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Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...

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A Comparative Study of Plantar Pressure and Inertial Sensors for Cross-Country Ski Classification Using Deep

Aurora Polo-Rodríguez¹, Pablo Escobedo², Fernando Martínez-Martí³

¹Department of Computer Engineering, Automatics and Robotics, Research Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, 18014 Granada, Spain.

Sensors (Basel, Switzerland)

|March 17, 2025

Summary

This study compares low-cost sensors for classifying cross-country skiing techniques. Plantar pressure sensors achieve high accuracy, rivaling inertial sensors for technique analysis.

Keywords:

IMU cross-country ski gear classification deep learning pressure sensors skating instrumented insoles wearable sensors

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Area of Science:

Sports Science
Biomechanics
Sensor Technology

Background:

Cross-country skiing technique classification is crucial for performance analysis.
Existing methods may be costly or invasive.
Developing accessible and accurate sensor systems is needed.

Purpose of the Study:

To compare the effectiveness of low-cost, low-invasiveness sensors for classifying cross-country skiing techniques.
To evaluate different sensor combinations, including plantar pressure sensors and inertial measurement units (IMUs).
To assess the performance of a deep learning model for technique classification.

Main Methods:

A dataset was created using instrumented insoles measuring plantar pressure, foot angles, and acceleration.
A deep learning model (CNN and LSTM) was trained on various sensor configurations.
Comparative analysis was performed using symmetrical data from skiers.

Main Results:

Plantar pressure sensors demonstrated encouraging performance in technique classification.
Classification accuracy using plantar pressure sensors approached that of inertial sensing.
The proposed approach achieved a global average accuracy of 94% to 99% with minimal sensor setup.

Conclusions:

Low-cost plantar pressure sensors offer a viable and precise solution for cross-country skiing technique classification.
The developed deep learning approach shows significant potential for sports performance analysis.
This methodology can be adapted for other sports requiring technique assessment.