Preece et al. explained windowing techniques are used in most activity classification researches. In real-time applications, windows are defined concurrently with data collection. There are three different windowing techniques which are sliding windows, event-defined windows and activity-defined windows. The sliding window technique divided signals into fixed length (window) with no inter-window gap. The range of window size varies from 0.25s to 6.7s. Some studies proposed overlapping window between adjacent windows. The sliding window approach does not require pre-processing of the signal and is therefore suited for real-time applications. The papers explains there are two components in accelerometer signals: static acceleration and dynamic acceleration. Static acceleration is due to the effect of gravity and dynamic acceleration is the acceleration of body segment to which the sensor is attached. In the absence of motion, static postures differences  and postural transition identification  can be classified by using the angle between sensor orientation relative to the vertical axis.
 S. J. Preece, J. Y. Goulermas, L. P. J. Kenney, D. Howard, K. Meijer, and R. Crompton, “Activity identification using body-mounted sensors—a review of classification techniques,” Physiol. Meas., vol. 30, no. 4, p. R1, Apr. 2009.
 K. Aminian, B. Najafi, C. Büla, P.-F. Leyvraz, and P. Robert, “Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes,” Journal of Biomechanics, vol. 35, no. 5, pp. 689–699, May 2002.
 B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew, C. J. Bula, and P. Robert, “Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 6, pp. 711–723, Jun. 2003.