  Each person has a unique gait, i.e., walking style, that can be used as a
biometric for personal identification. Recent works have demonstrated effective
gait recognition using deep neural networks, however most of these works
predominantly focus on classification accuracy rather than model efficiency. In
order to perform gait recognition using wearable devices on the edge, it is
imperative to develop highly efficient low-power models that can be deployed on
to small form-factor devices such as microcontrollers. In this paper, we
propose a small CNN model with 4 layers that is very amenable for edge AI
deployment and realtime gait recognition. This model was trained on a public
gait dataset with 20 classes augmented with data collected by the authors,
aggregating to 24 classes in total. Our model achieves 96.7% accuracy and
consumes only 5KB RAM with an inferencing time of 70 ms and 125mW power, while
running continuous inference on Arduino Nano 33 BLE Sense. We successfully
demonstrated realtime identification of the authors with the model running on
Arduino, thus underscoring the efficacy and providing a proof of feasiblity for
deployment in practical systems in near future.
