Article

Non-motorized treadmills (NMT) provide belt speed data that can be used to
estimate work output, and potentially, gait temporal-spatial parameters that
provide an improved understanding of gait performance.
The purpose of this study
was to determine the validity of an automated technique that uses belt speed data
from an NMT to estimate temporal-spatial gait parameters. Seventeen injury-free
adult participants performed a series of 20-s, metronome-guided walking and
running trials for each of eight predetermined cadence conditions (72-200
steps/min). Two NMT-based cadence algorithms [PSD estimated cadence (PEC) and
threshold estimated cadence (TEC)], and one NMT-based step length algorithm
(NMT_SL) were evaluated for their ability to predict traditional motion
analysis-based measures of cadence and step length (MAC and MA_SL, respectively).
The results of this study demonstrate that both the PEC and TEC algorithms were
capable of predicting MAC with a standard error of the estimate (SEE) less than
four steps/min (R(2) = 0.997 and R(2) = 0.993, respectively). Predictions of
MA_SL from NMT_SL were separated by gait type (walking vs. running) to account
for an obvious separation in the step length data with a qualitative gait change.
When applied to walking data, NMT_SL was capable of predicting MA_SL with an SEE
of 23 mm (R(2) = 0.96). When applied to running data, NMT_SL was capable of
predicting MA_SL with an SEE of 44 mm (R(2) = 0.80). The assessment of the novel
technique suggests that it is feasible to use non-motorized treadmill belt speed
data to predict gait events and analyze simple gait metrics. Future research
should evaluate the applicability of these algorithms for use with
participants/patients presenting with pathological gait.
CI - Copyright (c) 2014 Elsevier B.V. All rights reserved.

Langue : ANGLAIS