These people at least realize the total lack of objective measurements anywhere in stroke. Without that there will be no forward progress in stroke recovery. Now to propagate this knowledge to all stroke researchers, professors and leaders.
Aakash Kaku∗ ark576@nyu.edu Center for Data Science New York University
Avinash Parnandi∗ avinash.Parnandi@nyulangone.org Department of Neurology New York University School of Medicine
Anita Venkatesan anita.venkatesan@nyulangone.org Department of Neurology New York University School of Medicine
Natasha Pandit ngp238@nyu.edu Department of Neurology New York University School of Medicine
Heidi Schambra† Heidi.Schambra@nyulangone.org Department of Neurology New York University School of Medicine
Carlos Fernandez-Granda† cfgranda@cims.nyu.edu Center for Data Science Courant Institute of Mathematical Sciences New York University
∗ Equal contribution † Joint corresponding/last authors.
Abstract
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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