Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN

TL;DR

This paper demonstrates that encoding gait signals as images and applying deep CNN models can effectively classify frailty in elderly adults, offering a promising alternative to traditional assessment methods.

Contribution

The study introduces a novel approach of encoding gait signals as images and applying deep CNNs for frailty classification, improving accuracy over previous methods.

Findings

MS-CNN achieved 85.1% accuracy in frailty classification.

GAF images combined with MS-CNN yielded the best overall performance.

Encoding gait signals as images enhances deep learning-based frailty assessment.

Abstract

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.