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Updated: Jun 21, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions.

Eva Dorschky¹, Marlies Nitschke¹, Matthias Mayer¹

¹Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Frontiers in Bioengineering and Biotechnology

|March 6, 2025

Summary

This summary is machine-generated.

Sparse inertial sensor setups can accurately estimate movement variables for clinical and sports analysis. Even with fewer sensors, accuracy rivals full lower-body setups, enhancing usability outside the lab.

Keywords:

gait analysis gait simulations inertial measurement units optimal control trajectory optimization

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Last Updated: Jun 21, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

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Home-Based Monitor for Gait and Activity Analysis

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Published on: August 8, 2019

Area of Science:

Biomechanics
Human Movement Analysis
Wearable Sensor Technology

Background:

Accurate estimation of spatiotemporal, kinematic, and kinetic movement variables is crucial for clinical and sports applications.
Sparse inertial sensor setups offer a less obtrusive approach compared to full-body motion capture systems.
Investigating the accuracy of sparse sensor setups is vital for their practical implementation.

Purpose of the Study:

To determine if movement variables can be estimated as accurately from sparse inertial sensor setups as from a full lower-body setup.
To evaluate the impact of sensor placement and number on estimation accuracy.
To assess the feasibility of using sparse sensor setups for comprehensive movement analysis outside laboratory settings.

Main Methods:

Utilized optimal control problems with sagittal plane lower-body musculoskeletal models.
Minimized a combined objective function tracking inertial sensor data and muscular effort.
Simulated data for 10 participants across walking and running speeds using seven different sensor configurations (2-7 sensors).

Main Results:

Differences between inertial sensor estimates and optical motion capture data were small across all tested sensor setups.
Setups lacking a pelvis sensor resulted in inaccurate spatiotemporal variables due to excessive trunk lean.
Root mean square deviations (RMSDs) for joint angles ranged from 4.8-18 degrees, with sparse setups showing comparable or slightly higher errors than full setups.

Conclusions:

Comprehensive sagittal-plane motion analysis can be achieved with sparse inertial sensor setups, matching the accuracy of full sensor configurations.
Sparse setups, particularly those including sensors on the feet and pelvis or thighs, enhance usability for out-of-laboratory movement analysis.
The findings support the use of reduced inertial sensor configurations for practical, real-world biomechanical assessments.