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Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis.

Adrian Derungs¹, Oliver Amft²

¹Chair of Digital Health, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, 91052, Germany. adrian.derungs@fau.de.

Scientific Reports

|July 12, 2020

Summary

This study introduces a new method using biomechanical simulation to design and evaluate wearable motion systems. Personalized sensor placement significantly improves gait marker estimation accuracy for athletes and stroke patients.

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Area of Science:

Biomechanics
Wearable Technology
Biomedical Engineering

Background:

Wearable motion systems are crucial for gait analysis.
Designing effective systems requires understanding sensor placement and biomechanics.
Current methods often lack personalization and robust simulation.

Purpose of the Study:

To present a novel methodology for designing and assessing wearable motion systems.
To enable personalized gait marker estimation through biomechanical simulation and sensor data synthesis.
To analyze the impact of sensor position and algorithms on estimation accuracy.

Main Methods:

Development of personal biomechanical models.
Virtual attachment of sensor models to various body parts.
Synthesis of motion sensor data via simulation.
Evaluation using gait marker estimation algorithms in case studies (running athletes, hemiparetic patients).

Main Results:

Running speed impacts gait marker estimation performance.
Stride duration estimation error can reach 54% in athletes across 834 simulated positions.
Gait marker performance differs between affected and unaffected sides in stroke patients.
Optimal sensor positions vary during movement therapy.

Conclusions:

Personalized sensor positions and robust algorithms enhance gait marker estimation.
The methodology aids designers and developers in analyzing options and creating personalized wearable systems.
This approach is particularly beneficial for patients with movement disorders.