  Cycling power measurement is an indispensable metric with profound
implications for cyclists' performance and fitness levels. It empowers riders
with real-time feedback, supports precise training regimen planning, mitigates
injury risks, and enhances muscular development. Despite these advantages, the
widespread adoption of cycling power meters has been hampered by their
prohibitive cost and deployment complexity. This paper pioneers a
groundbreaking approach to power measurement in cycling, prioritizing
affordability and user-friendliness. To achieve this goal, we introduce a
cutting-edge Internet of Things (IoT) device that seamlessly integrates force
signals with inertial sensor data while leveraging the power of edge machine
learning techniques. In-field experimental evaluations demonstrate that our
prototype can estimate power with remarkable accuracy, boasting a Mean Absolute
Error (MAE) of only 12.29 Watts (4.1\%). Notably, our design emphasizes energy
efficiency, operating in a low-power mode that consumes a mere 50 milliwatts
and offers an exceptional battery life of up to 25.8 hours in always-on active
mode. With an ultra-low latency of 4.33 milliseconds for data processing and
inference, our system ensures real-time power estimation during cycling
activities. Incorporating IoT concepts and devices, this paper marks a
significant milestone in developing cost-effective and accurate cycling power
meters.
