Jose Ignacio Serrano

SyMSS: A syntax-based measure for short-text semantic similarity

Sentence and short-text semantic similarity measures are becoming an important part of many natural language processing tasks, such as text summarization and conversational agents. This paper presents SyMSS, a new method for computing short-text and sentence semantic similarity. The method is based on the notion that the meaning of a sentence is made up of not only the meanings of its individual words, but also the structural way the words are combined. Thus, SyMSS captures and combines syntactic and semantic information to compute the semantic similarity of two sentences. Semantic information is obtained from a lexical database. Syntactic information is obtained through a deep parsing process that finds the phrases in each sentence. With this information, the proposed method measures the semantic similarity between concepts that play the same syntactic role. Psychological plausibility is added to the method by using previous findings about how humans weight different syntactic roles when computing semantic similarity. The results show that SyMSS outperforms state-of-the-art methods in terms of rank correlation with human intuition, thus proving the importance of syntactic information in sentence semantic similarity computation.

A multistrategy approach for digital text categorization from imbalanced documents

The goal of the research described here is to develop a multistrategy classifier system that can be used for document categorization. The system automatically discovers classification patterns by applying several empirical learning methods to different representations for preclassified documents belonging to an imbalanced sample. The learners work in a parallel manner, where each learner carries out its own feature selection based on evolutionary techniques and then obtains a classification model. In classifying documents, the system combines the predictions of the learners by applying evolutionary techniques as well. The system relies on a modular, flexible architecture that makes no assumptions about the design of learners or the number of learners available and guarantees the independence of the thematic domain.

A Multimodal Human–Robot Interface to Drive a Neuroprosthesis for Tremor Management

Tremor is the most prevalent movement disorder, and its incidence is increasing with aging. In spite of the numerous therapeutic solutions available, 65% of those suffering from upper limb tremor report serious difficulties during their daily living. This gives rise to research on different treatment alternatives, amongst which wearable robots that apply selective mechanical loads constitute an appealing approach. In this context, the current work presents a multimodal human-robot interface to drive a neuroprosthesis for tremor management. Our approach relies on the precise characterization of the tremor to modulate a functional electrical stimulation system that compensates for it. The neuroprosthesis is triggered by the detection of the intention to move derived from the analysis of electroencephalographic activity, which provides a natural interface with the user. When a prediction is delivered, surface electromyography serves to detect the actual onset of the tremor in the presence of volitional activity. This information in turn triggers the stimulation, which relies on tremor parameters-amplitude and frequency-derived from a pair of inertial sensors that record the kinematics of the affected joint. Surface electromyography also yields a first characterization of the tremor, together with precise information on the preferred stimulation site. Apart from allowing for an optimized performance of the system, our multimodal approach permits the implementation of redundant methods to both enhance the reliability of the system and adapt to the specific needs of different users. Results with a representative group of patients illustrate the performance of the interface presented here and demonstrate its feasibility.

A SMS normalization system integrating multiple grammatical resources

SMS language presents special phenomena and important deviations from natural language. Every day, an impressive amount of chat messages, SMS messages, and e-mails are sent all over the world. This widespread use makes important the development of systems that normalize SMS language into natural language. However, typical machine translation approaches are difficult to adapt to SMS language because of many irregularities that are shown by this kind of language. This paper presents a new approach for SMS normalization that combines lexical and phonological translation techniques with disambiguation algorithms at two different levels: lexical and semantic. The method proposed does not depend on big annotated corpus, which is difficult to build and is applied in two different domains showing its easiness of adaptation across different languages and domains. The results obtained by the system outperform some of the existing methods of SMS normalization despite the fact that the Spanish language and the corpus created have some features that complicate the normalization task.

Evolutionary algorithm for noun phrase detection in natural language processing

Noun phrases of a document usually are the main information bearers. Thus, the detection of these units is crucial in many applications related to information retrieval, such as collecting relevant documents by search engines according to a user query, text summarizing, etc. We present an evolutionary algorithm for obtaining a probabilistic finite-state automaton, able to recognize valid noun phrases defined as a sequence of lexical categories. This approach is highly flexible in the sense that the automaton is able to recognize noun phrases similar enough to the ones given by the inferred noun phrase grammar. This flexibility can be allowed thanks to the very accurate set of probabilities provided by the evolutionary algorithm. It works with both, positive and negative examples of the language, thus improving the system coverage, while maintaining its precision. Experimental results show a clear improvement of the performance with respect to others systems

Evolutionary optimization of autonomous vehicle tracks

This paper presents a method for the optimization of reference tracks which is used as maps by an autonomous vehicle. Given a track obtained during a manual driving session by a GPS sensor installed in the vehicle, a reduction of the number of points obtained is needed in order to improve the autonomous tracking and control processing times. It is also needed a noise removal in order to avoid the lateral offset error in automatic controlled driving. The optimization is carried out by an evolutionary strategy approach. After an empirical fine tuning of the algorithm parameters, the experiments show that the algorithm is able to provide reference tracks that result in very closed to the tracks manually optimized by an expert, and with a similar autonomous driving performance.

Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review

Brain‐computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke.

CPWalker: Robotic platform for gait rehabilitation in patients with Cerebral Palsy

Cerebral Palsy (CP) is a disorder of posture and movement due to an imperfection or lesion in the immature brain. CP is often associated to sensory deficits, cognition impairments, communication and motor disabilities, behaviour issues, seizure disorder, pain and secondary musculoskeletal problems. New strategies are needed to help to promote, maintain, and rehabilitate the functional capacity, and thereby diminish the dedication and assistance required and the economical demands that this condition represents for the patient, the caregivers and the whole society. This paper describes the conceptualization and development of the integrated CPWalker robotic platform to support novel therapies for CP rehabilitation. This platform (Smart Walker + exoskeleton) is controlled by a multimodal interface to establish the interaction of CP children with robot-based therapies. The objective of these therapies is to improve the physical skills of children with CP and similar disorders. CPWalker concept will promote the earlier incorporation of CP patients to the rehabilitation therapy and increase the level of intensity and frequency of the exercises according to the task, which will enable the maintenance of therapeutic methods in daily basis, with the intention to lead to significant improvements in the treatment outcome.

An asynchronous BMI system for online single-trial detection of movement intention

This paper presents an approach for an asynchronous BMI proposed as a switching part of a tremor suppression system developed for real-time continuous conditions. The main purpose of this BMI-switch is to anticipate the execution of self-initiated movements performed after relatively long periods of inactivity. The performance indicators used for the detector validation are specially suited for the continuous characteristic of the paradigm used and it is demonstrated that our ERD-based bayesian classifier solution is a reliable option, detecting a high rate of positive cases and generating very few false positives during long intervals of inactivity. The subjects analyzed for our detector validation were patients with neurological tremor caused by different pathologies in order to assure the adaptability of our system.

Development and evaluation of a novel robotic platform for gait rehabilitation in patients with Cerebral Palsy

The term Cerebral Palsy (CP) is a set of neurological disorders that appear in infancy or early childhood and permanently affect body movement and muscle coordination. The prevalence of CP is twothree per 1000 births. Emerging rehabilitation therapies through new strategies are needed to diminish the assistance required for these patients, promoting their functional capability. This paper presents a new robotic platform called CPWalker for gait rehabilitation in patients with CP, which allows them to start experiencing autonomous locomotion through novel robot-based therapies. The platform (smart walker +exoskeleton) is controlled by a multimodal interface that gives high versatility. The therapeutic approach, as well as the details of the interactions may be defined through this interface. CPWalker concept aims to promote the earlier incorporation of patients with CP to the rehabilitation treatment and increases the level of intensity and frequency of the exercises. This will enable the maintenance of therapeutic methods on a daily basis, with the intention of leading to significant improvements in the treatment outcomes. Rehabilitation with free displacement and not restricted to a treadmill.Integration of central nervous system into therapies.Postural control and partial body weight support for individuals with more severe disorders.Assist as needed approach.Locomotion strategy based on laser sensor.