基于RNN预测分类的临床试验中接球成功的早期识别
Early Recognition of Ball Catching Success in Clinical Trials with RNN-Based Predictive Classification
摘要：
运动障碍会影响与动态物体的互动，例如接球。临床接球试验的分类可能会让我们深入了解手臂和球运动关系中存在的病理变化。准确的，但也需要早期的决定，以分类捕手的第一个球接触之前，接球尝试。为了获得有临床价值的结果，需要至少75%的显著决策置信度。因此，必须同时优化三个相互竞争的目标：准确性、早期性和决策信心。在这里，我们提出了一种耦合的分类和预测方法，用于早期时间序列分类：预测性的生成递归神经网络（RNN）根据已有的观察结果预测球轨迹的下一个数据点;判别RNN根据可用的数据点和展开的序列预测不断生成分类猜测。我们将我们的方法（我们称之为预测序列分类（PSC））与最先进的序列学习器进行比较，包括各种RNN和时间卷积网络（TCN）架构。在这个艰难的现实世界的任务中，我们可以始终如一地证明PSC在识别早期的准确性和置信度方面优于所有其他模型。具体而言，PSC能够在第一次触球前123毫秒就自信地对接球试验的成功进行分类。我们的结论是，PSC是一个很有前途的方法，早期的时间序列分类时，需要准确和自信的决定。
英文摘要：
Motor disturbances can affect the interaction with dynamic objects, such as catching a ball. A classification of clinical catching trials might give insight into the existence of pathological alterations in the relation of arm and ball movements. Accurate, but also early decisions are required to classify a catching attempt before the catcher's first ball contact. To obtain clinically valuable results, a significant decision confidence of at least 75% is required. Hence, three competing objectives have to be optimized at the same time: accuracy, earliness and decision-making confidence. Here we propose a coupled classification and prediction approach for early time series classification: a predictive, generative recurrent neural network (RNN) forecasts the next data points of ball trajectories based on already available observations; a discriminative RNN continuously generates classification guesses based on the available data points and the unrolled sequence predictions. We compare our approach, which we refer to as predictive sequential classification (PSC), to state-of-the-art sequence learners, including various RNN and temporal convolutional network (TCN) architectures. On this hard real-world task we can consistently demonstrate the superiority of PSC over all other models in terms of accuracy and confidence with respect to earliness of recognition. Specifically, PSC is able to confidently classify the success of catching trials as early as 123 milliseconds before the first ball contact. We conclude that PSC is a promising approach for early time series classification, when accurate and confident decisions are required.
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