  This paper introduces an effective solution for retrofitting construction
power tools with low-power IoT to enable accurate activity classification. We
address the challenge of distinguishing between when a power tool is being
moved and when it is actually being used. To achieve classification accuracy
and power consumption preservation a newly released algorithm called MiniRocket
was employed. Known for its accuracy, scalability, and fast training for
time-series classification, in this paper, it is proposed as a TinyML algorithm
for inference on resource-constrained IoT devices. The paper demonstrates the
portability and performance of MiniRocket on a resource-constrained, ultra-low
power sensor node for floating-point and fixed-point arithmetic, matching up to
1% of the floating-point accuracy. The hyperparameters of the algorithm have
been optimized for the task at hand to find a Pareto point that balances memory
usage, accuracy and energy consumption. For the classification problem, we rely
on an accelerometer as the sole sensor source, and BLE for data transmission.
Extensive real-world construction data, using 16 different power tools, were
collected, labeled, and used to validate the algorithm's performance directly
embedded in the IoT device. Experimental results demonstrate that the proposed
solution achieves an accuracy of 96.9% in distinguishing between real usage
status and other motion statuses while consuming only 7kB of flash and 3kB of
RAM. The final application exhibits an average current consumption of less than
15{\mu}W for the whole system, resulting in battery life performance ranging
from 3 to 9 years depending on the battery capacity (250-500mAh) and the number
of power tool usage hours (100-1500h).
