Patterson P, Draper S
Iowa State University, College of Engineering, Ames 50011, USA.
J Rehabil Res Dev. 1998 Jan;35(1):43-51.
A neural net approach was used to classify and analyze combinations of the physiological and kinematic responses (the factor patterns) of experienced and novice individuals during wheelchair propulsion, and to determine the key characteristics (individual factors) used in making this determination. A sequence of artificial neural networks (ANN) was developed and used to classify differences between eight nonimpaired controls and seven individuals using wheelchairs, who ranged in age from 24 to 36 years. The subjects propelled a wheelchair on a specially constructed dynamometer at three different velocity levels during which stroke pattern, force, energy, and efficiency data were collected. The data from 10 subjects (5 from each group) were used to train a net, with the data from the remaining 5 subjects used to test the resulting net. The nets correctly classified the training subjects in all 10 cases and correctly classified all 5 test subjects, indicating that the developed networks were able to generalize to new data sets. It was concluded that a minimal net consisting of only three variables, peak VO2 at the high velocity, hand force on the rim at the low velocity, and push angle at the high velocity, could accurately represent the differences between these groups.
采用神经网络方法对有经验者和新手在轮椅推进过程中的生理和运动学反应组合（因素模式）进行分类和分析，并确定做出该判断所使用的关键特征（个体因素）。开发了一系列人工神经网络（ANN），用于对8名非残疾对照者和7名年龄在24至36岁之间使用轮椅的个体之间的差异进行分类。受试者在特制的测力计上以三种不同速度水平推动轮椅，在此期间收集行程模式、力、能量和效率数据。来自10名受试者（每组5名）的数据用于训练网络，其余5名受试者的数据用于测试所得网络。这些网络在所有10个案例中都正确地对训练受试者进行了分类，并正确地对所有5名测试受试者进行了分类，表明所开发的网络能够推广到新的数据集。得出的结论是，一个仅由三个变量组成的最小网络，即高速时的峰值VO2、低速时轮辋上的手力以及高速时的推角，能够准确地代表这些组之间的差异。
