1D CNN and Machine Learning Methods for Fall Detection


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 3, pp. 85-90, Mar. 2021
https://doi.org/10.3745/KTSDE.2021.10.3.85,   PDF Download:
Keywords: Machine Learning, Deep Learning, Fall Detection, 1D Convolutional Neural Network
Abstract

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models’ validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.


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Cite this article
[IEEE Style]
I. Kim, D. Kim, S. Noh, J. Lee, "1D CNN and Machine Learning Methods for Fall Detection," KIPS Transactions on Software and Data Engineering, vol. 10, no. 3, pp. 85-90, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.3.85.

[ACM Style]
Inkyung Kim, Daehee Kim, Song Noh, and Jaekoo Lee. 2021. 1D CNN and Machine Learning Methods for Fall Detection. KIPS Transactions on Software and Data Engineering, 10, 3, (2021), 85-90. DOI: https://doi.org/10.3745/KTSDE.2021.10.3.85.