Gait pattern classification with integrated shoes

In this paper, we aim to study and classify gait patterns among flat walking, descending stairs, and ascending stairs using inertial measurement unit (IMU) including triaxial accelerometers and gyroscopes. Six subjects were invited to gather gait data of flat walking, descending stairs, and ascending stairs wearing the shoe-integrated system with free speeds. The design of the classifier for identifying gait patterns based on continuous kinematic signals is composed of three steps. In the first step, we separate gait signals of the six sensors in the same period into gait segments which are further used as the units for pattern feature analysis. Secondly, based on discrete wavelet transform (DWT), the average sum of squares of wavelet coefficients of each segment for anteroposterior acceleration, vertical acceleration, and sagittal plane angular rate are demonstrated and selected as the common features for gait pattern classification. At the last step, the fuzzy logic based classifier is proposed according to the distribution of the common features of different gait patterns. Experimental results demonstrate the proposed methodology is efficient for classifying gait patterns during humans´ daily activity.