Objective assessment in long-term rehabilitation under real-life recording conditions is a challenging task. We propose a data-driven method to evaluate changes in motor function under uncontrolled, long-term conditions with the low-cost Microsoft Kinect sensor. Instead of using human ratings as ground truth data, we propose kinematic features of hand motion, healthy reference trajectories derived by principal component regression, and methods taken from machine learning to analyze the progression of motor function. We demonstrate the capability of this approach on datasets with repetitive unrestrained bi-manual drumming movements in three-dimensional space of stroke survivors, patients suffering of Parkinson's disease, and a healthy control group. We present processing steps to eliminate the influence of varying recording setups under real-life conditions and offer visualization methods to support clinicians in the evaluation of treatment effects.

Abstract This paper introduces compressed eigenfunctions of the Laplace‐Beltrami operator on 3D manifold surfaces. They constitute a novel functional basis, called the compressed manifold basis, where each function has local support. We derive an algorithm, based on the alternating direction method of multipliers (ADMM), to compute this basis on a given triangulated mesh. We show that compressed manifold modes identify key shape features, yielding an intuitive understanding of the basis for a human observer, where a shape can be processed as a collection of parts. We evaluate compressed manifold modes for potential applications in shape matching and mesh abstraction. Our results show that this basis has distinct advantages over existing alternatives, indicating high potential for a wide range of use‐cases in mesh processing.

Abstract We present a comprehensive data‐driven statistical model for skin and muscle deformation of the human shoulder‐arm complex. Skin deformations arise from complex bio‐physical effects such as non‐linear elasticity of muscles, fat, and connective tissue; and vary with physiological constitution of the subjects and external forces applied during motion. Thus, they are hard to model by direct physical simulation. Our alternative approach is based on learning deformations from multiple subjects performing different exercises under varying external forces. We capture the training data through a novel multi‐camera approach that is able to reconstruct fine‐scale muscle detail in motion. The resulting reconstructions from several people are aligned into one common shape parametrization, and learned using a semi‐parametric non‐linear method. Our learned data‐driven model is fast, compact and controllable with a small set of intuitive parameters – pose, body shape and external forces, through which a novice artist can interactively produce complex muscle deformations. Our method is able to capture and synthesize fine‐scale muscle bulge effects to a greater level of realism than achieved previously. We provide quantitative and qualitative validation of our method.

We propose a method that extracts sparse and spatially localized deformation modes from an animated mesh sequence. To this end, we propose a new way to extend the theory of sparse matrix decompositions to 3D mesh sequence processing, and further contribute with an automatic way to ensure spatial locality of the decomposition in a new optimization framework. The extracted dimensions often have an intuitive and clear interpretable meaning. Our method optionally accepts user-constraints to guide the process of discovering the underlying latent deformation space. The capabilities of our efficient, versatile, and easy-to-implement method are extensively demonstrated on a variety of data sets and application contexts. We demonstrate its power for user friendly intuitive editing of captured mesh animations, such as faces, full body motion, cloth animations, and muscle deformations. We further show its benefit for statistical geometry processing and biomechanically meaningful animation editing. It is further shown qualitatively and quantitatively that our method outperforms other unsupervised decomposition methods and other animation parameterization approaches in the above use cases.