We present an autonomous assistive robotic system for human activity recognition from video sequences. Due to the large variability inherent to video capture from a nonfixed robot (as opposed to a fixed camera), as well as the robot's limited computing resources, implementation has been guided by robustness to this variability and by memory and computing speed efficiency. To accommodate motion speed variability across users, we encode motion using dense interest point trajectories. Our recognition model harnesses the dense interest point bag-of-words representation through an intersection kernel-based SVM that better accommodates the large intra-class variability stemming from a robot operating in different locations and conditions. To contextually assess the engine as implemented in the robot, we compare it with the most recent approaches to human action recognition in the public datasets (non-robot-based), including a novel approach of our own that is based on a two-layer SVM-hidden conditional random field sequential recognition model. The latter's performance is among the best within the recent state of the art. We show that our robot-based recognition engine, while less accurate than the sequential model, nonetheless shows good performances, especially given the adverse test conditions of the robot, relative to those of a fixed camera