The determination of gait events such as heel strike and toe-off provide the basis for defining stance and swing phases of gait cycles. Two algorithms for determining event times for treadmill and overground walking based solely on kinematic data are presented. Kinematic data from treadmill walking trials lasting 20-45s were collected from three subject populations (healthy young, n=7; multiple sclerosis, n=7; stroke, n=4). Overground walking trials consisted of approximately eight successful passes over two force plates for a healthy subject population (n=5). Time of heel strike and toe-off were determined using the two new computational techniques and compared to events detected using vertical ground reaction force (GRF) as a gold standard. The two algorithms determined 94% of the treadmill events from healthy subjects within one frame (0.0167s) of the GRF events. In the impaired populations, 89% of treadmill events were within two frames (0.0334s) of the GRF events. For overground trials, 98% of events were within two frames. Automatic event detection from the two kinematic-based algorithms will aid researchers by accurately determining gait events during the analysis of treadmill and overground walking.