  The new generation of wireless technologies, fitness trackers, and devices
with embedded sensors can have a big impact on healthcare systems and quality
of life. Among the most crucial aspects to consider in these devices are the
accuracy of the data produced and power consumption. Many of the events that
can be monitored, while apparently simple, may not be easily detectable and
recognizable by devices equipped with embedded sensors, especially on devices
with low computing capabilities. It is well known that deep learning reduces
the study of features that contribute to the recognition of the different
target classes. In this work, we present a portable and battery-powered
microcontroller-based device applicable to a wobble board. Wobble boards are
low-cost equipment that can be used for sensorimotor training to avoid ankle
injuries or as part of the rehabilitation process after an injury. The exercise
recognition process was implemented through the use of cognitive techniques
based on deep learning. To reduce power consumption, we add an adaptivity layer
that dynamically manages the device's hardware and software configuration to
adapt it to the required operating mode at runtime. Our experimental results
show that adjusting the node configuration to the workload at runtime can save
up to 60% of the power consumed. On a custom dataset, our optimized and
quantized neural network achieves an accuracy value greater than 97% for
detecting some specific physical exercises on a wobble board.
