Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN
- Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN
- 김진욱; 문경률; 아샤드 무함마드 지샨; 정다운; 박미나; 신형은
- Issue Date
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- 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) MSCNN, 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 strideto-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.
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