Movement of the thighs is an important factor for studying gait cycle. In this paper, a hybrid hidden Markov model (HMM)/Kalman filter (KF) scheme is proposed to track the hip angle during gait cycles. Within such a framework, HMM and KF work in parallel to estimate the hip angle and detect major gait events. This approach has been applied to study gait features of different subjects and compared with video based approach. Experimental results indicate that 1.) the swing angle of the hip can be detected with simple hardware configuration using biaxial accelerometers and 2.) the hip angle can be tracked for different subjects within the error range of -5°+5°.