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Differentiative Feature-based Fall Detection System
Date Issued
January 1, 2021
Type
Conference Paper
Abstract
Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder but also the caregiver. Although there are numbers of applications and devices in the market for the user, the cutting-edge technology as a machine learning-based algorithm can increase effectiveness of fall detection model into device's effectiveness. The available technology is embedded accelerometer and gyroscope sensor into a smartphone provide benefit dataset. These data can be used for reducing and managing serious injury and caregiver can assist on time. The leverage performance of a Smart Steps application by including the essence of machine learning algorithm and 5-fold cross validation rises accuracy in fall detection. Thus, this paper proposed a novel method of 4 binary classification-Decision Tree, SVM, K-Nearest Neighbors, and Gradient Boosting. The focusing on acceleration magnitude, angular velocity magnitude, and difference between pre-current, current-post values are taken into account in the study. The opened dataset, MobiFall, are split into 2 groups 1) train group 80% and 2) test group 20% for gathering effectiveness result. The model's assessment measures in 4-dimension 1) accuracy, 2) precision, 3) recall and, 4) F1-Score. The results demonstrates increasing values that 95.65% of accuracy, 91.20% precision, 90.86% recall and, 91.03% F1-Score. The fall detection of the study can conclude that the machine learning-based algorithm offers more accuracy and effectively than threshold-based algorithm.
Citation
International Conference on Control Automation and Systems, 2021-October, 359-364, 2021