Abstract
In our daily life activity, sometimes there is a chance of getting fall unintentionally. Unintentional falls are dangerous to health and may cause a serious problem, especially for elderly people whose have a higher probability of getting fall. In this paper, we develop an algorithm to distinguish falls from other activity daily living (ADL) based on accelerometer and gyroscope sensor data embedded on a wearable device. Several fall detection algorithms exist, with the majority are using rule-based algorithm. We take advantage of recurrent neural networks (RNN) as a tool for analyzing sequence time series data from sensors. The experiment was conducted using publicly available dataset UMA FALL ADL from Universidad de Málaga. The dataset consists of several recorded sensor-tag data, consisting of accelerometer, gyroscope and magnetometer sensor, representing the daily activity of several subjects including falls. Based on our experiment, we found that our algorithm yields a good result distinguishing fall from ADL.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Earth and Environmental Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
