  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.
