; a movement measurement unit to measure a body movement; and a stiffness-ellipse calculation unit, an equilibrium-point calculation unit and a muscle synergy calculation unit to calculate, from measurement information obtained at the measurement units, feature amounts of a stiffness ellipse, an equilibrium point, and a muscle synergy that are base vectors describing the equilibrium point at an operating point based on a musculoskeletal model.