Stair-specific algorithms for identification of touch-down and foot-off when descending or ascending a non-instrumented staircase

•Algorithms defining touch-down and foot-off (TD/FO) in stair ambulation are presented.•Performance of these algorithms was determined by comparison to force defined events.•TD/FO were determined with acceptable precision in young and old adults.•The algorithms performed equally well at differing riser heights (85-255 mm).•These algorithms can be used when force-instrumented staircases are unavailable.

The present study introduces four event detection algorithms for defining touch-down and foot-off during stair descent and stair ascent using segmental kinematics. For stair descent, vertical velocity minima of the whole body center-of-mass was used to define touch-down, and foot-off was defined as the instant of trail limb peak knee flexion. For stair ascent, vertical velocity local minima of the lead-limb toe was used to define touch-down, and foot-off was defined as the local maxima in vertical displacement between the toe and pelvis. The performance of these algorithms was determined as the agreement in timings of kinematically derived events to those defined kinetically (ground reaction forces). Data were recorded while 17 young and 15 older adults completed stair descent and ascent trials over a four-step instrumented staircase. Trials were repeated for three stair riser height conditions (85 mm, 170 mm, and 255 mm). Kinematically derived touch-down and foot-off events showed good agreement (small 95% limits of agreement) with kinetically derived events for both young and older adults, across all riser heights, and for both ascent and descent. In addition, agreement metrics were better than those returned using existing kinematically derived event detection algorithms developed for overground gait. These results indicate that touch-down and foot-off during stair ascent and descent of non-instrumented staircases can be determined with acceptable precision using segmental kinematic data.