Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review.

BACKGROUND: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. OBJECTIVE: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. METHODS: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. RESULTS: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. CONCLUSIONS: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail.

CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?

In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of "tales with a message" is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.

Architecture design of a reinforcement environment for learning sign languages.

Different fields such as linguistics, teaching, and computing have demonstrated special interest in the study of sign languages (SL). However, the processes of teaching and learning these languages turn complex since it is unusual to find people teaching these languages that are fluent in both SL and the native language of the students. The teachings from deaf individuals become unique. Nonetheless, it is important for the student to lean on supportive mechanisms while being in the process of learning an SL. Bidirectional communication between deaf and hearing people through SL is a hot topic to achieve a higher level of inclusion. However, all the processes that convey teaching and learning SL turn difficult and complex since it is unusual to find SL teachers that are fluent also in the native language of the students, making it harder to provide computer teaching tools for different SL. Moreover, the main aspects that a second language learner of an SL finds difficult are phonology, non-manual components, and the use of space (the latter two are specific to SL, not to spoken languages). This proposal appears to be the first of the kind to favor the Costa Rican Sign Language (LESCO, for its Spanish acronym), as well as any other SL. Our research focus stands on reinforcing the learning process of final-user hearing people through a modular architectural design of a learning environment, relying on the concept of phonological proximity within a graphical tool with a high degree of usability. The aim of incorporating phonological proximity is to assist individuals in learning signs with similar handshapes. This architecture separates the logic and processing aspects from those associated with the access and generation of data, which makes it portable to other SL in the future. The methodology used consisted of defining 26 phonological parameters (13 for each hand), thus characterizing each sign appropriately. Then, a similarity formula was applied to compare each pair of signs. With these pre-calculations, the tool displays each sign and its top ten most similar signs. A SUS usability test and an open qualitative question were applied, as well as a numerical evaluation to a group of learners, to validate the proposal. In order to reach our research aims, we have analyzed previous work on proposals for teaching tools meant for the student to practice SL, as well as previous work on the importance of phonological proximity in this teaching process. This previous work justifies the necessity of our proposal, whose benefits have been proved through the experimentation conducted by different users on the usability and usefulness of the tool. To meet these needs, homonymous words (signs with the same starting handshape) and paronyms (signs with highly similar handshape), have been included to explore their impact on learning. It allows the possibility to apply the same perspective of our existing line of research to other SL in the future.

Correction: MergedTrie: Efficient textual indexing.

[This corrects the article DOI: 10.1371/journal.pone.0215288.].

MergedTrie: Efficient textual indexing.

The accessing and processing of textual information (i.e. the storing and querying of a set of strings) is especially important for many current applications (e.g. information retrieval and social networks), especially when working in the fields of Big Data or IoT, which require the handling of very large string dictionaries. Typical data structures for textual indexing are Hash Tables and some variants of Tries such as the Double Trie (DT). In this paper, we propose an extension of the DT that we have called MergedTrie. It improves the DT compression by merging both Tries into a single and by segmenting the indexed term into two fixed length parts in order to balance the new Trie. Thus, a higher overlapping of both prefixes and suffixes is obtained. Moreover, we propose a new implementation of Tries that achieves better compression rates than the Double-Array representation usually chosen for implementing Tries. Our proposal also overcomes the limitation of static implementations that does not allow insertions and updates in their compact representations. Finally, our MergedTrie implementation experimentally improves the efficiency of the Hash Tables, the DTs, the Double-Array, the Crit-bit, the Directed Acyclic Word Graphs (DAWG), and the Acyclic Deterministic Finite Automata (ADFA) data structures, requiring less space than the original text to be indexed.

Hospitalizations and economic analysis in psychotic patients with paliperidone palmitate long-acting injection. / Hospitalizaciones y análisis económico en pacientes psicóticos con palmitato de paliperidona de larga duración.

PURPOSE OF THE STUDY: Prevent hospitalizations in psychotic disorders is an important aim, so long-acting antipsychotic is a good option that can control better the correct adherence. Moreover, in the current economic context pharmacoeconomic studies are necessary. We estimate the effect in prevention of paliperidone palmitate long-acting injection (PP-LAI) and calculate the economic cost in the 12 months preceding the start of treatment with PP-LAI and 12 months later. METHODS: Mirror image study of 71 outpatients diagnosed with psychotic disorders and treated with PP-LAI. In a first analysis, we measured along one year: number of hospitalizations/year, number of hospitalization in days, number of emergency assists/year and if there is antipsychotics associated to long-acting treatment. After this phase, we applied Fees Act of Valencia for economic analysis and estimate of the cost per hospitalization (€ 5,640.41) and hospital emergency (€ 187.61). SUMMARY OF RESULTS: After one year of treatment with PP-LAI (mean dose=130.65mg/month), we obtained greater numbers in assistance variables: total hospitalizations decrease, 78.8% (P=.009); shortening in hospitalization days, 89.4% (P=.009); abridgement of number of emergency assists, 79.1% (P=.002); decrease of rate of antipsychotics associated to long-acting treatment, 21% (P<.0001); increase in monotherapy, 53.8% (P<.0001). Therefore, after 12 months of treatment with PP-LAI we obtained a reduction in inpatient spending (savings of € 175,766.54) and increased spending on antipsychotics 32% (equivalent to € 151,126.92). CONCLUSIONS: PP-LAI can be an effective therapy for the treatment of patients with severe psychotic disorders: improves symptomatic stability and can prevent hospitalizations with cost-effective symptom control.

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services.

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.