Department of Computer Science, IT University of Copenhagen, 2300 Copenhagen, Denmark.
Intelligence for Embedded Systems-Research Line, SDAS Research Group, Ibarra 100150, Ecuador.
Sensors (Basel). 2021 Jun 28;21(13):4422. doi: 10.3390/s21134422.
The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient's weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.
通过足底压力分析（podometry），可以分析和检测儿童患者的不同类型的障碍和治疗方法。早期发现患者体重分布不均，可预防膝盖和下脊柱的严重损伤。本文提出了一种使用电阻压力传感器来检测正常、扁平或弓形脚印的嵌入式系统。为此，研究了硬件和软件相关标准，以通过信号耦合和滤波过程来改善数据采集。随后，学习算法允许我们估计学龄前和学龄儿童志愿者的足迹生物力学类型。结果，所提出的算法实现了 97.2%的整体分类准确性。在 1000 名学龄前儿童样本中，扁平足的比例为 60%。同样，在 600 名学龄儿童样本中，扁平足的比例为 52%。
