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Related Experiment Video

Updated: May 4, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings.

Michele Zanoletti^1,2, Pasquale Bufano^1,3, Francesco Bossi²

¹National Research Council, Institute of Clinical Physiology, 56124 Pisa, Italy.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

Wearable devices like smartwatches and smart shoes accurately estimate daily life mobility and gait speed. Combining smartwatches and smart shoes offers the best performance for clinical insights, outperforming single devices or all devices together.

Keywords:

daily life monitoring gait speed estimation machine learning mobility analysis smart sensors smart shoes smartphone smartwatch telemedicine wearable devices

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Home-Based Monitor for Gait and Activity Analysis

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Last Updated: May 4, 2026

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

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

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Area of Science:

Biomedical Engineering
Wearable Technology
Clinical Assessment

Background:

Assessing daily life mobility is crucial for understanding clinical conditions like Chronic Obstructive Pulmonary Disease (COPD).
Wearable devices offer a promising avenue for objective mobility assessment outside clinical settings.

Purpose of the Study:

To comprehensively analyze the performance of various wearable devices in estimating gait speed.
To identify optimal device combinations for accurate and practical daily life mobility assessment.
To explore the synergistic effects of combining different wearable sensors.

Main Methods:

Collected gait speed data from 20 healthy subjects using smartphones, smartwatches, and smart shoes during a modified Six-Minute Walking Test.
Evaluated individual device performance and combined device accuracy.
Conducted feature importance analysis to identify key variables for gait speed estimation.

Main Results:

Individual wearable devices showed comparable performance in gait speed estimation.
The combination of smartwatches and smart shoes demonstrated superior accuracy.
Smartwatches provided additional health-related information and improved accuracy when paired with other devices.
Using all devices concurrently did not enhance, and may have reduced, estimation accuracy.

Conclusions:

Smartwatches and smart shoes represent a highly effective combination for gait speed estimation in daily life.
The findings suggest potential redundancy when using multiple wearable devices simultaneously.
Feature importance analysis provides valuable insights for developing refined gait speed estimation models.