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http://dx.doi.org/10.6109/jkiice.2021.25.11.1649

Study of regularization of long short-term memory(LSTM) for fall detection system of the elderly  

Jeong, Seung Su (Department of Electrical, Electronic, and Control Eng., Hankyong National University)
Kim, Namg Ho (Convergence Technology Campus of Korea Polytechnic)
Yu, Yun Seop (ICT&Robotics Eng. and IITC, Hankyong National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.11, 2021 , pp. 1649-1654 More about this Journal
Abstract
In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively.
Keywords
Tensorflow; Fall detection; The elderly; Long short-term memory(LSTM);
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