Abstract: There is provided a method and apparatus for detecting a fall event of the user. The method includes collecting data associated with activities of the user from sensors and distributing the collected data to data sub-windows using signal windowing and segmentation, the data sub-windows indicative of a pre-fall moment, a fall moment, and a post-fall moment. The method includes extracting features from the data sub-windows and determining whether the event is a fall event using the extracted features. The determination of whether the event is a fall event can be determined by applied support vector machine technique. The developed machine learning based methods may balance a trade-off between accuracy and complexity of the evaluation. The method further includes multiple rejection filters mitigate false alarms due to fall-like activities of daily living. The method includes a personalization process to update the machine learning based methods associated with each user.

Abstract: There is provided a method and apparatus for detecting a fall event of the user. In particular, the method includes collecting data associated with activities of the user from a plurality of sensors and distributing the collected data to data sub-windows using signal windowing and segmentation, the data sub-windows indicative of a pre-fall moment, a fall moment, and a post-fall moment. The method further includes extracting a plurality of features from one or more of the data sub-windows and determining whether the event is a fall event at least in part based on the extracted features. The determination of whether the event is a fall event can further be determined by applied support vector machine (SVM) technique. The developed machine learning based methods may be substantially optimum in terms of a trade-off between accuracy and complexity of the evaluation.