Efficient fall detection in four directions based on smart insoles and RDAE-LSTM model

计算机科学 加速度计 自编码 人工智能 特征(语言学) 特征提取 深度学习 模式识别(心理学) 机器学习 计算机视觉 语言学 哲学 操作系统
作者
Zhirong Lin,Zengwei Wang,Houde Dai,Xuke Xia
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:205: 117661-117661 被引量:12
标识
DOI:10.1016/j.eswa.2022.117661
摘要

Efficient fall detection is significant for the elderly, persons with motor symptoms, and people who perform risky actions, with four types of falling postures. However, most studies focused on distinguishing fall or not, although the fall direction information is crucial to turn on the corresponding part of the airbag or quickly assess the damage level. To accurately, rapidly, and reliably recognize different directional falls (forward, backward, left, and right) during daily life, this study proposes a novel fall detection methodology based on a pair of commercial lightweight smart insoles and a long short-term memory (LSTM) framework with a trained referencing denoising autoencoder (RDAE). Compared with traditional autoencoders, the referencing sub-path, i.e., RDAE, is additionally attached to achieve the automatic feature extraction. A pair of wireless in-shoe insoles (OpenGo, Moticon GmbH), each side equipped with 13 plantar pressure sensors and a tri-axial accelerometer, was employed to capture comprehensive spatial-temporal gait parameters. Hence an effective response to a fall, together with the estimation of the corresponding direction, can be accomplished, where the accuracy and response time are two primary concerns. The proposed RDAE-LSTM network provides a reliable testing result in classification, with 98.6% accuracy and 8.7 ms response time for determining fall directions, demonstrating a more convincing performance than other algorithms. The proposed methodology is an unobtrusive choice for users whose daily life is not affected by the fall detection device. The RDAE-LSTM model was proven to accurately and quickly recognize falls in four directions for the unbalanced fall detection dataset.
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