  Human activity recognition~(HAR) has attracted significant research interest
due to its applications in health monitoring and patient rehabilitation. Recent
research on HAR focuses on using smartphones due to their widespread use.
However, this leads to inconvenient use, limited choice of sensors and
inefficient use of resources, since smartphones are not designed for HAR. This
paper presents the first HAR framework that can perform both online training
and inference. The proposed framework starts with a novel technique that
generates features using the fast Fourier and discrete wavelet transforms of a
textile-based stretch sensor and accelerometer. Using these features, we design
an artificial neural network classifier which is trained online using the
policy gradient algorithm. Experiments on a low power IoT device (TI-CC2650
MCU) with nine users show 97.7% accuracy in identifying six activities and
their transitions with less than 12.5 mW power consumption.
