A Novel Approach for Toe Off Estimation During Locomotion and Transitions on Ramps and Level Ground.

Abstract

Identification of the toe off event is critical in many gait applications. Accelerometer threshold-based algorithms lack adaptability and have not been tested for transitions between locomotion states. We describe a new approach for toe off identification using one accelerometer in over ground and ramp walking, including transitions. The method uses invariant foot acceleration features in the segment of gait, where toe off is probable. Wavelet analysis of foot acceleration is used to derive a unique feature in a particular frequency band, yielding estimated toe off occurrence. We tested the new method for five conditions: over ground walking (W), ramp ascending (RA), ramp descending (RD); transitions between states (W-RA, W-RD). Mean absolute estimation error was 17.4 ± 12.5, 13.8 ± 8.5, and 22.0 ± 16.4 ms for steady states W, RA, and RD, 20.1 ± 15.5, and 17.1 ± 13.7 ms for transitions W-RA and W-RD, respectively. Algorithm performance was equivalent across all pairs of transition and locomotion state except between RA and RD ( p = 0.03), demonstrating adaptability. The db1 wavelet outperformed db2 across states and transitions (p < 0.01). The presented algorithm is a simple, robust approach for toe off detection.