Cyclist Joint Torque Estimation Using Load and Skeletal Data
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Solution Overview
Problem
Estimating joint torque of a cyclist while pedaling a bicycle is challenging due to the complexity and limitations of simple sensors mounted on bicycles, which are not capable of accurately measuring the joint torque using large-scale equipment like motion capture systems.
Innovation Solution
A joint torque computation system that employs a cyclist model with nodes representing hip, knee, and ankle joints, using an evaluation function based on joint power and torque parameters, such as root mean square (RMS), to estimate joint torque by analyzing load data and skeletal structure data, even with altered bicycle configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion capture systems are used to estimate joint torque, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential measurement components needed for joint torque estimation, eliminating the need for complex motion capture systems. It uses a minimal sensor configuration (accelerometers, gyroscopes, and load cells) that can be mounted on the bicycle and cyclist, extracting only the necessary data for torque calculation while discarding unnecessary system complexity.
Solution Approach 2:
The patent creates a simplified computational model that copies the essential biomechanical relationships of the human body during cycling. Instead of using complex physical measurement systems, it employs a musculoskeletal model with segments and joints that replicates the mechanical behavior, allowing joint torque estimation through mathematical computation rather than direct physical measurement.
2Device complexity
If simple sensors are used on bicycles, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces direct mechanical measurement systems with a computational approach. Instead of using complex force sensors or motion capture equipment to directly measure joint torque, it substitutes these with simple sensors (accelerometers, gyroscopes, load cells) combined with biomechanical modeling and mathematical computation to indirectly calculate joint torque with high precision.
Solution Approach 2:
The patent introduces computational models and mathematical algorithms as intermediaries between simple sensor measurements and joint torque estimation. The musculoskeletal model, segment dynamics equations, and optimization algorithms act as mediators that transform basic sensor data into accurate joint torque values, bridging the gap between simple measurements and complex physiological parameters.
3Measurement precision
If inverse dynamic analysis is performed with detailed motion data, then joint torque accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the musculoskeletal model structure, segment parameters, and optimization algorithms before actual data processing. The model geometry, mass distribution, and moment of inertia are predetermined based on standard anthropometric data, allowing rapid computation during actual cycling analysis without requiring time-consuming model setup or calibration during the measurement process.
Data Source
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AI summary
Joint torque is estimated for a joint of a cyclist using a simple configuration. A joint torque computation system (10) includes a joint torque computation device (12), a detection section (14), an input section (16), and an output section (18). The joint torque computation device (12) includes a data acquisition section (122), a torque estimation section (124), and a change estimation section (126). The joint torque computation device (12) employs load data representing load applied to a pedal, skeletal data, and structural data to compute a change of joint torque when a position of a saddle has been displaced, and to output the saddle position to the output section (18). The change estimation section (126) uses plural estimated joint torque changes to decide a saddle position enabling the cyclist to develop their maximum power.