A Cascade-Classifier Approach for Fall Detection

Abstract

The current machine learning algorithms in fall detection, especially those that use a sliding window, have a high computational cost because they need to compute the features from almost all samples. This computation causes energy drain and means that the associated wearable devices re- quire frequent recharging, making them less usable. This study proposes a cascade approach that reduces the computational cost of the fall detection classifier. To examine this approach, accelerometer data from 48 subjects performing a combination of falls and ordinary behaviour is used to train 3 types of classifier (J48 Decision Tree, Logistic Regression, and Multilayer Perceptron). The results show that the cascade approach significantly reduces the computational cost both for learning the classifier and executing it once learnt. Furthermore, the Multilayer Perceptron achieves the highest performance with precision of 93.5%, recall of 94.2%, and f-measure of 93.5%.