Fullenkamp Adam M, Matthew Laurent C, Campbell Brian M
Exercise Science, Bowling Green State University, Bowling Green, OH, United States.
Exercise Science, Bowling Green State University, Bowling Green, OH, United States.
Gait Posture. 2015 Jan;41(1):141-5. doi: 10.1016/j.gaitpost.2014.09.017. Epub 2014 Sep 28.
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.
非电动跑步机（NMT）可提供皮带速度数据，该数据可用于估算工作输出，还可能用于估算步态时空参数，从而更好地了解步态表现。本研究的目的是确定一种利用NMT的皮带速度数据估算时空步态参数的自动化技术的有效性。17名无损伤的成年参与者针对8种预定节奏条件（72 - 200步/分钟）中的每一种进行了一系列20秒、节拍器引导的步行和跑步试验。评估了两种基于NMT的节奏算法[功率谱密度估算节奏（PEC）和阈值估算节奏（TEC）]以及一种基于NMT的步长算法（NMT_SL）预测基于传统运动分析的节奏和步长测量值（分别为MAC和MA_SL）的能力。本研究结果表明，PEC和TEC算法均能够预测MAC，估计标准误差（SEE）小于4步/分钟（R²分别为0.997和0.993）。根据步态类型（步行与跑步）对NMT_SL对MA_SL的预测进行区分，以考虑步长数据因步态质的变化而出现的明显差异。应用于步行数据时，NMT_SL能够以23毫米的SEE预测MA_SL（R² = 0.96）。应用于跑步数据时，NMT_SL能够以44毫米的SEE预测MA_SL（R² = 0.80）。对这项新技术的评估表明，利用非电动跑步机皮带速度数据预测步态事件并分析简单步态指标是可行的。未来的研究应评估这些算法对呈现病理性步态的参与者/患者的适用性。
