Detection Of Collapse By Android Smartphone
MetadataShow full item record
The bachelor’s study is focused to design and build an Android application for the detection of collapse, which is enhanced by new techniques coming from a sphere of the artificial intelligence modified for smartphones. The application uses accelerometer outputs which are in suspicious moments analysed by the neural network. The artificial intelligence is based on simulated events of collapse and events which resemble a fall of a person. The study describes data collected from 20 people. To provide the best results of training, the most convenient and useful features were selected by multiple approaches. Total accuracy of the collapse detection reached 93 %, with 9 % and 13 % of false positive and false negative detections, respectively.
Keywordscollapse, Android smartphone, fall detection, machine learning, neural network, Python, Java, Tensorflow lite, Keras
Document typePeer reviewed
Document versionFinal PDF
SourceProceedings of the 25st Conference STUDENT EEICT 2019. s. 34-37. ISBN 978-80-214-5735-5
- Student EEICT 2019