Automatic fall risk assessment with Siamese network for stroke survivors using inertial sensor‐based signals

• Published in: International journal of intelligent systems Vol. 37; no. 9; pp. 6168 - 6184
• Main Authors: Fan, Xiaomao, Wang, Hailiang, Zhao, Yang, Huang, Kuang‐Hui, Wu, Ya‐Ting, Sun, Tien‐Lung, Tsui, Kwok‐Leung
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limited 01.09.2022
• Subjects: Artificial neural networks; deep learning; Discrete Wavelet Transform; Embedded systems; fall risk assessment; Feature extraction; Gait; Inertial sensing devices; inertial sensor‐based signal; Intelligent systems; Populations; Rehabilitation; Risk assessment; Sensors; small sample size; Stroke; Survival; timed up and go test; Wavelet transforms
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.22838

Fall is a major threat to stroke survivors with the problems of gait and balance disorders in the rehabilitation phase following severe consequences on quality of life and a heavy burden to their families. Many solutions have been proposed to assess fall risk for elders based on inertial sensor‐based signals, however, there still exists a great challenge of transferring them from elderly populations to the stroke‐survivors populations as gait disorder patterns are significant difference between elders and stroke survivors. In this study, we conduct a pilot study to collect inertial sensor‐based signals from stroke survivors when they performed the timed up and go test, and build an automatic fall risk assessment model with the architecture of Siamese network, with a merit of mitigating the problem of small sample size. Specifically, the proposed automatic fall risk assessment model consists of two parallel convolutional neural networks, each of which is composed of three convolutional layers, two max‐pooling layers, and three fully connected layers. To utilize the space relation among accelerator‐based and gyroscope‐based signals, two‐dimensional discrete wavelet transform extracts image‐like features, wavelet coefficients, from inertial sensor‐based signals as the input. Experimental results show that the proposed fall risk assessment model has achieved a promising results, which outperform cutting‐edge methods with a big margin. The proposed fall risk assessment model with low computational complexity and limited memory consuming can be deployed on an embedded system to provide fall risk assessment service for stroke survivors in point‐of‐care environments or community settings.