Classifying step and spin turns using wireless gyroscopes and implications for fall risk assessments.

Area of Science:

• Biomechanics
• Gerontology
• Wearable Technology

Background:

• Older adults exhibit a higher prevalence of spin turns, a less stable turning strategy compared to step turns.
• Current methods for identifying turning strategies rely on direct visual observation, limiting remote and longitudinal monitoring.
• Spin turns are associated with an increased risk of falls in older populations.

Purpose of the Study:

• To develop and validate novel methods for remotely monitoring turning behavior using inertial measurement units (IMUs).
• To assess the accuracy of IMU-based methods in distinguishing between spin turns and step turns.
• To enable non-laboratory, longitudinal assessment of turning strategies for fall risk evaluation.

Main Methods:

• Five young adults performed 90° turns at varying speeds around obstacles while wearing three IMUs (trunk, left shank, right shank).
• Two unique algorithms utilizing gyroscope data were developed to classify turning strategies.
• The performance of individual and combined IMU methods was compared against visual classification of turning behavior.

Main Results:

• The combined IMU-based method achieved high sensitivity (86.8%) and specificity (92.2%) in detecting spin turns.
• Accuracy was particularly high at slow walking speeds, with 89.4% sensitivity and 100% specificity.
• Individual methods showed moderate sensitivity and specificity (76.1%/76.7% and 76.1%/84.4%).

Conclusions:

• A combined IMU-based approach offers a reliable, remote method for monitoring turning strategies, specifically identifying increased use of spin turns.
• This technology can be integrated into fall prevention systems for continuous monitoring outside clinical settings.
• The method facilitates research into the association between turning strategies and fall risk in real-world environments.