A computational algorithm for classifying step and spin turns using pelvic center of mass trajectory and foot...

Area of Science:

• Biomechanics
• Gait Analysis
• Computational Methods

Background:

• Transient changes in direction during ambulation are typically classified as step or spin turns.
• Current visual rating methods for turn classification are subjective and time-consuming.
• Objective and efficient methods are needed to quantify turning strategies, especially in populations with altered gait.

Purpose of the Study:

• To present and validate a novel computational, marker-based classification method for identifying turning strategies.
• To utilize pelvic center of mass (pCOM) trajectory and time-distance parameters for quantitative turn classification.
• To compare the performance of the pCOM-based method against visual evaluation and existing computational classifiers.

Main Methods:

• Developed a marker-based computational algorithm using pCOM trajectory and time-distance parameters.
• Evaluated the algorithm's sensitivity, specificity, and accuracy for 90-degree turns in uninjured controls and individuals with transtibial and transfemoral amputations.
• Compared the pCOM-based method with two existing computational classifiers based on trunk and shank angular velocities (AV).

Main Results:

• The pCOM-based algorithm achieved 94.5% overall accuracy, 96.6% sensitivity, and 93.5% specificity in classifying turning strategies.
• It demonstrated superior accuracy compared to AV-based methods across different cueing paradigms (freeform and forced) and populations (with and without amputation).
• The method showed significant accuracy improvements over existing computational classifiers (P<0.001).

Conclusions:

• The pCOM-based algorithm provides an efficient, objective, and accurate method for classifying 90-degree turning strategies using optical motion capture.
• This computational approach can be applied in laboratory settings and potentially extended to various cueing paradigms and populations with altered gait.
• The study highlights the potential of pCOM analysis for advancing quantitative gait research and clinical applications.