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.

研究提出了一种智能、紧凑且能源高效的无线传感器系统，用于滑雪跳台运动中的实时性能分析和生物反馈，以提高滑雪运动员的学习效果和训练成果。

Skilog：滑雪跳台运动的性能分析和生物反馈的智能传感器系统

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

Learning relies on coordinated synaptic changes in recurrently connected
populations of neurons. Therefore, understanding the collective evolution of
synaptic connectivity over learning is a key challenge in neuroscience and
machine learning. In particular, recent work has shown that the weight matrices
of task-trained RNNs are typically low rank, but how this low rank structure
unfolds over learning is unknown. To address this, we investigate the rank of
the 3-tensor formed by the weight matrices throughout learning. By fitting RNNs
of varying rank to large-scale neural recordings during a motor learning task,
we find that the inferred weights are low-tensor-rank and therefore evolve over
a fixed low-dimensional subspace throughout the entire course of learning. We
next validate the observation of low-tensor-rank learning on an RNN trained to
solve the same task by performing a low-tensor-rank decomposition directly on
the ground truth weights, and by showing that the method we applied to the data
faithfully recovers this low rank structure. Finally, we present a set of
mathematical results bounding the matrix and tensor ranks of gradient descent
learning dynamics which show that low-tensor-rank weights emerge naturally in
RNNs trained to solve low-dimensional tasks. Taken together, our findings
provide novel constraints on the evolution of population connectivity over
learning in both biological and artificial neural networks, and enable reverse
engineering of learning-induced changes in recurrent network dynamics from
large-scale neural recordings.

通过大规模神经记录，我们研究了学习过程中权重矩阵形成的 3-Tensor 的秩，并发现推断得到的权重具有较低的张量秩，并且通过数学结果证明了较低的张量秩权重在训练低维任务的 RNN 中自然地产生。

神经动态的低张量秩学习

Low Tensor Rank Learning of Neural Dynamics

To answer the existence of optimal swimmer learning/teaching strategies, this
work introduces a two-level clustering in order to analyze temporal dynamics of
motor learning in breaststroke swimming. Each level have been performed through
Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on
large and correlated datasets. The induced sparsity selects key points of the
coordination phase without any prior knowledge.

通过两级聚类分析蝶泳游泳中的运动学习的时间动态，采用无监督的 Sparse Fisher-EM 框架，可以高效地处理大规模相关数据集，感应稀疏性选择了关键点的协调阶段。