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In this paper, a novel weightlessness feature for activity recognition from a tri-axial acceleration signals have been proposed. Since the orientation between accelerometer and userpsilas body may continuously change when user perform activities, we propose an algorithm to calibrate the actual vertical direction of accelerometer signal through estimating the gravitational direction. We combine peaks of signal and weightlessness feature to produce six dimensional weightlessness-based features for activity recognition. Classification of the activities is performed with Support Vector Machine (SVM). The average accuracy of four activities using the proposed weightlessness-based features is 97.21%, which are better than using traditional widely used time-domains features (mean, standard deviation, energy and correlation of acceleration data). Experimental results show that the new features can be used to effectively recognize different human activities and they are robust for different location of accelerometer.