Human activity recognition is one of the promising research areas in the domain of computer vision. Color sensor cameras are frequently used in the literature for human activity recognition systems. These cameras map 4D real-world activities to 3D digital space by discarding important depth information. Due to the elimination of depth information, the achieved results exhibit degraded performance. Therefore, this research work presents a robust approach to recognize a human activity by using both the aligned RGB and the depth channels to form a combined RGBD. Furthermore, in order to handle the occlusion and background challenges in the RGB domain, Spatial-Temporal Interest Point (STIP) based scheme is employed to deal with both RGB and depth channels. Moreover, the proposed scheme only extracts the interest points from depth video (D-STIP) such that the identical interest points are used to extract the cuboid descriptors from RGB (RGB-DESC) and depth (D-DESC) channels. Finally, a concatenated feature vector, comprising features from both channels is passed to exploit a bag of visual words scheme for human activity recognition. The proposed combined RGBD features based approach has been tested on the challenging MSR activity dataset to show the improved capability of combined approach over a single channel approach.