Gait Event Detection Based on Fuzzy Logic Model by Using IMU Signals of Lower Limbs

Gait event detection is an essential approach to execute accurate gait recognition, and many studies use portable and reliable inertial measurement units (IMUs) for gait event detection. The popular methods mainly pay attention to the rules of specific signals or build the machine learning models when the event occurs, both of which overlook the consideration of the differences in characteristics coupled by multiple inputs. In this article, we propose a method based on fuzzy logic to quantify the event possibility and use it to detect gait events through the angular velocities and accelerations of lower limbs measured by IMUs. The proposed method identifies the event when heel and toe contact or leave the ground, making full use of the distribution characteristics of all extracted inputs without complex calculation. The mean absolute time differences between the detection and actual event in the recognition of heel strike (HS), toe strike (TS), heel off (HO), and toe off (TO) are 34, 23, 28, and 38 ms, respectively, in walking. We aim to propose an analysis method and provide some reference for gait recognition of assisted walking exoskeleton robots for healthy individuals, such as soldiers and workers.