Presenting in Virtual Worlds: An Architecture for a 3D Anthropomorphic Presenter

Multiparty-interaction technology is changing entertainment, education, and training. Deployed examples of such technology include embodied agents and robots that act as a museum guide, a news presenter, a teacher, a receptionist, or someone trying to sell you insurance, homes, or tickets. In all these cases, the embodied agent needs to explain and describe. This article describes the design of a 3D virtual presenter that uses different output channels (including speech and animation of posture, pointing, and involuntary movements) to present and explain. The behavior is scripted and synchronized with a 2D display containing associated text and regions (slides, drawings, and paintings) at which the presenter can point. This article is part of a special issue on interactive entertainment

Use of eHealth platforms and apps to support monitoring and management of home-quarantined patients with COVID-19 in the province of Trento, Italy: app development and implementation

Background: Italy was the first country to largely experience the COVID-19 epidemic among other Western countries during the so-called first wave of the COVID-19 pandemic. Proper management of an increasing number of home-quarantined individuals created a significant challenge for health care authorities and professionals. This was especially true when considering the importance of remote surveillance to detect signs of disease progression and consequently regulate access to hospitals and intensive care units on a priority basis. Objective: In this paper, we report on an initiative promoted to cope with the first wave of the COVID-19 epidemic in the Spring/Summer of 2020, in the Autonomous Province of Trento, Italy. A purposefully built app named TreCovid19 was designed to provide dedicated health care staff with a ready-to-use tool for remotely monitoring patients with progressive symptoms of COVID-19, who were home-quarantined during the first wave of the epidemic, and to focus on those patients who, based on their self-reported clinical data, required a quick response from health care professionals. Methods: TreCovid19 was rapidly developed to facilitate the monitoring of a selected number of home-quarantined patients with COVID-19 during the very first epidemic wave. The app was built on top of an existing eHealth platform, already in use by the local health authority to provide home care, with the following functionalities: (1) to securely collect and link demographic and clinical information related to the patients and (2) to provide a two-way communication between a multidisciplinary health care team and home-quarantined patients. The system supported patients to self-assess their condition and update the multidisciplinary team on their health status. The system was used between March and June 2020 in the province of Trento. Results: A dedicated multidisciplinary group of health care professionals adopted the platform over a period of approximately 3 months (from March-end to June 2020) to monitor a total of 170 patients with confirmed COVID-19 during home quarantine. All patients used the system until the end of the initiative. The TreCovid19 system has provided useful insights of possible viability and impact of a technological–organizational asset to manage a potentially critical workload for the health care staff involved in the periodic monitoring of a relevant number of quarantined patients, notwithstanding its limitations given the rapid implementation of the whole initiative. Conclusions: The technological and organizational model adopted in response to the COVID-19 pandemic was developed and finalized in a relatively short period during the initial few weeks of the epidemic. The system successfully supported the health care staff involved in the periodic monitoring of an increasing number of home-quarantined patients and provided valuable data in terms of disease surveillance

General Overview of the SPEAR Speech Recognition System

This work presents a general overview of a new speech processing and recognition tool (SPEAR). The SPEAR system handles speech signals in different analysis levels. First, a signal processing stage is used to adjust and analyze the speech waveform with different techniques (Fourier analysis, noise filtering, signal segmentation, etc). Second, a feature extraction and data mining module are used to extract the main parameters needed for describing every speech segment (formant frequencies, energy, LP, cepstrum and PLP coefficients, etc.). The speech recognition is performed in two phases. The first one is a frame by frame phone estimation by means of a set of parallel associative memories and the second one uses a DTW algorithm for complete word matching. Experimental results show a good performance of the SPEAR tool for speaker dependent and independent tests under noisy environments (over 15 dB S/N rate)

A Parallel Associative Memory Architecture for Phone Estimation

This paper presents a parallel associative memory (PAM) method for phone estimation in speech recognition applications. The present method is the core of the SPEAR speech analysis and recognition tool developed at the University of Genova, Italy. The PAM architecture is explained in detail and compared in cost/performance tradeoff with other State of the Art systems. Representative experiments are reported to show the PAM\u2019s operation under clean and noisy environments. The current tests show accurate phone recognition levels over a S/N rate >= 15 dB and good performance when compared to other phone estimators

Handwritten Digit Recognition by means of an Holographic Associative Memory

In this paper, an Holographic Associative Memory (HAM) is proposed for recognizing handwritten variations of the 10 digits. First, the handwritten characters were taken from the NIST standard database in order to extract relevant features from each one of them. Each digit was represented so, as a vector of 112 features constructed by dividing each character in 16 equal sized partitions, each one used to extract 7 different features for recognition. Second, these feature vectors and reduced combinations of them, were input to train several HAM systems respectively. Then, all these memories were test with a new set of patterns and the lowest-error HAM was choosen as the best training set. The features used in this last memory were taken as the most significant variables for describing each digit in the database. Finally, these most significant features were used to show the behaviour of the recognition rate when training the HAM with reduced training sets. Some final conclusions are reported and future work directions are proposed

Improving Text Entry Performance for Spanish-Speaking Non-Expert and Impaired Users

In this paper, an efficient method is described which is able to improve the efficiency of typing texts for non-expert and impaired users. Specifically, we propose an innovative keyboard configuration that improves typing performance in languages with transparent orthographies. By adopting a novel orthogonal framework, the configuration of the keyboard is defined as a 2-D regular array of keys. According to this scheme it is possible to input, in a direct and intuitive way, any possible combination of pseudo-syllables (which are text entry units with simpler consonant-vowel phonemic structure), being also possible to introduce single characters in the classical, letter by letter, way. The orthogonal keyboard scheme has been applied and tested with the Spanish language, for it is the most spoken transparent language in the world. The performed tests show a significant improvement of the efficiency in alphanumeric text typing