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首页> 外文期刊>Journal of the Royal Society Interface >Gait recognition: highly uniquedynamic plantar pressure patternsamong 104 individuals
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Gait recognition: highly uniquedynamic plantar pressure patternsamong 104 individuals

机译：步态识别：104个个体中高度独特的动态足底压力模式

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摘要

Everyone's walking style is unique, and it has been shown that both humans and computers are very good at recognizing known gait patterns. It is therefore unsurprising that dynamic foot pressure patterns, which indirectly reflect the accelerations of all body parts, are also unique, and that previous studies have achieved moderate-to-high classification rates (CRs) using foot pressure variables. However, these studies are limited by small sample sizes (n < 30), moderate CRs (CR ≈ 90%), or both. Here we show, using relatively simple image processing and feature extraction, that dynamic foot pressures can be used to identify n = 104 subjects with a CR of 99.6 per cent. Our key innovation was improved and automated spatial alignment which, by itself, improved CR to over 98 per cent, a finding that pointedly emphasizes inter-subject pressure pattern uniqueness. We also found that automated dimensionality reduction invariably improved CRs. As dynamic pressure data are immediately usable, with little or no pre-processing required, and as they may be collected discreetly during uninterrupted gait using in-floor systems, foot pressure-based identification appears to have wide potential for both the security and health industries.
机译：每个人的行走方式都是独特的，并且已经表明，人和计算机都非常擅长识别已知的步态模式。因此，毫不奇怪的是，间接反映身体各个部位加速度的动态脚压模式也很独特，并且以前的研究已经使用脚压变量实现了中到高分类率（CR）。但是，这些研究受到样本量小（n <30），中度CR（CR≈90％）或两者的限制。在这里，我们显示，使用相对简单的图像处理和特征提取，可以使用动态足底压力来识别n = 104个受试者，其CR为99.6％。我们的关键创新得到了改进，并实现了自动空间对齐，这本身就将CR提高了98％以上，这一发现明确强调了受试者间压力模式的独特性。我们还发现，自动减少维数总是可以改善CR。由于动态压力数据可立即使用，几乎不需要或无需进行预处理，并且由于可以使用地板下系统在不间断的步态中谨慎收集动态压力数据，因此基于脚压力的识别对于安全和医疗行业都具有广阔的潜力。 。

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