In large shopping malls and airports, pedestrians often change floors using conveniently located lifts and escalators. Floor changing activity recognition (FCAR) therefore can be a vital aid to multi-floor pedestrian navigation systems. The focus of this paper is to achieve accurate FCAR with the minimal number of features. Using experimental data, we compare the performance of various feature selection methods and classifiers trained to detect whether the user is using an escalator or a lift. The results show that an accelerometer embedded in a smartphone can achieve 94% recognition accuracy using only 5 features.
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