A wearable sensory system for human lower extremity motion analysis is proposed, and an intelligent computation method for this sensory system is presented. The standard method for human motion analysis is the optical motion analysis using high-speed cameras to record human 3D motion, but this method is only limited in the laboratory research, because it requires expensive devices, large space and time-exhausted calibration experiments. In this study, two low-cost human motion analysis systems are constructed, deferent from the conventional 3D motion analysis system based on high-speed camera. These wearable systems incorporate gyroscopes (ENC-05EB) to measure angular velocities of body segments, and two-axis accelerometers (ADXL202) are used to measure the accelerations for the purpose of leg (foot, shank and thigh) motion analysis in every human motion cycle. The first wearable sensor system is designed for only foot motion analysis and the second system can be used for a leg (foot, shank and thigh) motion analysis. Then based on the two sensor systems, a fuzzy inference system (FIS) is developed for the calculation of the gait phases fed by sensors outputs. A digital filter is also designed to eliminate noises from of the output of the fuzzy inference system, which enhances robustness of the system. Finally, experimental study is conducted to validate the wearable sensor systems using an optical motion analysis system (Hi-DCam)