通过可穿戴传感器和深度学习实现数据驱动的中风康复
Towards data-driven stroke rehabilitation via wearable sensors and deep learning
摘要：
中风后的恢复通常是不完全的，但康复训练可能通过参与内源性神经可塑性来加强恢复。在中风的临床前模型中，需要高剂量的康复训练来恢复动物患肢的功能性运动。然而，在人类中，加强恢复所需的训练剂量尚不清楚。这种无知源于缺乏客观、务实的方法来衡量康复活动中的训练剂量。在这里，为了开发测量方法，我们采取了关键的第一步，自动识别功能原语，活动的基本构建块。48名慢性中风患者在佩戴惯性测量单元（IMU）以捕获上身运动的同时进行各种康复活动。基元是由人类标记者识别的，他们标记并分割相关的IMU数据。我们使用机器学习对这些原语进行自动分类。我们设计了一个卷积神经网络模型，其性能优于现有方法。该模型包括初始模块，用于计算传感器数据中不同物理量的单独嵌入。此外，它用实例规范化（使用从测试数据计算的统计数据）取代了批规范化（基于从训练数据计算的统计数据执行规范化）。当将该方法应用于新患者时，这增加了对可能的分布变化的鲁棒性。通过这种方法，我们达到了70%的平均分类准确率。因此，使用基于IMU的运动捕捉和深度学习的组合，我们能够自动识别图元。这种方法建立在客观测量的康复训练基础上，能够识别和计算累积到训练剂量的功能基元。
英文摘要：
Recovery after stroke is often incomplete, but rehabilitation training may potentiate recovery by engaging endogenous neuroplasticity. In preclinical models of stroke, high doses of rehabilitation training are required to restore functional movement to the affected limbs of animals. In humans, however, the necessary dose of training to potentiate recovery is not known. This ignorance stems from the lack of objective, pragmatic approaches for measuring training doses in rehabilitation activities. Here, to develop a measurement approach, we took the critical first step of automatically identifying functional primitives, the basic building block of activities. Forty-eight individuals with chronic stroke performed a variety of rehabilitation activities while wearing inertial measurement units (IMUs) to capture upper body motion. Primitives were identified by human labelers, who labeled and segmented the associated IMU data. We performed automatic classification of these primitives using machine learning. We designed a convolutional neural network model that outperformed existing methods. The model includes an initial module to compute separate embeddings of different physical quantities in the sensor data. In addition, it replaces batch normalization (which performs normalization based on statistics computed from the training data) with instance normalization (which uses statistics computed from the test data). This increases robustness to possible distributional shifts when applying the method to new patients. With this approach, we attained an average classification accuracy of 70%. Thus, using a combination of IMU-based motion capture and deep learning, we were able to identify primitives automatically. This approach builds towards objectively-measured rehabilitation training, enabling the identification and counting of functional primitives that accrues to a training dose.
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