Real-time fall detection and activity recognition using low-cost wearable sensors

Abstract

We present a real-time fall detection and activity recognition system (FDAR) that can be easily deployed using Wii Remotes worn on human body. Features extracted from continuous accelerometer data streams are used for training pattern recognition models, then the models are used for detecting falls and recognizing 14 fine grained activities including unknown activities in realtime. An experiment on 12 subjects was conducted to rigorously evaluate the system performance. With the recognition rates as high as 91% precision and recall for 10-fold cross validation and as high as 82% precision and recall for leave one subject out evaluations, the results demonstrated that the development of real-time fall detection and activity recognition systems using low-cost sensors is feasible. © 2013 Springer-Verlag Berlin Heidelberg.