According to the World Health Organization, Healthcare Technology (HT) is defined as the application of techniques and knowledge in the way of devices, medicaments, vaccines, procedures, and systems in order to develop solutions for healthcare problems and enhance the quality of life. Clinical Engineering has emerged as an interdisciplinary profession in the areas of medical equipment and technology management. With the correct support of Information and Communication Technologies (ICTs), these and others questions may be resolved through the ubiquitous environments and services that allow the acquisition, processing, diagnostic, transmission, and information-sharing in real time. Ubiquitous healthcare is a new paradigm that allows developing models and tools that improve the processes through monitoring, evaluation, prediction, and decision-making of the medical equipment condition. This chapter presents an ubiquitous management methodology for predictive maintenance with support of ICT and predictive analysis techniques that enhance decision-making in medical equipment.

This paper compares the performance of three text-to-speech (TTS) models released from June 2021 to January 2022 in order to establish a baseline for Brazilian Portuguese. Those models were trained using dataset for Brazilian Portuguese. The experimental setup considers tts-portuguese dataset to fine-tune the following TTS models: VITS end-to-end model; glowtts and gradtts acoustic models both using hifi-gan vocoder. Performance metrics are arranged into objective and subjective metrics. As subjective metrics, the naturalness and intelligibility are measured based on the mean opinion score (MOS). Results shows that gradtts+hifigan model achieved naturalness of 4.07 MOS, close to performance of current commercial models.

This paper documents the development of a special case of multilingual Automatic Speech Recognition model, specifically tailored to attend two languages spoken by the majority of Latin America, Portuguese and Spanish. The bilingual model combines Language Identification and Speech Recognition developed with the Wav2Vec2.0 architecture and trained on several open and private speech datasets. In this model, the feature encoder is trained jointly for all tasks and different context encoders are trained for each task. The model is evaluated separately on two tasks: language identification and speech recognition. The results indicate that this model achieves good performance on speech recognition and average performance on language identification, training on a low quantity of speech material. The average accuracy of the language identification module on the MLS dataset is 66.75%. The average Word Error Rate in the same scenario is 13.89%, which is better than average 22.58% achieved by the commercial speech recognizer developed by Google.

Social media data has changed the way big data is used. The amount of data available offers more natural insights that make it possible to find relations and social interactions. Natural language processing (NLP) is an essential tool for such a task. NLP promises to scale traditional methods that allow the automation of tasks for social media datasets. A social media text dataset with a large number of attributes is referred to as a high-dimensional text dataset. One of the challenges of high-dimensional text datasets for NLP text clustering is that not all the measured variables are important for understanding the underlying phenomena of interest, and dimension reduction needs to be performed. Nonetheless, for text clustering, the existing literature is remarkably segmented, and the well-known methods do not address the problems of the high dimensionality of text data. Thus, different methods were found and classified in four areas. Also, it described metrics and technical tools as well as future directions.

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