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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

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Published on: November 7, 2014

Estimating Gait Speed in the Real World With a Head-Worn Inertial Sensor.

Paolo Tasca, Francesca Salis, Samanta Rosati

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|March 3, 2025

Summary

This study validates a machine learning method using head-worn inertial sensors for accurate stride detection and gait speed estimation in real-world walking. The approach enables reliable, continuous gait analysis outside laboratory settings.

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Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Area of Science:

Biomechanics
Wearable Technology
Machine Learning in Healthcare

Background:

Head-worn inertial sensors offer unobtrusive gait analysis in real-world conditions.
Existing methods for stride-by-stride gait speed estimation using head-worn sensors are limited, especially for real-world data.

Purpose of the Study:

To validate a two-step machine learning method for estimating initial contacts and stride-by-stride gait speed.
To utilize a single inertial sensor placed on the temporal region for gait analysis.
To develop a method applicable to real-world, unsupervised walking conditions.

Main Methods:

A convolutional neural network was employed for stride detection.
Gaussian process regression was used to infer stride-by-stride gait speed from detected strides.
Over 100,000 strides from 15 healthy young adults were analyzed using a multi-sensor wearable system for labeling.

Main Results:

The stride detector achieved an F1-score > 92% and mean absolute error < 40 ms.
Stride-by-stride gait speed prediction showed a very strong correlation (Spearman coefficient > 0.86) with target speed.
Low mean absolute error (< 0.085 m/s) was observed for gait speed estimation.

Conclusions:

The developed machine learning method is valid for quantitative, stride-by-stride gait speed evaluation in real-world settings.
This approach provides a foundation for integrating inertial sensors with head-worn devices for clinical gait analysis.
The findings support the use of head-worn sensors for accessible and continuous gait monitoring.