Poor selection of footwear, underestimation of foot health, sedentary life, and lack of accessible foot screening can have significant long-term adverse effects on the health of lower limbs. Unobtrusive, pervasive methods for automated foot screening have the potential to allow for timely detection of foot abnormalities. In the present study, we describe a proof-of-concept where data collected through sensor-enabled insoles and processed through one-dimensional convolutional neural networks were used to distinguish normal, cavus, and planus feet. We explored several combinations of sensor modalities to find the one that reflects foot types optimally. The highest accuracy of classification of 99.26% was achieved when angular velocity and force sensing were combined. Based on results, we suggest that sensor insoles, combined with optimal classification techniques, could be used for foot screening.

鞋类选择不当，足部健康低估，久坐不动以及缺乏脚部检查可能会对下肢的健康产生长期的重大不利影响。用于自动足部检查的不引人注目的，无处不在的方法有可能允许及时发现足部异常。在本研究中，我们描述了一种概念验证，其中通过传感器启用的鞋垫收集的数据和通过一维卷积神经网络处理的数据用于区分正常，足弓和扁平足。我们探索了传感器模式的几种组合，以找到最能反映脚型的传感器。当角速度和力感测相结合时，分类的最高准确度为99.26％。根据结果​​，我们建议传感器鞋垫，