Analysis of human daily living activities, particularly walking activity, is essential for health-care applications such as fall prevention, physical rehabilitation exercises, and gait monitoring. Studying the evolution of the gait cycle using wearable sensors is beneficial for the detection of any abnormal walking pattern. This paper proposes a novel discrete/continuous unsupervised Hidden Markov Model method that is able to recognize six gait phases of a typical human walking cycle through the use of two wearable Inertial Measurement Units (IMUs) mounted at both feet of the subject. The results obtained with the proposed approach were compared to those of well-known supervised and unsupervised segmentation approaches. The obtained results show the efficiency of the proposed approach in accurately recognizing the different gait phases of a human gait cycle. The proposed model allows the consideration of the sequential aspect of the walking gait phases while operating in an unsupervised context that avoids the process of data labeling, which is often tedious and time-consuming, particularly within a massive-data context.