We present a novel framework to analyze hand force signals and to capture their key spatio-temporal patterns in order to characterize Rheumatoid Arthritis. We introduce a new continuous representation of hand force and derive optimal intra-class alignments using the notion of Karcher means on the quotient space under the action of the warping group. We apply this idea to temporally register hand force signal data using non-linear time warping. As a result, the original signals are separated into their phase and amplitude components. To capture the amplitude and phase variabilities in force functions we compute the dominant eigenfunctions of the covariance operator using functional principal component analysis. Finally, we use support vector machine classifiers to learn priors from current state-of-the-art features and additional features derived from our functional data analysis framework. The experimental results demonstrate that the proposed framework generates clinically relevant features leading to state-of-the-art classification performance.
['Chafik Samir', 'Sebastian Kurtek', 'Anuj Srivastava', 'Noé Gomes Borges']
An elastic functional data analysis framework for preoperative evaluation of patients with Rheumatoid Arthritis