The Number of Steps for Representative Real-World, Unsupervised Walking Data Using a Shoe-Worn Inertial Sensor - Jesse M. Charlton; Calvin Kuo; Michael A. Hunt (IEEE Transactions on Neural Systems and Rehabilitation Engineering)
Inertial measurement units are now commonly used to quantify gait in healthy and clinical populations outside the laboratory environment, yet it is unclear how much data needs to be collected in these highly variable environments before a consistent gait pattern is identified. We investigated the number of steps to reach consistent outcomes calculated from real-world, unsupervised walking in people with (n=15) and without (n=15) knee osteoarthritis. A shoe-embedded inertial sensor measured seven foot-derived biomechanical variables on a step-by-step basis during purposeful, outdoor walking over seven days. Univariate Gaussian distributions were generated from incrementally larger training data blocks (increased in 5 step increments) and compared to all unique testing data blocks (5 steps/block). A consistent outcome was defined when the addition of another testing block did not change the percent similarity of the training block by more than 0.01% and this was maintained for the subsequent 100 training blocks (equivalent to 500 steps). No evidence was found for differences between those with and without knee osteoarthritis (p=0.490), but the measured gait outcomes differed in the number of steps to become consistent ( $\text{p}< 0.001$ ). The results demonstrate that collecting consistent foot-specific gait biomechanics is feasible in free-living conditions. This supports the potential for shorter or more targeted data collection periods that could reduce participant or equipment burden.