Kinect have been used as a revolutionary sensor for recent human activity recognition research, mainly due to its ready skeletal joint information that facilitates activity analysis. However, the sensor's unstable and imprecise measurement may impair the analysis results. To alleviate this impact due to the unavoidable noisy measurement, this paper presents a recognition approach based on the relative positional relationship among measured joints data. Relative positional relationship between two joints refers to the relation either one joint's position in the x, y, z coordinates is to the left or right, above or below, in front or behind the other. Capturing these relative positional relationship among all measured joints data as a {-1, 0, 1} sequence throughout the time span of an activity, or more succinctly represented by a summary of the change frequency of these relationship, proves to be an effective feature for comparison and classification. In doing so, measured joint data are first pre-processed by SVIT (skeleton-based viewpoint invariant transformation) to eliminate viewpoint variation. Then, as the first feature, change frequencies of relative positional relationship among joints are constructed. As a complimentary feature, a HOG3D-like feature that summarizes the joints spatial distribution throughout an activity is also constructed. Classification credibility of feature is then applied to fuse the two features with ELM as machine learning for activity classification. The proposed approach with its efficient implementation has been applied to MSRDailyActivity3D and demonstrated favorable performance than results from literature.