Pilar Zorrilla Calvo

Antecedents of customer loyalty in residential energy markets: Service quality, satisfaction, trust and switching costs

A conceptual framework is proposed that analyses the effect of perceived service quality, customer satisfaction, trust in the energy provider and perceived switching costs on customer loyalty in residential energy markets. Three distinct dimensions of perceived service quality are identified: technical quality of core services, technical quality of peripheral services and service process quality (functional service quality). The proposed model is tested within the scope of a representative survey of Spanish residential energy customers. Regarding the dimensions of service quality, the results indicate significant effects only of service process quality on satisfaction and, indirectly, on customer loyalty. Loyalty effects of further variables in the model are significant.

Advances in a Multimodal Approach for Dysphagia Analysis Based on Automatic Voice Analysis

Parkinson’s disease (PD) is the second most frequent neurodegenerative disease with prevalence among general population reaching 0.1–1 %, and an annual incidence between 1.3–2.0/10,000 inhabitants. The most obvious symptoms are movement-related such as tremor, rigidity, slowness of movement and walking difficulties and frequently these are the symptoms that lead to the PD diagnoses but also they could have In this sense voice analysis is a safe, non-invasive, and reliable screening procedure for PD patients with dysphagia, which could detect patients at high risk of clinically significant aspiration. In this paper we will present a part of an ongoing project that will evaluate automatic speech analysis based on linear and non-linear features. These can be reliable predictors/indicators of swallowing and balance impairments in PD. An important advantage of voice analysis is its low intrusiveness and easy implementation in clinical practice. Thus, if a significant correlation between these simple analyses and the gold standard video-fluoroscopic analysis will imply simpler and less stressing diagnostic test for the patients as well as the use of cheaper analysis systems.

Analysis of non-invasive writing signals, applied to Essential Tremor: A nonlinear approach

In this paper, which is part of a wide-ranging cross study for the diagnosis of Essential Tremor, we present a selection of nonlinear biomarkers from drawings and handwriting. These include Fractal Dimension and Entropy. The presence of integrated features of other diseases such as stress will be also analyzed. Then these new biomarkers will be integrated in future works with those obtained in the Biodonostia study. It should be highlighted that the use of this technology could provide undoubted benefits towards the development of more sustainable, low cost, high quality, non-invasive technologies. These systems are easily adaptable to the user and environment, and from a social and economic point of view can be very useful in real complex environments.

Automatic voice analysis for dysphagia detection

ABSTRACT Purpose: A videofluoroscopic analysis is the gold standard approach to determine whether a dysphagia problem exists. This procedure is invasive as it involves radiation but also provides the most direct physical evidence of swallowing problems. The main goal of this study was to evaluate an automatic tool based on voice analysis to support medical detection of dysphagia. Methods: An automatic voice analysis system has been developed. Prior to (basal) and immediately following (viscosity) swallowing liquids of varying viscosity and volume, individuals with Parkinson Disease were required to produce the same test word. The acoustic features (linear and non-linear) of this word were then analyzed with regard to specific situations by standard and Machine Learning methods. Results: The results indicated a high degree of accuracy in detecting voice associated with basal and viscosity states. Conclusion: Thus, while the gold standard of dysphagia diagnosis continues to involve video-fluoroscopy analysis, the consideration of voice analysis may prove to be a far simpler and less invasive approach to diagnosis by advanced voice features.

Multi-class Versus One-Class Classifier in Spontaneous Speech Analysis Oriented to Alzheimer Disease Diagnosis

Most of medical developments require the ability to identify samples that are anomalous with respect to a target group or control group, in the sense they could belong to a new, previously unseen class or are not class data. In this case when there are not enough data to train two-class One-class classification appear like an available solution. On the other hand non-linear approaches could give very useful information. The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal. The methods selected in this case are speech biomarkers oriented to Spontaneous Speech and Emotional Response Analysis. In this approach One-class classifiers and two-class classifiers are analyzed. The use of information about outlier and Fractal Dimension features improves the system performance.