Characterization of a Raspberry Pi as the Core for a Low-cost Multimodal EEG-fNIRS Platform.

Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts.

Mass segmentation of mammograms using Markov models associated with constrained clustering.

In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include additional information in the form of constraints to the clustering process, present better visual segmentation results than the original model, as well as a lower final energy which implies a better quality in the final segmentation.

Automation of motor dexterity assessment.

Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network.

Stress modelling and prediction in presence of scarce data.

Objective: Stress at work is a significant occupational health concern. Recent studies have used various sensing modalities to model stress behaviour based on non-obtrusive data obtained from smartphones. However, when the data for a subject is scarce it becomes a challenge to obtain a good model.

Robot training for hand motor recovery in subacute stroke patients: A randomized controlled trial.

Background: Evidence of superiority of robot training for the hand over classical therapies in stroke patients remains controversial. During the subacute stage, hand training is likely to be the most useful.

Aim: To establish whether robot active assisted therapies provides any additional motor recovery for the hand when administered during the subacute stage (<4 months from event) in a Mexican adult population diagnosed with stroke.

Gesture therapy: an upper limb virtual reality-based motor rehabilitation platform.

Virtual reality platforms capable of assisting rehabilitation must provide support for rehabilitation principles: promote repetition, task oriented training, appropriate feedback, and a motivating environment. As such, development of these platforms is a complex process which has not yet reached maturity. This paper presents our efforts to contribute to this field, presenting Gesture Therapy, a virtual reality-based platform for rehabilitation of the upper limb.

Neural reorganization accompanying upper limb motor rehabilitation from stroke with virtual reality-based gesture therapy.

Background: Gesture Therapy is an upper limb virtual reality rehabilitation-based therapy for stroke survivors. It promotes motor rehabilitation by challenging patients with simple computer games representative of daily activities for self-support. This therapy has demonstrated clinical value, but the underlying functional neural reorganization changes associated with this therapy that are responsible for the behavioral improvements are not yet known.

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks.

Objective: The human immunodeficiency virus (HIV) is one of the fastest evolving organisms in the planet. Its remarkable variation capability makes HIV able to escape from multiple evolutionary forces naturally or artificially acting on it, through the development and selection of adaptive mutations. Although most drug resistance mutations have been well identified, the dynamics and temporal patterns of appearance of these mutations can still be further explored.

Gesture therapy: a vision-based system for upper extremity stroke rehabilitation.

Stroke is the main cause of motor and cognitive disabilities requiring therapy in the world. Therefor it is important to develop rehabilitation technology that allows individuals who had suffered a stroke to practice intensive movement training without the expense of an always-present therapist. We have developed a low-cost vision-based system that allows stroke survivors to practice arm movement exercises at home or at the clinic, with periodic interactions with a therapist.