Monitoring and analyzing floor vibrations to determine human activity has major applications in fields such as health care and security. For example, structural vibrations could be used to determine if an elderly person living independently falls, or if a room is occupied or empty. Monitoring human activity using floor vibration promises to have advantages over other methods. For example, it does not have the privacy concerns of other methods such as vision-based techniques, or the compliance challenges of wearable sensors. The analysis of the signals becomes a classification problem determining the type of human activity. Unfortunately only a few research groups are performing research of this subject even though there is a significant number of techniques that could be applied to this field. To date, no systematic study about the challenges and advantages of using different types of algorithms for this problem has been performed. This paper proposes a benchmark problem to: (i) encourage researchers to design new algorithms for monitoring human activity using floor vibrations, (ii) provide a dataset to test new algorithms, and (iii) allow the comparison of proposed methods based on a set of standard metrics. The benchmark consists of seven different cases of increasing difficulty. Each case has a specific number of sensors, calibration signals, and type of floor excitation forces to be considered. The paper also proposes specific metrics that enable the direct comparison of different techniques. Research groups interested in monitoring human activity using floor vibrations are encouraged to use the experimental data and evaluation metrics published in this paper to develop their own methodologies. This will enable the community of researchers to easily compare and contrasts techniques and better understand what type of methods will be appropriate in different applications. (C) 2016 Elsevier Ltd. All rights reserved.