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.

翻译：在跳台滑雪中，低重复跳跃次数限制了训练效果。因此，提高每次跳跃的学习效率是成功的关键。运动员训练的核心要素是运动学习，研究表明反馈方法可加速这一过程。其中，助滑阶段对重心的精细控制至关重要——实际起跳仅在瞬间完成（约300毫秒），因此助滑过程中任何不平衡的身体姿态都会影响飞行阶段。本文提出一套智能、紧凑且高能效的无线传感器系统，用于跳台滑雪训练中的实时性能分析与生物反馈。该系统通过在滑雪靴鞋垫的三个关键位置以100Hz频率测量足底压力实现功能。足底压力数据可直接传输至教练以优化其指导反馈，亦可输入机器学习模型，通过滑雪靴内的振动电机为运动员提供即时动作反馈。在生物反馈场景中，足底压力作为优化后XGBoost模型的输入变量，我们实现了对重心预测（背向偏移、中立站立、腹向偏移）高达92.7%的预测精度。进一步地，我们基于PULP架构，将XGBoost模型并行化并优化适配至RISC-V低功耗并行处理器（GAP9），通过片上部署实现实时检测与反馈（0.0109毫秒/推理）。该智能系统采用轻薄设计（底板13毫米、天线3.2毫米）与轻量构造（26克），功耗分析表明其能效设计可支持多日持续运行（最长300小时），无需中途充电。