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Related Experiment Video

Updated: Jan 27, 2026

Automated Gait Analysis in Mice with Chronic Constriction Injury

Automated Gait Analysis in Mice with Chronic Constriction Injury

Published on: October 17, 2017

Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions.

Lauren C Benson¹, Christian A Clermont², Ricky Watari³

¹Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada. lauren.benson@ucalgary.ca.

Sensors (Basel, Switzerland)

|April 3, 2019

Summary

This summary is machine-generated.

New algorithms using accelerometers detect running gait events from the foot and back. These automated methods offer consistent accuracy across various conditions, enabling real-world running analysis.

Keywords:

accelerometer gait event detection initial contact running toe off wearable technology

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Area of Science:

Biomechanics
Wearable Technology
Sports Science

Background:

Accurate running gait event identification (initial contact and toe off) is vital for analysis.
Real-world running gait evaluation requires robust algorithms using wearable sensors.

Purpose of the Study:

Develop and validate automated accelerometer-based algorithms for detecting running gait events.
Assess algorithm accuracy across diverse running conditions.

Main Methods:

Algorithms developed using foot and low back accelerometers.
Validation against force plate data (gold standard).
Testing across different speeds, foot strikes, and surfaces.

Main Results:

Consistent back-foot difference in gait event detection across conditions.
Algorithms accommodate variability in running patterns.
Demonstrated accuracy in varied real-world running scenarios.

Conclusions:

Automated accelerometer-based algorithms provide reliable running gait event detection.
These methods are suitable for diverse real-world running environments.
Facilitates real-time running pattern evaluation with wearable technology.