Deep learning—Accelerating Next Generation Performance Analysis Systems?

Proceedings

Brock, Heike¹ 

关键词: machine learning;    deep learning;    specialized activity recognition;    motion performance analysis;    wearable sensor data;    data processing;   

DOI  :  10.3390/proceedings2060303

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multi-layered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems.

【 授权许可】

CC BY