Frequency Domain Cluster Analysis of Human Activity Using Triaxial Accelerometer Data

This paper presents cluster analysis of tri-axial accelerometer data acquired from various human physical activities as well as simulated falls using frequency domain features. Clustering was performed using K Means, Gaussian Mixed Model and Fuzzy C-Means methods. In our analysis we focused on two problems: the first clustering problem being activity recognition and differentiation from simulated human falls, while the other problem focused on distinction between single jerk events (e.g. jumping, falling) and continuous activity signals (e.g. running, walking).