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Related Experiment Video

Updated: Jun 27, 2025

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

Felix Kluge¹, Yonatan E Brand², M Encarna Micó-Amigo³

¹Novartis Biomedical Research, Novartis Pharma AG, Basel, Switzerland.

JMIR Formative Research

|May 1, 2024

Summary

This summary is machine-generated.

Wrist-worn sensors can detect gait sequences in real-world settings, aiding mobility analysis. However, lower back sensors offer superior accuracy for gait detection across diverse patient groups.

Keywords:

Mobilise-D accelerometer digital health digital mobility outcomes inertial measurement unit validation walking wearable sensor

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Last Updated: Jun 27, 2025

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Published on: August 8, 2019

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Published on: April 13, 2016

Area of Science:

Digital Health
Biomedical Engineering
Rehabilitation Technology

Background:

Wrist-worn inertial sensors are crucial for real-world mobility evaluation in digital health.
Gait detection algorithms are essential for analyzing long-term sensor data, but arm movements complicate wrist-based detection.
A comparative validation of wrist-worn gait detection algorithms across diverse patient populations and sensor positions is lacking.

Purpose of the Study:

To validate gait sequence (GS) detection algorithms for wrist-worn sensors using real-world data.
To compare the performance of wrist-worn sensor algorithms against those used with lower back-worn sensors.

Main Methods:

Eighty-three participants (including patients with Parkinson disease, multiple sclerosis, hip fracture recovery, COPD, heart failure, and healthy older adults) wore wrist, lower back, and foot inertial sensors.
A multisensor reference system (including pressure insoles and infrared distance sensors) was used for validation.
Ten wrist-based gait detection algorithms were validated and compared to lower back-based algorithms.

Main Results:

The best wrist-based algorithm achieved mean sensitivities between 0.55-0.81 and specificities between 0.95-0.98.
Estimated walking time error for the best wrist algorithm ranged from 8.9% to 32.7%.
Lower back sensors demonstrated superior performance, with mean sensitivities of 0.71-0.91, specificities of 0.96-0.99, and walking time errors of 6.3%-23.5%, particularly in patients with severe gait impairments.

Conclusions:

Wrist-worn sensors can detect gait sequences effectively in real-world scenarios, facilitating gait parameter extraction.
The study provides evidence for informed decisions regarding sensor placement in clinical gait studies.
Lower back sensor placement generally yields higher gait detection accuracy compared to wrist placement, especially in complex patient populations.