Weightlessness feature - a novel feature for single tri-axial accelerometer based activity recognition

• Published in: 2008 19th International Conference on Pattern Recognition pp. 1 - 4
• Main Authors: Zhenyu He, Zhibin Liu, Lianwen Jin, Li-Xin Zhen, Jian-Cheng Huang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2008
• Subjects: Acceleration; Accelerometers; Data mining; Feature extraction; Humans; Laboratories; Pattern recognition; Robustness; Support vector machine classification; Support vector machines
• ISBN: 9781424421749; 1424421748
• ISSN: 1051-4651; 2831-7475
• DOI: 10.1109/ICPR.2008.4761688

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.