Instrumented gait through objective data is important in clinical rehabilitation as it provides objective mobility assessment. Typically, those data help pinpoint the root causes of mobility impairments, subsequently enabling the foundation for the development of effective rehabilitation protocols/programs. Inertial sensors-based wearables such as accelerometers collect high-resolution data beyond the lab over prolonged periods. However, that results in big data that is expensive to store and time-consuming to process. Equally, streaming inertial data to a base station (e.g., smartphone) has notable challenges such as high bandwidth requirements, and high-power consumption. Here, we present a novel wearable edge device that overcomes those challenges by utilizing edge computing. The developed edge device can collect and process raw data on the device, then transfers the extracted gait characteristics to the cloud via a mobile phone connection for real-time monitoring. In the processing stage, the developed edge device detects walking/gait bouts and extracts step and stride durations, without requiring data storage and offline processing. The accuracy and reliability of the device were investigated by comparison to reference technology in the lab. Interclass correlation coefficients (ICC) between the edge device and reference were ≥0.935, 0.971, and 0.973 for slow, preferred, and fast walking, respectively. Beyond the lab, mean absolute error values for the step and stride durations between the edge device and reference were 0.001s and 0.007s, respectively. Results suggest the edge device is suitable for instrumenting gait in real-time and has the potential to be used continuously beyond the lab.