  Motion ability is one of the most important human properties, including gait
as a basis of human transitional movement. Gait, as a biometric for recognizing
human identities, can be non-intrusively captured signals using wearable or
portable smart devices. In this study gait patterns is collected using a
wireless platform of two sensors located at chest and right ankle of the
subjects. Then the raw data has undergone some preprocessing methods and
segmented into 5 seconds windows. Some time and frequency domain features is
extracted and the performance evaluated by 5 different classifiers. Decision
Tree (with all features) and K-Nearest Neighbors (with 10 selected features)
classifiers reached 99.4% and 100% respectively.
