Data for: 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 subjects while walking normally and
impairing their balance with ankle braces, eye-blocking masks, and
pneumatic jets on their legs. In some walking trials, we also applied
mechanical perturbations to their pelvis. We provide a comprehensive
biomechanics dataset as supplementary material. We 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%), while other metrics were less effective (≤68%).
Incorporating perturbation data did not improve the accuracy of these
metrics, though 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. In
clinical practice, taking baseline measurements might improve the
detection of impairment due to aging or disease progression.