基于时间序列的运动数据分类_运动分类

基于时间序列的运动数据分类

Pose Estimation AI(PostNet) is becoming more widely accepted and used in our daily lives, starting from Fitness Applications to Research projects. They hold the power to replace so much of the capital spent on buying hardware and other electronics used for the same task. Let's take the example of games, to clone the movement of a real-life sportsperson into a game, heavy equipment is required. I believe that in the future the same task could be efficiently performed by these neural networks.

从健身应用到研究项目，Pose Estimation AI(PostNet)在我们的日常生活中正越来越广泛地被接受和使用。 他们拥有权力来代替花费在购买用于同一任务的硬件和其他电子设备上的大量资本。 让我们以游戏为例，要将现实生活中的运动员的动作克隆到游戏中，需要重型设备。 我相信，将来这些神经网络可以有效地执行相同的任务。

In this article, we will understand some basic concepts that lead up to classifying movement. The topics of discussion will be pose estimation, Movement, BVH file format, and CMU dataset. I will provide various examples and visualizations to help you understand each.

在本文中，我们将了解导致运动分类的一些基本概念。 讨论的主题将是姿势估计，运动，BVH文件格式和CMU数据集。 我将提供各种示例和可视化帮助您理解它们。

姿势估计 (Pose Estimation)

http://www.cs.cmu.edu/~ILIM/projects/IM/humanpose/humanpose.html http://www.cs.cmu.edu/~ILIM/projects/IM/humanpose/humanpose.html中的图片

The primary aim of pose estimation as the name suggests is to replicate the pose of a human body(s) in a given scenario. The posture of a body can naturally be defined by a skeleton, a segmentation, or even by the formation of skinned linear bodies. Most of the algorithms estimate poses by predicting the key points of a body which define certain joints like shoulders, hips, elbows, knees, etc. A human body has about 244 degrees of freedom with about 230 joints. To extract a perfect pose a system would require to show all these specifications and hence the task of perfecting a pose estimation AI is still in progress.

顾名思义，姿势估计的主要目的是在给定场景中复制人体的姿势。 身体的姿势自然可以通过骨骼，分割或什至通过形成皮肤的线性体来定义。 大多数算法是通过预测定义某些关节(例如肩膀，臀部，肘部，膝盖等)的人体关键点来估计姿势的。人体具有约244个自由度和约230个关节。 为了提取一个完美的姿势，系统需要显示所有这些规格，因此完善姿势估计AI的任务仍在进行中。

算法 (Algorithm)

TensorFlow in TensorFlow在 https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5 https://medium.com/tensorflow/real-time-human-pose-estimation-