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This paper investigates the expressive power of several time- and frequency-domain features extracted from 3-D accelerometer sensors. The raw data represent movements of humans and cars. The aim is to obtain a quantitative as well as a qualitative expression of the uncertainty associated with random placement of sensors in wireless sensor networks. Random placement causes calibration, location and orientation errors to occur. Different type of movements are considered-slow and fast movements; horizontal, vertical, and lateral movements; smooth and jerky movements, etc. Particular attention is given to the analysis of the existence of correlation between sets of raw data which should represent similar or correlated movements. The investigation demonstrates that while frequency-domain features are generally robust, there are also computationally less intensive time-domain features which have low to moderate uncertainty. Moreover, features extracted from slow movements are generally error prone, regardless of their specific domain.