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Gait parameter and event estimation using smartphones.

Lucia Pepa¹, Federica Verdini¹, Luca Spalazzi¹

¹Department of Information Engineering, Politecnica delle Marche University Ancona, AN, Italy.

|July 2, 2017

Summary

Smartphones can accurately estimate gait parameters like step count and length during daily activities. This study validates smartphone accuracy against a gold standard, supporting real-world gait monitoring applications.

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Area of Science:

Biomechanics
Wearable Technology
Digital Health

Background:

Smartphone inertial sensors offer potential for gait parameter estimation during daily living.
Accuracy evaluation against a gold standard is crucial for reliable gait analysis.
Previous studies require deeper assessment of smartphone performance in various gait metrics.

Purpose of the Study:

To conduct a step-by-step assessment of smartphone performance in estimating key gait parameters.
To evaluate the influence of smartphone placement and orientation on estimation accuracy.
To compare smartphone-based gait analysis with stereophotogrammetry in healthy subjects.

Main Methods:

Development of a smartphone application to acquire and process inertial sensor data.

Keywords:

Heel strike Inverted pendulum model Step count Step length Step period

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Evaluation of smartphone alignment using acceleration vector reference frames and two placement strategies.

Implementation of three heel strike detection methods, followed by step count, step period, and step length estimation using the inverted pendulum model.

Statistical comparison with stereophotogrammetry using Pearson correlation, error analysis, ANOVA, and Bland-Altman limits of agreement.

Main Results:

High correlations observed between smartphone estimations and stereophotogrammetry for heel strike (0.99), step period (0.96), step count (0.93), and step length (0.92).
Smartphone placement did not significantly impact estimation performance.
Acceleration reference frames and heel strike detection methods showed the most significant influence on step count accuracy.

Conclusions:

This study provides detailed insights into the expected accuracy of smartphones for gait monitoring.
The validated accuracy supports the use of smartphones as a viable tool for real-life gait analysis.
Results encourage the integration of smartphone-based gait monitoring in various applications.