Millecamps Alexandre, Lowry Kristin A, Brach Jennifer S, Perera Subashan, Redfern Mark S, Sejdić Ervin
Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, 3700 O׳Hara Street, Pittsburgh, PA 15261, USA.
Deparment of Physical Therapy, Des Moines University, Des Moines, IA 50312, USA.
Comput Biol Med. 2015 Jul;62:164-74. doi: 10.1016/j.compbiomed.2015.03.027. Epub 2015 Apr 4.
Gait accelerometry is an important approach for gait assessment. Previous contributions have adopted various pre-processing approaches for gait accelerometry signals, but none have thoroughly investigated the effects of such pre-processing operations on the obtained results. Therefore, this paper investigated the influence of pre-processing operations on signal features extracted from gait accelerometry signals. These signals were collected from 35 participants aged over 65years: 14 of them were healthy controls (HC), 10 had Parkinson׳s disease (PD) and 11 had peripheral neuropathy (PN). The participants walked on a treadmill at preferred speed. Signal features in time, frequency and time-frequency domains were computed for both raw and pre-processed signals. The pre-processing stage consisted of applying tilt correction and denoising operations to acquired signals. We first examined the effects of these operations separately, followed by the investigation of their joint effects. Several important observations were made based on the obtained results. First, the denoising operation alone had almost no effects in comparison to the trends observed in the raw data. Second, the tilt correction affected the reported results to a certain degree, which could lead to a better discrimination between groups. Third, the combination of the two pre-processing operations yielded similar trends as the tilt correction alone. These results indicated that while gait accelerometry is a valuable approach for the gait assessment, one has to carefully adopt any pre-processing steps as they alter the observed findings.
步态加速度测量是步态评估的一种重要方法。以往的研究对步态加速度测量信号采用了各种预处理方法，但没有一项研究全面探讨过这些预处理操作对所得结果的影响。因此，本文研究了预处理操作对从步态加速度测量信号中提取的信号特征的影响。这些信号来自35名年龄超过65岁的参与者：其中14人是健康对照者（HC），10人患有帕金森病（PD），11人患有周围神经病变（PN）。参与者以偏好的速度在跑步机上行走。对原始信号和预处理信号都计算了时域、频域和时频域中的信号特征。预处理阶段包括对采集到的信号进行倾斜校正和去噪操作。我们首先分别研究了这些操作的效果，然后研究了它们的联合效果。基于所得结果得出了几个重要的观察结果。首先，与原始数据中观察到的趋势相比，单独的去噪操作几乎没有效果。其次，倾斜校正对报告结果有一定程度的影响，这可能导致更好地区分不同组。第三，两种预处理操作的组合产生了与单独进行倾斜校正相似的趋势。这些结果表明，虽然步态加速度测量是步态评估的一种有价值的方法，但必须谨慎采用任何预处理步骤，因为它们会改变观察结果。
