This paper presents an intelligent fuzzy inference (IFI) algorithm using inertial measurement units (IMUs) and smart shoes to recognize human activities. IFI algorithm recognizes the activities based on ground contact forces (GCFs) and the knee joint angles. The smart shoes are designed to measure GCFs exerted by the wearer. A total of four IMUs are mounted on bilateral thighs and shanks to provide acceleration and angular rate data. To calculate knee flexion extension, a calibration procedure is adopted which eliminates the need for an external camera system. Then, an extended Kalman filter (EKF) is used to estimate the relative orientations of thigh and shank segments, from which knee angle is calculated. Random forest search (RFS) technique is used as a baseline to compare with the performance of the IFI algorithm. To evaluate the performance of this algorithm, several outdoor experiments are conducted on two healthy subjects for six activities including sitting, standing, walking, going upstairs, going downstairs and jogging. The results show that the algorithm is capable of classifying six activities with higher precision and less update time compared to the baseline approach for both subject dependent and independent tests. Also, the algorithm detects transitions between all the activities smoothly such as sit-to-stand or stand-to-walk with higher precision.