Fall detection based on inertial and barometric pressure sensors
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The following work presents the study of fall detection using inertial and barometric pressure sensors. Technological advances have made it possible to develop inertial sensors of very small size that allow, among other things, to monitor human movement, and in this case, more specifically, detect falls. These sensors are used in so-called inertial measuring units, which are small devices that are placed on a specific part of the body and are able to measure motion. The thesis focuses on reducing the number of false positives generated with this type of systems by adding an extra measurement component, height, to the inertial system to detect falls, using a barometric pressure sensor. A number of steps have been taken to achieve this goal. First of all, a barometric pressure sensor has been chosen from the current sensors on the market. This sensor has been chosen by performing a series of tests to determine the best sensor among 3 possible candidates. Once selected, the sensor configuration parameters have been optimized for our task. Secondly, a heterogeneous database of 14 volunteers of different heights, ages and sexes has been compiled using the inertial measurement unit and the barometric pressure sensor. Thirdly, signal processing techniques and methods of extracting features have been applied to the database to obtain 4 different sets of data that include signals obtained by combining inertial measurements with barometric and signals obtained only from inertial measurements, to then make a comparison between the two and see if better results are obtained by combining the different sensors. Finally, 5 supervised learning algorithms have been trained with the data obtained. The results obtained demonstrate the feasibility of adding a barometric pressure sensor to an inertial system.