Activity recognition in beach volleyball using a Deep Convolutional Neural Network
Activity recognition in beach volleyball using a Deep Convolutional Neural Network
复制标题
DOI：
10.1007/s10618-017-0495-0
复制
发表时间：
2017-11-01
影响因子：
4.8
通讯作者：
Eskofier, Bjoern M.
中科院分区：
文献类型：
--
作者：
Kautz, Thomas;Groh, Benjamin H.;Eskofier, Bjoern M.
运动中的许多损伤是由于过度使用造成的。这些损伤是职业和非职业海滩排球运动员成绩下降的主要原因。对球员行为的监控可以帮助识别和理解风险因素，并防止此类伤害。目前，耗时的视频检查是海滩排球运动员详细监控的唯一选择。缺乏可靠的自动监测系统阻碍了对过度使用损伤风险因素的调查。本文提出了一种基于可穿戴传感器的海滩排球非侵入式自动监测系统。我们通过设计一个用于基于传感器的活动分类的深度卷积神经网络来研究深度学习在这种情况下的可能性。这种新方法的性能进行了比较，五种常见的分类算法。通过我们的深度卷积神经网络，我们实现了83.2%的分类准确率，从而比其他分类算法高出16.0%。我们的研究结果表明，详细的球员监测海滩排球使用可穿戴传感器是可行的。现有方法和我们的深度神经网络之间的巨大性能差距表明，深度学习有潜力扩展基于传感器的活动识别的边界。
Many injuries in sports are caused by overuse. These injuries are a major cause for reduced performance of professional and non-professional beach volleyball players. Monitoring of player actions could help identifying and understanding risk factors and prevent such injuries. Currently, time-consuming video examination is the only option for detailed player monitoring in beach volleyball. The lack of a reliable automatic monitoring system impedes investigations about the risk factors of overuse injuries. In this work, we present an unobtrusive automatic monitoring system for beach volleyball based on wearable sensors. We investigate the possibilities of Deep Learning in this context by designing a Deep Convolutional Neural Network for sensor-based activity classification. The performance of this new approach is compared to five common classification algorithms. With our Deep Convolutional Neural Network, we achieve a classification accuracy of 83.2%, thereby outperforming the other classification algorithms by 16.0%. Our results show that detailed player monitoring in beach volleyball using wearable sensors is feasible. The substantial performance margin between established methods and our Deep Neural Network indicates that Deep Learning has the potential to extend the boundaries of sensor-based activity recognition.