Fall detection algorithm based on accelerometer and gyroscope sensor data using Recurrent Neural Networks
I Wayan Wiprayoga Wisesa, Genggam Mahardika · IOP Conference Series Earth and Environmental Science · 2019
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.