An accelerometry-based gait analysis approach via the platform of sensor network is reported in this paper. The hardware units of the sensor network are wearable accelerometers that are attached at the limbs of human body. For the specific task of gait analysis, flexion angles of the thighs during gait cycles are computed. A Kalman filter is designed to estimate the flexionextension angle, angular velocity of the thigh using the output of the wearable accelerometers. The proposed approach has been applied to four subjects and the performance is compared with videobased approach. Comparative results indicate that with the proposed Kalman filter, the sensor network is able to track the movement of the thighs during gait cycles with good accuracy and simultaneously detect major gait event of foot contact from the waveform of the angular velocity.