This paper introduces a simple yet effective strategy for exercise
classification and muscle group activation prediction (MGAP). These tasks have
significant implications for personal fitness, facilitating more affordable,
accessible, safer, and simpler exercise routines. This is particularly relevant
for novices and individuals with disabilities. Previous research in the field
is mostly dominated by the reliance on mounted sensors and a limited scope of
exercises, reducing practicality for everyday use. Furthermore, existing MGAP
methodologies suffer from a similar dependency on sensors and a restricted
range of muscle groups, often excluding strength training exercises, which are
pivotal for a comprehensive fitness regimen. Addressing these limitations, our
research employs a video-based deep learning framework that encompasses a broad
spectrum of exercises and muscle groups, including those vital for strength
training. Utilizing the "Workout/Exercises Video" dataset, our approach
integrates the X3D and SlowFast video activity recognition models in an
effective way to enhance exercise classification and MGAP performance. Our
findings demonstrate that this hybrid method obtained via weighted ensemble
outperforms existing baseline models in accuracy. Pretrained models play a
crucial role in enhancing overall performance, with optimal channel reduction
values for the SlowFast model identified near 10. Through an ablation study
that explores fine-tuning, we further elucidate the interrelation between the
two tasks. Our composite model, a weighted-average ensemble of X3D and
SlowFast, sets a new benchmark in both exercise classification and MGAP across
all evaluated categories, offering a robust solution to the limitations of
previous approaches.

通过使用基于视频的深度学习框架，整合 X3D 和 SlowFast 视频活动识别模型的加权融合方法，本研究在锻炼分类和肌肉群激活预测方面取得了显著的性能提高。

基于混合 X3D-SlowFast 网络的视频运动分类和激活肌群预测

Video-based Exercise Classification and Activated Muscle Group  Prediction with Hybrid X3D-SlowFast Network

We present a transfer learning analysis on a sporting environment of the
expanded 3D (X3D) neural networks. Inspired by action quality assessment
methods in the literature, our method uses an action recognition network to
estimate athletes' cumulative race time (CRT) during an ultra-distance
competition. We evaluate the performance considering the X3D, a family of
action recognition networks that expand a small 2D image classification
architecture along multiple network axes, including space, time, width, and
depth. We demonstrate that the resulting neural network can provide remarkable
performance for short input footage, with a mean absolute error of 12 minutes
and a half when estimating the CRT for runners who have been active from 8 to
20 hours. Our most significant discovery is that X3D achieves state-of-the-art
performance while requiring almost seven times less memory to achieve better
precision than previous work.

我们对扩展 3D（X3D）神经网络在体育环境中进行了迁移学习分析，通过运动识别网络来估计运动员在超长距离比赛期间的累计比赛时间（CRT）。我们评估了 X3D 的性能，这是一种扩展小型 2D 图像分类体系结构的行动识别网络，可以沿多个网络轴（包括空间、时间、宽度和深度）扩展。我们证明了该神经网络在短时间内可以提供出色的性能，对于活动时间在 8 到 20 个小时之间的跑步者估计 CRT 的平均绝对误差仅为 12 分钟半。我们最重要的发现是，相较于以前的工作，X3D 在达到更好的精度时所需的内存几乎要少七倍。

一种用于野外运动环境中跑者性能评估的 X3D 神经网络分析

An X3D Neural Network Analysis for Runner's Performance Assessment in a  Wild Sporting Environment

This paper presents X3D, a family of efficient video networks that
progressively expand a tiny 2D image classification architecture along multiple
network axes, in space, time, width and depth. Inspired by feature selection
methods in machine learning, a simple stepwise network expansion approach is
employed that expands a single axis in each step, such that good accuracy to
complexity trade-off is achieved. To expand X3D to a specific target
complexity, we perform progressive forward expansion followed by backward
contraction. X3D achieves state-of-the-art performance while requiring 4.8x and
5.5x fewer multiply-adds and parameters for similar accuracy as previous work.
Our most surprising finding is that networks with high spatiotemporal
resolution can perform well, while being extremely light in terms of network
width and parameters. We report competitive accuracy at unprecedented
efficiency on video classification and detection benchmarks. Code will be
available at: this https URL

本文提出了 X3D，一个高效的视频网络家族，可沿多个网络轴（在空间、时间、宽度和深度方面）逐步扩展微小的 2D 图像分类架构。采用类似于机器学习中的特征选择方法的简单逐步网络扩展方法，在每个步骤中扩展单个轴，从而实现对于复杂性的良好准确性权衡。通过向前递推扩展和向后收缩，将 X3D 扩展到特定目标复杂性。与以前的工作相比，X3D 实现了最先进的性能，同时需要较少的乘加和参数。我们最令人惊讶的发现是，具有高空间时间分辨率的网络可以表现出色，同时在网络宽度和参数方面非常轻量化。我们在视频分类和检测基准测试上报告了具有竞争力的准确性，代码将可在此 https URL 上获得。