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Terrain Recognition and Gait Cycle Prediction Using IMU

It is well known that terrain recognition and gait cycle prediction are important for powered exoskeleton. However, only a few works have focused on the concerns of complexity of the control system caused by using redundant sensors. In this paper, only two IMU sensors are applied to collect information of the angle and angular velocity of the hip joint in the situation of level-ground walking, ramp ascent, and ramp descent. Based on information acquired from these two IMU sensors, two methods are proposed to achieve terrain recognition. One method uses the angle of the hip joint when the two legs intersect as the threshold of terrain recognition. It can identify the terrain (level-ground walking, ramp ascent, ramp descent) during stable walking, but it cannot recognize the transitional terrain (from level-ground walking to ramp ascent, from ramp ascent to ramp descent, and so on) and its robustness is limited. The other method selects the angle and angular velocity of the hip joints as the eigenvector, and uses SVM for terrain recognition. The accuracy of terrain recognition is improved from 69.7% to 100% after introducing the Gaussian kernel function instead of Linear kernel function. For gait cycle prediction, Wiener one step prediction is applied in predicting the GC. Compared to actual GC, the error from predicted GC based on mean prediction is more than 8.0%, while the error from Wiener on step prediction is less than 4.35%.