Comparative Study on Fall Detection using Machine Learning Approaches
Conference proceedings article
Authors/Editors
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Publication Details
Author list: Thienrawit Tongskulroongruang, Paphatchaya Wiphunawat, Wisanu Jutharee, Watchara Kaewmahanin, Teerameth Rassameecharoenchai, Tanagorn Jennawasin and Boonserm Kaewkamnerdpong
Publication year: 2022
Start page: 1
End page: 4
Number of pages: 4
URL: https://ieeexplore.ieee.org/document/9795445
Abstract
Falls are one of the most dangerous problems for the elderly. A reliable fall detection system
can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset
that includes signals from portable devices. We propose a simple feature selection approach to reduce the number of effective input attributes based on a voting strategy. The dataset is classified into three categories, i.e., pre-fall, fall and post-fall using various machine learning approaches. The computational time is significantly reduced for most of the classification algorithms, and the relative reduction reaches to 66.67% with decision tree algorithm. Classification accuracy can reach as high as 95.17% when using neural network, while the relative reductions on the classification accuracy due to the feature selection do not exceed 1.50%.
Keywords
Fall Detection, Feature Selection, Portable Devices