Detecting artificially impaired balance in human locomotion: metrics, perturbation effects and detection thresholds.

Measuring balance is important for detecting impairments and developing interventions to prevent falls, but there is no consensus on which method is most effective. Many balance metrics derived from steady-state walking data have been proposed, such as step-width variability, step-time variability, foot placement predictability, maximum Lyapunov exponent and margin of stability. Recently, perturbation-based metrics such as center of mass displacement have also been explored. Perturbations typically involve unexpected disturbances applied to the subject. In this study we collected walking data from 10 healthy human subjects while walking normally and while impairing balance with ankle braces, eye-blocking masks and pneumatic jets on their legs. In some walking trials we also applied mechanical perturbations to the pelvis. We obtained a comprehensive biomechanics dataset and compared the ability of various metrics to detect impaired balance using steady-state walking and perturbation recovery data. We also compared metric performance using thresholds informed by data from multiple subjects versus subject-specific thresholds. We found that step-width variability, step-time variability and foot placement predictability, using steady-state data and subject-specific thresholds, detected impaired balance with the highest accuracy (≥86%), whereas other metrics were less effective (≤68%). Incorporating perturbation data did not improve accuracy of these metrics, although this comparison was limited by the small amount of perturbation data included and analyzed. Subject-specific baseline measurements improved the detection of changes in balance ability. Thus, in clinical practice, taking baseline measurements might improve the detection of impairment due to aging or disease progression.