Sanchez-Casanova Jorge, Liu-Jimenez Judith, Tirado-Martin Paloma, Sanchez-Reillo Raul
University Group for ID technologies (GUTI), University Carlos III of Madrid, Spain.
Heliyon. 2021 Feb 12;7(2):e06270. doi: 10.1016/j.heliyon.2021.e06270. eCollection 2021 Feb.
Currently, there exist different technologies applied in the world of medicine dedicated to the detection of health problems such as cancer, heart diseases, etc. However, these technologies are not applied to the detection of lower body pathologies. In this article, a Neural Network (NN)-based system capable of classifying pathologies of the lower train by the way of walking in a non-controlled scenario, with the ability to add new users without retraining the system is presented. All the signals are filtered and processed in order to extract the Gait Cycles (GCs), and those cycles are used as input for the NN. To optimize the network a random search optimization process has been performed. To test the system a database with 51 users and 3 visits per user has been collected. After some improvements, the algorithm can correctly classify the 92% of the cases with 60% of training data. This algorithm is a first approach of creating a system to make a first stage pathology detection without the requirement to move to a specific place.
目前，医学领域应用了不同的技术来检测癌症、心脏病等健康问题。然而，这些技术并未应用于下肢疾病的检测。在本文中，提出了一种基于神经网络（NN）的系统，该系统能够在非受控场景下通过行走方式对下肢疾病进行分类，并且能够在不重新训练系统的情况下添加新用户。所有信号都经过滤波和处理以提取步态周期（GCs），这些周期用作神经网络的输入。为了优化网络，执行了随机搜索优化过程。为了测试该系统，收集了一个包含51名用户且每位用户有3次访问记录的数据库。经过一些改进后，该算法能够使用60%的训练数据正确分类92%的病例。该算法是创建一个无需前往特定地点即可进行第一阶段疾病检测系统的初步方法。
