Robot Division, Korea Institute of Industrial Technology, Ansan 15588, Korea.
School of Electrical Engineering, Kookmin University, Seoul 02707, Korea.
Sensors (Basel). 2021 Jun 2;21(11):3842. doi: 10.3390/s21113842.
In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.
在本文中，我们提出了一种新的基于时间自适应加权累积特征的可穿戴活动识别和行人识别方法，该方法基于从类别足底压力中提取的特征。该方法依赖于三个与压力相关的特征，这些特征是通过在三个不同的时间加权形式下累积站立脚在每一步中的压力来计算的。此外，我们还考虑了一个反映压力变化的特征。这四个特征通过随时间对步压数据进行不同的加权来描述一步中的站立姿势。我们使用这些特征来分析行走过程中的站立脚，然后基于多层多类支持向量机分类器识别可穿戴活动和行人。实验结果表明，该方法在分析连续八步时，在这两个任务上的准确率达到 97%。为了更快的处理速度，该方法在考虑连续两步时，对可穿戴活动识别和行人识别的准确率分别达到 89.9%和 91.3%，而在考虑一步时，准确率分别下降到 83.3%和 82.3%。比较结果表明，该方法在准确性和时间敏感性方面具有很高的性能。
