Comparative Study on Fall Detection using Machine Learning Approaches
Thienrawit Tongskulroongruang, Paphatchaya Wiphunawat, Wisanu Jutharee, Watchara Kaewmahanin, Teerameth Rassameecharoenchai, Tanagorn Jennawasin, Boonserm Kaewkamnerdpong · 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · 2022
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%.