Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones.

ABSTRACT: The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activit

SUBMITTER: Creagh AP 

PROVIDER: S-EPMC8275610 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature