Gait analysis is one of the major topics in rehabilitation and sport. Tracking and determining gait phases can be done using various sensors and methods. In this paper, a fuzzy logic method is proposed to analyze and detect the five phases of a gait cycle using ground reaction force (GRF) and its gradient. The proposed method enables better detection adaptability at different walking speeds and body weights compared with the traditional threshold algorithms. In this algorithm, the GRF, measured by an insole equipped with force sensing resistors (FSR) and GRF gradient, which represent the plantar pressure transmission during a cycle, is passed through a set of fuzzy rules to detect the five gaits. A genetic algorithm (GA) is also applied for optimizing the fuzzy logic membership functions to reach minimum detection delay. A cost function is defined based on the difference between the normal reference gait and the output of the fuzzy logic gait phases. Detected phases are IC (initial contact), LR (loading response), MS (mid-stance), PS (pre-swing), and SW (swing). It is shown that the proposed method reaches a highly reliable performance of phase detection, especially for the initial contact (IC) and toe-off (TO). The average detection delays for the IC and TO phases, using the fuzzy-based method for three walking speeds of 0.4, 0.85, and 1.3 m/s, were -14.3±16.9ms and 1.24±17.0ms, respectively, and the average duration of stance and swing phases are 61.42% and 38.58%, respectively.

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