Detection of Gait Events Using Ear-Worn IMUs During Functional Movement Tasks.

Area of Science:

• Biomechanics
• Wearable technology
• Clinical assessment

Background:

• Complex walking tasks are crucial for evaluating vestibular, nervous, and musculoskeletal system function.
• Wearable inertial measurement units (IMUs) offer a scalable, ecologically valid alternative to traditional assessment methods.
• Ear-worn IMUs present a novel approach for unobtrusive gait analysis.

Purpose of the Study:

• To investigate the efficacy of ear-worn IMUs in identifying gait events during complex walking tasks.
• To compare the performance of an existing gait event detection algorithm with a newly developed, more robust algorithm.
• To enhance the accuracy of gait event detection, particularly during tasks involving head movements.

Main Methods:

• Collected gait data from 68 participants with diverse ages and movement conditions using ear-worn IMUs.
• Employed an existing gait event detection algorithm and a novel algorithm designed for robustness against lateral head movements.
• Analyzed the sensitivity and accuracy of both algorithms in detecting initial and terminal foot contacts during various walking tasks.

Main Results:

• Both algorithms demonstrated high initial contact sensitivity across all tested walking tasks.
• The new algorithm achieved superior terminal contact sensitivity during turning and walking with horizontal head turns.
• The enhanced algorithm provided more accurate estimations of stance and swing times, especially in challenging conditions.

Conclusions:

• The developed algorithm using ear-worn IMUs significantly improves gait event detection accuracy for complex walking.
• This advancement facilitates more detailed clinical assessments of gait and movement disorders.
• The findings support the use of ear-worn IMUs for improved gait analysis in both clinical and daily life settings.