Boyle Alistair, Ross Gwyneth B, Graham Ryan B
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4827-4830. doi: 10.1109/EMBC44109.2020.9176426.
Biomechanical movement data are highly correlated multivariate time-series for which a variety of machine learning and deep neural network classification techniques are possible. For image classification, convolutional neural networks have reshaped the field, but have been challenging to apply to 3D movement data with its intrinsic multidimensional nonlinear correlations. Deep neural networks afford the opportunity to reduce feature engineering effort, remove model-based approximations that can introduce systematic errors, and reduce the manual data processing burden which is often a bottleneck in biomechanical data acquisition. What classification techniques are most appropriate for biomechanical movement data? Baseline performance for 3D joint centre trajectory classification using a number of traditional machine learning techniques are presented. Our framework and dataset support a robust comparison between classifier architectures over 416 athletes (professional, college, and amateur) from five primary and six non-primary sports performing thirteen non-sport-specific movements. A variety of deep neural networks specifically intended for time-series data are currently being evaluated.
生物力学运动数据是高度相关的多变量时间序列，对此可以采用多种机器学习和深度神经网络分类技术。对于图像分类，卷积神经网络重塑了该领域，但将其应用于具有内在多维非线性相关性的三维运动数据却颇具挑战。深度神经网络提供了减少特征工程工作量、消除可能引入系统误差的基于模型的近似以及减轻人工数据处理负担的机会，而人工数据处理负担往往是生物力学数据采集的瓶颈。哪些分类技术最适合生物力学运动数据？本文给出了使用多种传统机器学习技术对三维关节中心轨迹分类的基线性能。我们的框架和数据集支持对来自五项主要运动和六项非主要运动的416名运动员（职业、大学和业余）进行的13种非特定于运动的动作，在分类器架构之间进行稳健比较。目前正在评估多种专门用于时间序列数据的深度神经网络。
