Lower-limb exoskeletons play an essential role in rehabilitation and mobility assistance, where accurate real-time gait phase recognition is critical for achieving safe, synchronized, and intuitive human–robot interaction. Many existing approaches rely on multiple sensors such as IMUs, EMG, and FSRs, which increase system complexity, computational load, cost, and susceptibility to mechanical wear. In this study, we propose a lightweight and robust gait phase detection framework that uses only hip and knee joint encoder data—sensors that are already integrated into most exoskeletons and are less prone to noise and misplacement. The method employs a finite state machine (FSM) to identify gait phases and detect key gait events, including heel strike, in real time. The approach was first evaluated in simulation using the SCONE (Opensim) platform and then experimentally implemented on the NEXA knee-joint exoskeleton with multiple healthy participants. Results show that the proposed method reliably predicts gait phases and heel-strike timing with minimal temporal error, while achieving significantly higher processing frequency compared to sensor-rich configurations. These findings demonstrate that accurate and efficient gait phase recognition can be achieved using only encoder data, offering a practical and low-cost solution for real-world exoskeleton control applications.

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