Frailty is a common and critical condition in elderly adults, which may lead to further deterioration of health. However, difficulties and complexities exist in traditional frailty assessments based on activity-related questionnaires. These can be overcome by monitoring the effects of frailty on the gait. In this paper, it is shown that by encoding gait signals as images, deep learning-based models can be utilized for the classification of gait type. Two deep learning models (a) SS-CNN, based on single stride input images, and (b) MS-CNN, based on 3 consecutive strides were proposed. It was shown that MS-CNN performs best with an accuracy of 85.1\%, while SS-CNN achieved an accuracy of 77.3\%. This is because MS-CNN can observe more features corresponding to stride-to-stride variations which is one of the key symptoms of frailty. Gait signals were encoded as images using STFT, CWT, and GAF. While the MS-CNN model using GAF images achieved the best overall accuracy and precision, CWT has a slightly better recall. This study demonstrates how image encoded gait data can be used to exploit the full potential of deep learning CNN models for the assessment of frailty.

翻译：然而,根据与活动有关的调查问卷,传统的脆弱评估存在困难和复杂性,可以通过监测脆弱对行走的影响来克服这些困难和复杂性。在本文中,通过将行走信号编码成图像,可以使用深层次的学习模型来对行走类型进行分类;两个深层次学习模型(a) SS-CNN,以单步输入图像为基础,以及(b) MS-CNN,以连续3步为基础,提出了MS-CNN, 显示MS-CNN的精确度达到85.1 ⁇,而SS-CNN的精确度达到77.3 ⁇,这表明这些困难和复杂性可以通过监测脆弱状态对行走的影响来克服。这是因为MS-CNN能够观察到更多的与行走至行走变化相对应的特征,而步行走变化是易变的主要症状之一。Gait信号被用STFT、CWT和GAFF格式编码为图像。使用GAFAF图像的MS-CNN模型取得了最佳的总体精确度和精确度,而CWT有一个略微好的回顾,因为MS-CNN的深层模型是如何利用了对TRIS的模型进行深层数据进行学习。