Background: Previous review studies have suggested that computer games can serve as an alternative or additional form of treatment in several areas (schizophrenia, asthma or motor rehabilitation). Although several naturalistic studies have been conducted showing the usefulness of serious video games in the treatment of some abnormal behaviours, there is a lack of serious games specially designed for treating mental disorders.Aim: The purpose of our project was to develop and evaluate a serious video game designed to remediate attitudinal, behavioural and emotional processes of patients with impulse-related disorders.Method and results: The video game was created and developed within the European research project PlayMancer. It aims to prove potential capacity to change underlying attitudinal, behavioural and emotional processes of patients with impulse-related disorders. New interaction modes were provided by newly developed components, such as emotion recognition from speech, face and physiological reactions, while specific impulsive reactions were elicited. The video game uses biofeedback for helping patients to learn relaxation skills, acquire better self-control strategies and develop new emotional regulation strategies. In this article, we present a description of the video game used, rationale, user requirements, usability and preliminary data, in several mental disorders.
The present work reports research efforts toward development and evaluation of a unified framework for automatic bioacoustic recognition of specific insect pests. Our approach is based on capturing and automatically recognizing the acoustic emission resulting from typical behaviors, e.g., locomotion and feeding, of the target pests. After acquisition the signals are amplified, filtered, parameterized, and classified by advanced machine learning methods on a portable computer. Specifically, we investigate an advanced signal parameterization scheme that relies on variable size signal segmentation. The feature vector computed for each segment of the signal is composed of the dominant harmonic, which carry information about the periodicity of the signal, and the cepstral coefficients, which carry information about the relative distribution of energy among the different spectral sub-bands. This parameterization offers a reliable representation of both the acoustic emissions of the pests of interest and the interferences from the environment. We illustrate the practical significance of our methodology on two specific cases: 1) a devastating pest for palm plantations, namely, Rhynchophorus ferrugineus Olivier and 2) a pest that attacks warehouse stored rice (Oryza sativa L.), the rice weevil, Sitophilus oryzae (L.) (both Coleoptera: Curculionidae, Dryophorinae). These pests are known in many countries around the world and contribute for significant economical loss. The proposed approach led to detection results in real field trials, reaching 99.1% on real-field recordings of R. ferrugineus and 100% for S. oryzae.
10In the present work we study the appropriateness of a number of linear and non-linear regression 11 methods employed on the task of combining multiple phonetic boundary predictions. The proposed 12 fusion schemes are independent of the implementation of the individual segmentation engines as well 13 as from their number. In order to illustrate the practical significance of the proposed approach, we 14 employ 112 speech segmentation engines based on hidden Markov models (HMMs).
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