Consumption analysis of smartphone based fall detection systems with multiple external wireless sensors Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy costeffective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphonebased FDS can be improved if it also exploits the measurements collected by small lowpower wireless sensors, which can be firmly attached to the userxe2x80x99s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphonebased FDS is viable.

Fall Detection with Supervised Machine Learning using Wearable Sensors Unintentional falls can cause severe injuries to a person, and even death, especially when no immediate assistance is provided. The aim of Fall Detection Systems (FDSs) is to detect the occurrence of a fall and to automatically and promptly request the necessary assistance. This work proposes a FDS based on wearable sensors  i.e., accelerometers and gyroscopes xe2x80x93 and Machine Learning (ML), for sensor signal processing and detection. The process extracts a number of features on portions of the signal and classifies them as falls or regular daily activities. The classifier is a Support Vector Machine (SVM) that is trained using a manually labelled dataset, where human activities are distinguished between falls and regular activities. The method is assessed on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained in the literature for the same dataset; the proposed method largely outperforms the original analysis technique proposed for the SisFall dataset, with an F1 score higher than 97% and a recall higher than 99.7%.