The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the GP. In this article, we study two novel GP estimation methods: a State-based Method (SM) which employs the phase portrait of the hip angle (similar to previous methods), but uses a stretching transformation to reduce the nonlinearity of the estimated GP; and a Time-based Method (TM) that utilizes feature recognition on the hip angle signal to update the estimated cadence twice per gait cycle. The methods were tested across various speeds and slopes, encompassing steady and transient walking conditions. The results demonstrated the ability of both methods to estimate the GP in a range of conditions. The TM outperformed the SM, exhibiting a root-mean-squared error below 3% compared to 8.5% for the SM. However, the TM exhibited diminished performance during speed transitions, whereas the SM performed consistently in steady and transient conditions. The SM displayed a better performance in inclined walking and demonstrated higher linearity at faster speeds. Through the assessment of these methods in diverse conditions, this study lays the groundwork for further advancements in GP estimation methods and their application in assistive controllers.