A cascade-classifier approach for fall detection

The current machine learning algorithms in fall detection, especially those that use a sliding window, have a high com-putational 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 compu-tational 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 cas-cade approach significantly reduces the computational cost both for learning the classifier and executing it once learnt. Furthermore, the Multilayer Perceptron achieves the high-est performance with precision of 93.5%, recall of 94.2%, and f-measure of 93.5%.