Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors
This addresses the problem of model obsolescence in dynamic IoT environments for developers, but it is incremental as it compares existing algorithms.
The paper compared four state-of-the-art continual learning algorithms for gesture recognition and image classification in TinyML systems, showing they maintain reliability with only a few percentage points drop in accuracy compared to original models on unconstrained platforms.
Tiny machine learning (TinyML) in IoT systems exploits MCUs as edge devices for data processing. However, traditional TinyML methods can only perform inference, limited to static environments or classes. Real case scenarios usually work in dynamic environments, thus drifting the context where the original neural model is no more suitable. For this reason, pre-trained models reduce accuracy and reliability during their lifetime because the data recorded slowly becomes obsolete or new patterns appear. Continual learning strategies maintain the model up to date, with runtime fine-tuning of the parameters. This paper compares four state-of-the-art algorithms in two real applications: i) gesture recognition based on accelerometer data and ii) image classification. Our results confirm these systems' reliability and the feasibility of deploying them in tiny-memory MCUs, with a drop in the accuracy of a few percentage points with respect to the original models for unconstrained computing platforms.