Body fall detection with Kalman filter and SVM

Abstract

In this paper, an approach for human body fall detection is presented that can be supported with a modern smartphone equipped with accelerator sensors. Falling is one of the most significant causes of injury, mainly for elderly citizens, and is one of the reasons why many individuals are forced to leave the comfort and privacy of their homes and live in an assisted-care environment. The acceleration measured by the embedded tri-accelerometer sensor, was utilized to collect the information about the body motion and was used to develop a robust algorithm to accurately detect a fall. This data is incorporated by a realtime Pose Body Model (PBM) which is identified by an Extended Kalman Filter (EKF) algorithm. Moreover, a Support Vector Machine (SVM) performs a binary classification of the observed data, allowing the detection of fall incidents. This fall detection system is tailored for mobile phones and has an important application in the field of safety and security, but can also be used in motion analysis of body moving and live style monitoring. Experimental results showed that this methodology can detect most types of single human falls quite accurately. © 2015 Springer International Publishing.