Accurate fall detection using 3-axis accelerometer sensor and MLF algorithm

Nowadays, with the growing population of elderly people, the number of elderly without caregivers at home has also increased. It is clear that an elderly living alone at home is at higher risk of severe damage, due to potential delays in notifying caregivers and providing care at healthcare facilities. This especially becomes critical in case of high-risk incidents such as stroke or heart attack. To address this issue, an increasing number of methods have been proposed that employ various fall detection algorithms for elderly people. In this paper, we propose a new algorithm to detect falls, using a multi-level fuzzy min-max neural network. The proposed algorithm is compared with three other machine-learning algorithms (MLP, KNN, SVM). The main focus of this paper is on the effect of dimensionality reduction with using the Principal Component Analysis (PCA) method inside the proposed algorithm. The evaluations show that the multi-level fuzzy min-max neural network provides a high level of accuracy with a small number of dimensions. This is in contrast to the other algorithms, where accuracy is further lowered after applying dimensionality reduction. The performance evaluation of this algorithm on a public dataset obtained using accelerometer sensor data with using three dimensions indicates an accuracy of 97.29% for the sensitivity metric and 98.70% for the specifity metric.