Decision for reconstructive interventions of the upper limb in individuals with tetraplegia: the effect of treatment characteristics

Objective: To determine the effect of treatment characteristics on the\ud decision for reconstructive interventions for the upper extremities (UE) in\ud subjects with tetraplegia. - \ud Setting: Seven specialized spinal cord injury centres in the Netherlands. - \ud Method: Treatment characteristics for UE reconstructive interventions were\ud determined. Conjoint analysis (CA) was used to determine the contribution\ud and the relative importance of the treatment characteristics on the decision\ud for therapy. Therefore, a number of different treatment scenarios using these\ud characteristics were established. Different pairs of scenarios were presented\ud to subjects who were asked to choose the preferred scenario of each set. - \ud Results: forty nine subjects with tetraplegia with a stable C5, C6 or C7\ud lesion were selected. All treatment characteristics significantly influenced\ud the choice for treatment. Relative importance of treatment characteristics\ud were: intervention type (surgery or surgery with FES implant) 13%, number\ud of operations 15%, in patient rehabilitation period 22%, ambulant\ud rehabilitation period 9%, complication rate 15%, improvement of elbow\ud function 10%, improvement of hand function 15%. In deciding for therapy\ud 40% of the subjects focused on one characteristic. - \ud Conclusion: CA is applicable in Spinal Cord Injury medicine to study the\ud effect of health outcomes and non-health outcomes on the decision for\ud treatment. Non-health outcomes which relate to the intensity of treatment\ud are equally important or even more important than functional outcome in the\ud decision for reconstructive UE surgery in subjects with tetraplegia

A quantitative taxonomy of human hand grasps

Background: A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods: This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results: The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions: The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields

Multivariate time series classification with temporal abstractions

The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015