A classifier based approach to real-time fall detection using low-cost wearable sensors

In this paper, we present a novel fall detection method using wearable sensors that are inexpensive and easy to deploy. A new, simple, yet effective feature extraction scheme is proposed, in which features are extracted from slices or quanta of sliding windows on the sensor´s continuously acceleration data stream. Extracted features are used with a support vector machine model, which is trained to classify frames of data streams into containing falls or not. The proposed method is rigorously evaluated on a dataset containing 144 falls and other activities of daily living (which produces significant noise for fall detection). Results shows that falls could be detected with 91.9% precision and 94.4% recall. The experiments also demonstrate the superior performance of the proposed methods over three other fall detection methods.