A Tool for Automated Fall Risk Classification

For elderly it is more likely to fall and a fall can lead to severe injuries. Furthermore there is an ongoing discussion, if manual methods without any sensing and computing hardware or automated methods with the support of sensors and computing hardware are better in classifying elderly in regard of their risk to fall. Beside of these discussions, new sensing hardware allows the improvement of existing approaches so that automated fall risk classification can i) use very cheap pressure-sensing hardware, ii) can easily be used by nursing staff, and iii) keep the assessment time short. We present in this paper a tool for automated fall risk classification, which applies the leaning test. We also show that our approach with low measuring frequency and the rather coarse resolution of surface under pressure is able to achieve repeatable and reliable classification results.