  The accuracy of tinyML applications is often affected by various
environmental factors, such as noises, location/calibration of sensors, and
time-related changes. This article introduces a neural network based on-device
learning (ODL) approach to address this issue by retraining in deployed
environments. Our approach relies on semi-supervised sequential training of
multiple neural networks tailored for low-end edge devices. This article
introduces its algorithm and implementation on wireless sensor nodes consisting
of a Raspberry Pi Pico and low-power wireless module. Experiments using
vibration patterns of rotating machines demonstrate that retraining by ODL
improves anomaly detection accuracy compared with a prediction-only deep neural
network in a noisy environment. The results also show that the ODL approach can
save communication cost and energy consumption for battery-powered Internet of
Things devices.
