基于堆叠惯性信号的跳台滑雪动作识别

Action recognition of ski jumping based on stacked inertial signals

动作识别是滑雪运动状态智能监测的关键环节之一。本文以跳台滑雪运动为研究对象,通过堆叠的方式将不同惯性传感器、不同关节点的数据进行融合生成结构化的数据,从而利用深度卷积神经网络实现跳台滑雪动作的识别。首先对采集到的跳台滑雪运动过程中的不同传感器、人体不同关节点的惯性传感数据进行归一化处理映射至[0,1]之间,然后通过颜色映射将各类数据堆叠生成图像,接着利用Resnet等二维卷积神经网络对跳台滑雪中的动身至助滑、直线助滑、曲线助滑、起跳及早期飞行、稳定飞行及落地共5类动作的堆叠惯性信号图像进行识别。实验结果表明,对9次跳台滑雪数据融合后生成的2250幅堆叠惯性信号图像进行识别,召回率和准确率达到了93.8%和91.7%;同时分析了单个类别惯性传感器对各关节点数据融合后的识别结果的影响。本文提出的不同传感器、不同关节点堆叠惯性信号融合和动作识别方法能够为跳台滑雪运动的智能化分析提供支撑。

Motion recognition is one of the key links in the intelligent monitoring of ski jumping.This paper takes ski jumping as the research object,and fuses the data of different inertial sensors and different joint nodes to generate structured data by stacking,to realize the recognition of ski jumping movements by using deep convolutional neural network.Firstly,the collected inertial sensing data of different sensors and different human body points in the process of ski jumping are normalized and mapped to between[0,1].And then using color mapping to stack all kinds of data to create an image.Then use two-dimensional convolutional neural networks such as Resnet,to identify 5types of movements in ski jumping:start-to-slip,straight-line assist,curve-assist,take-off and early flight,stable flight and landing.The experimental results show that the 2250stacked inertia signal images generated by 9times of ski jumping data fusion are recognized,and the recall rate and accuracy are 93.8%and 91.7%,respectively.At the same time,the influence of a single class inertial sensor on the recognition result of the fusion data of each joint node is analyzed.The proposed method of stacking inertial signal fusion and action recognition of different sensors and different joints can provide support for intelligent analysis of ski jumping.