Borg Mark, Mizzi Stephen, Farrugia Robert, Mifsud Tiziana, Mizzi Anabelle, Bajada Josef, Falzon Owen
Centre for Biomedical Cybernetics, University of Malta, MSD 2080 Msida, Malta.
Department of Podiatry, Faculty of Health Sciences, University of Malta, MSD 2080 Msida, Malta.
Bioengineering (Basel). 2025 Feb 1;12(2):143. doi: 10.3390/bioengineering12020143.
Monitoring plantar foot temperatures is essential for assessing foot health, particularly in individuals with diabetes at increased risk of complications. Traditional thermographic imaging measures foot temperatures in unshod individuals lying down, which may not reflect thermal characteristics of feet in shod, active, real-world conditions. These controlled settings limit understanding of dynamic foot temperatures during daily activities. Recent advancements in wearable technology, such as insole-based sensors, overcome these limitations by enabling continuous temperature monitoring. This study leverages a data-driven clustering approach, independent of pre-selected foot regions or models like the angiosome concept, to explore normative thermal patterns in shod feet with insole-based sensors. Data were collected from 27 healthy participants using insoles embedded with 21 temperature sensors. The data were analysed using clustering algorithms, including k-means, fuzzy c-means, OPTICS, and hierarchical clustering. The clustering algorithms showed a high degree of similarity, with variations primarily influenced by clustering granularity. Six primary thermal patterns were identified, with the "butterfly pattern" (elevated medial arch temperatures) predominant, representing 51.5% of the dataset, aligning with findings in thermographic studies. Other patterns, like the "medial arch + metatarsal area" pattern, were also observed, highlighting diverse yet consistent thermal distributions. This study shows that while normative thermal patterns observed in thermographic imaging are reflected in insole data, the temperature distribution within the shoe may better represent foot behaviour during everyday activities, particularly when enclosed in a shoe. Unlike thermal imaging, the proposed in-shoe system offers the potential to capture dynamic thermal variations during ambulatory activities, enabling richer insights into foot health in real-world conditions.
监测足底温度对于评估足部健康至关重要,尤其是对于糖尿病患者这类并发症风险增加的人群。传统的热成像技术是在不穿鞋的平躺个体中测量足部温度,这可能无法反映在穿鞋、活动的现实环境中足部的热特征。这些受控环境限制了我们对日常活动中动态足部温度的理解。可穿戴技术的最新进展,如基于鞋垫的传感器,通过实现连续温度监测克服了这些限制。本研究利用一种数据驱动的聚类方法,独立于预先选定的足部区域或血管体概念等模型,以探索使用基于鞋垫的传感器时穿鞋足部的标准热模式。从27名健康参与者中收集数据,使用嵌入21个温度传感器的鞋垫。使用聚类算法对数据进行分析,包括k均值、模糊c均值、密度相连空间聚类算法(OPTICS)和层次聚类。聚类算法显示出高度的相似性,其差异主要受聚类粒度的影响。识别出六种主要的热模式,其中“蝴蝶模式”(内侧足弓温度升高)占主导,占数据集的51.5%,与热成像研究结果一致。还观察到其他模式,如“内侧足弓+跖骨区域”模式,突出了不同但一致的热分布。这项研究表明,虽然热成像中观察到的标准热模式在鞋垫数据中有所体现,但鞋内的温度分布可能更能代表日常活动中的足部行为,尤其是当足部被鞋子包裹时。与热成像不同,所提出的鞋内系统有潜力捕捉动态活动中的热变化,从而在现实条件下更深入地了解足部健康。