Online Stream Sampling for Low-Memory On-Device Edge Training for WiFi Sensing

Deploying machine learning models on-board edge devices allows for low latency model inference and data privacy by keeping sensor data local to the computation rather than at a central server. However, typical TinyML systems train a single global model which is duplicated across all edge devices. This leads to a model that is generalized to the training data, but not specialized to the unique physical environment where the device is deployed. In this work, we evaluate how we can train machine learning models on-board low-memory edge devices with streams of incoming data. When using these low-memory devices, storage space is at a minimum and as such, representative data samples from the data stream must be captured to ensure that the models can improve even with a limited set of available training samples. We propose the Variable Low/High Loss sampling method for selecting representative data samples from a data stream and demonstrate that our methods are able to increase the accuracy of the machine learning model compared to state-of-the-art methods. We demonstrate the applicability of our proposed method for WiFi sensing based human activity detection, where WiFi signals are used to predict human activities in a given environment without requiring sensors on their bodies.