Wearable devices that measure and recognise human activity in real time require classification algorithms that are both fast and accurate when implemented on limited hardware. A decision-tree-based method for differentiating between individual walking, running, stair climbing and stair descent strides using a single channel of a foot-mounted gyroscope suitable for implementation on embedded hardware is presented. Temporal features unique to each activity were extracted using an initial subject group (n = 13) and a decision-tree-based classification algorithm was developed using the timing information of these features. A second subject group (n = 10) completed the same activities to provide data for verification of the system. Results indicate that the classifier was able to correctly match each stride to its activity with >90% accuracy. Running and walking strides in particular matched with >99% accuracy. The outcomes demonstrate that a lightweight yet robust classification system is feasible for implementation on embedded hardware for real-time daily monitoring.