基于压力传感器和卷积神经网络的实时临床步态分析和足部异常检测

Mahdi Islam, Musarrat Tabassum, M. M. Nishat, Fahim Faisal, Muhammad Sayem Hasan
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引用次数: 12

摘要

本研究基于流行的卷积神经网络(CNN)模型的实现,提出了一种基于传感器采集数据的迁移学习算法的步态障碍检测的新见解。本文提出利用压力传感器提取步态过程中的热图图像,然后在各种分类算法中进行训练和测试,用于步态异常诊断和检测。步态是对身体运动和运动的生物学和科学研究,主要作为检查人体神经、肌肉和骨骼系统的可靠参数。为了为可能的应用构建方便和精确的分类系统,在多个预先存在的CNN模型中生成合成数据,然后使用常规性能指标对其进行评估。提出的概念产生的实验结果表明,所有被测试的迁移学习方案都具有更高的准确性,其中Vgg16模型达到了97.15%的显着准确性。结果表明,该分析不仅在准确性方面表现出显著的性能,而且还降低了复杂性和计算时间,使该方法高效而有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time Clinical Gait Analysis and Foot Anomalies Detection Using Pressure Sensors and Convolutional Neural Network
This research presents a novel insight on gait disorder detection using transfer learning algorithms on sensor-acquired data based on the implementation of popular Convolutional Neural Network (CNN) models. The paper proposes the use of pressure sensors to extract heatmap images during gait, which are then trained and tested in various classification algorithms for gait abnormality diagnosis and detection. Gait is a biological and scientific study of body movement and locomotion that emphatically serves as a reliable parameter for inspecting the human body’s neuromuscular and skeletal systems. To build a convenient and precise classification system for possible application, synthetic data was generated in multiple preexisting CNN models, which were then evaluated using conventional performance metrics. The proposed notion yielded experimental findings that showed higher accuracies for all transfer learning schemes tested, with the Vgg16 model achieving a notable accuracy of 97.15%. As a result, the analysis demonstrated not only a significant performance in terms of accuracy, but also reduced complexity and computing time, making the approach efficient yet effective.
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