  In ski jumping, low repetition rates of jumps limit the effectiveness of
training. Thus, increasing learning rate within every single jump is key to
success. A critical element of athlete training is motor learning, which has
been shown to be accelerated by feedback methods. In particular, a fine-grained
control of the center of gravity in the in-run is essential. This is because
the actual takeoff occurs within a blink of an eye ($\sim$300ms), thus any
unbalanced body posture during the in-run will affect flight. This paper
presents a smart, compact, and energy-efficient wireless sensor system for
real-time performance analysis and biofeedback during ski jumping. The system
operates by gauging foot pressures at three distinct points on the insoles of
the ski boot at 100Hz. Foot pressure data can either be directly sent to
coaches to improve their feedback, or fed into a ML model to give athletes
instantaneous in-action feedback using a vibration motor in the ski boot. In
the biofeedback scenario, foot pressures act as input variables for an
optimized XGBoost model. We achieve a high predictive accuracy of 92.7% for
center of mass predictions (dorsal shift, neutral stand, ventral shift).
Subsequently, we parallelized and fine-tuned our XGBoost model for a RISC-V
based low power parallel processor (GAP9), based on the PULP architecture. We
demonstrate real-time detection and feedback (0.0109ms/inference) using our
on-chip deployment. The proposed smart system is unobtrusive with a slim form
factor (13mm baseboard, 3.2mm antenna) and a lightweight build (26g). Power
consumption analysis reveals that the system's energy-efficient design enables
sustained operation over multiple days (up to 300 hours) without requiring
recharge.
