Multi-Sensor Edge Angle Detection for Performance Analysis in Ski Jumping

In ski jumping, performance during the gliding phase depends on achieving an aerodynamic posture that maximizes the lift-to-drag ratio. In the V-style technique, the ski edge angle is a key determinant. Reducing the edge angle flattens the skis, increases their effective surface area, and improves aerodynamic lift, ultimately contributing to longer flight distances. Ski edge angles are biomechanically constrained by the limited range of ankle inversion. Current sensing solutions widely quantify these angles using multi-system approaches that combine sensor signals through geometric relations. Such configurations require instrumentation on both the boot and the ski, altering mass distribution, affecting balance during flight, and increasing system complexity. To overcome these limitations, this work presents a wearable sensing system that measures both boot inclination and ski edge angle without modifying the ski surface. Two ultrasonic Time of Flight (ToF) sensors and an in-shoe Inertial Measurement Unit (IMU) are integrated into a single boot-mounted unit. Edge angles are estimated by combining ultrasonic distance measurements with IMU data through geometric reconstruction of the boot-ski configuration. Laboratory experiments demonstrate an angle resolution of 0.4500°, a Mean Absolute Error (MAE) of 0.2640°, and a coefficient of determination exceeding 99\% when compared with reference measurements, indicating strong linear agreement between the two modalities. The system achieves an end-to-end latency of 30.31 ms, enabling real-time feedback suitable for athlete training, while consuming 1.28 mW of power. With a total weight of only 18.6 g the proposed system enables unobtrusive measurement of ski edge angle and boot orientation.

Skilog: A Smart Sensor System for Performance Analysis and Biofeedback in Ski Jumping

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.