Health Care Professional-Supported Co-Design of a Mime Therapy-Based Serious Game for Facial Rehabilitation.

This research letter presents the co-design process for RG4Face, a mime therapy-based serious game that uses computer vision for human facial movement recognition and estimation to help health care professionals and patients in the facial rehabilitation process.

Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review.

Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.

A Telemedicine Platform for Aphasia: Protocol for a Development and Usability Study.

BACKGROUND: Aphasia is a central disorder of comprehension and expression of language that cannot be attributed to a peripheral sensory deficit or a peripheral motor disorder. The diagnosis and treatment of aphasia are complex. Interventions that facilitate this process can lead to an increase in the number of assisted patients and greater precision in the therapeutic choice by the health professional. OBJECTIVE: This paper describes a protocol for a study that aims to implement a computer-based solution (ie, a telemedicine platform) that uses deep learning to classify vocal data from participants with aphasia and to develop serious games to treat aphasia. Additionally, this study aims to evaluate the usability and user experience of the proposed solution. METHODS: Our interactive and smart platform will be developed to provide an alternative option for professionals and their patients with aphasia. We will design 2 serious games for aphasia rehabilitation and a deep learning-driven computational solution to aid diagnosis. A pilot evaluation of usability and user experience will reveal user satisfaction with platform features. RESULTS: Data collection began in June 2022 and is currently ongoing. Results of system development as well as usability should be published by mid-2023. CONCLUSIONS: This research will contribute to the treatment and diagnosis of aphasia by developing a telemedicine platform based on a co-design process. Therefore, this research will provide an alternative method for health care to patients with aphasia. Additionally, it will guide further studies with the same purpose. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40603.

Efeitos da fisioterapia aquática no equilíbrio de pacientes pós-acidente vascular encefálico: uma revisão integrativa / Effects of aquatic physical therapy on balance in post-cerebrovascular accident patients: an integrative review

Objetivo: Atualizar a literatura sobre os efeitos da terapia aquática no equilíbrio de pacientes pós-AVE e verificar os métodos avaliativos mais empregados. Métodos: A busca na literatura foi realizada em 6 bases de dados Pubmed, Web of Science, Scopus, Medline, PEDro e Cochrane, utilizando a associação de descritores, palavras-chave e operadores booleanos "Stroke" AND "Hydrotherapy" OR "Hydrokinesiotherapy" OR "Aquatic Physiotherapy" AND "Balance", estipulando critérios de inclusão e exclusão. Resultados: Dos 259 estudos identificados, foram selecionados 14 para análise e síntese qualitativa. No geral, os resultados evidenciaram diferenças significativas no equilíbrio de indivíduos com AVE após terapia aquática. Conclusão: Quando comparada às técnicas de fisioterapia neurofuncional convencionais, a fisioterapia aquática apresenta superioridade de eficácia. Os meios avaliativos mais utilizados são a Berg Balance Scale e a Timed Up and Go por se tratarem de ferramentas de rápida e fácil aplicação, além de alta eficácia, demonstrando a relevância do estudo em aspectos de reabilitação funcional em meio a disfunções advindas de comprometimentos neurológicos.

Neural Networks to Recognize Patterns in Topographic Images of Cortical Electrical Activity of Patients with Neurological Diseases.

Software such as EEGLab has enabled the treatment and visualization of the tracing and cortical topography of the electroencephalography (EEG) signals. In particular, the topography of the cortical electrical activity is represented by colors, which make it possible to identify functional differences between cortical areas and to associate them with various diseases. The use of cortical topography with EEG origin in the investigation of diseases is often not used due to the representation of colors making it difficult to classify the disease. Thus, the analyses have been carried out, mainly, based on the EEG tracings. Therefore, a computer system that recognizes disease patterns through cortical topography can be a solution to the diagnostic aid. In view of this, this study compared five models of Convolutional Neural Networks (CNNs), namely: Inception v3, SqueezeNet, LeNet, VGG-16 and VGG-19, in order to know the patterns in cortical topography images obtained with EEG, in Parkinson's disease, Depression and Bipolar Disorder. SqueezeNet performed better in the 3 diseases analyzed, with Parkinson's disease being better evaluated for Accuracy (88.89%), Precison (86.36%), Recall (91.94%) and F1 Score (89.06%), the other CNNs had less performance. In the analysis of the values of the Area under ROC Curve (AUC), SqueezeNet reached (93.90%) for Parkinson's disease, (75.70%) for Depression and (72.10%) for Bipolar Disorder. We understand that there is the possibility of classifying neurological diseases from cortical topographies with the use of CNNs and, thus, creating a computational basis for the implementation of software for screening and possible diagnostic assistance.

Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.

BACKGROUND: Mental disorders are normally diagnosed exclusively on the basis of symptoms, which are identified from patients' interviews and self-reported experiences. To make mental health diagnoses and monitoring more objective, different solutions have been proposed such as digital phenotyping of mental health (DPMH), which can expand the ability to identify and monitor health conditions based on the interactions of people with digital technologies. OBJECTIVE: This article aims to identify and characterize the sensing applications and public data sets for DPMH from a technical perspective. METHODS: We performed a systematic review of scientific literature and data sets. We searched 8 digital libraries and 20 data set repositories to find results that met the selection criteria. We conducted a data extraction process from the selected articles and data sets. For this purpose, a form was designed to extract relevant information, thus enabling us to answer the research questions and identify open issues and research trends. RESULTS: A total of 31 sensing apps and 8 data sets were identified and reviewed. Sensing apps explore different context data sources (eg, positioning, inertial, ambient) to support DPMH studies. These apps are designed to analyze and process collected data to classify (n=11) and predict (n=6) mental states/disorders, and also to investigate existing correlations between context data and mental states/disorders (n=6). Moreover, general-purpose sensing apps are developed to focus only on contextual data collection (n=9). The reviewed data sets contain context data that model different aspects of human behavior, such as sociability, mood, physical activity, sleep, with some also being multimodal. CONCLUSIONS: This systematic review provides in-depth analysis regarding solutions for DPMH. Results show growth in proposals for DPMH sensing apps in recent years, as opposed to a scarcity of public data sets. The review shows that there are features that can be measured on smart devices that can act as proxies for mental status and well-being; however, it should be noted that the combined evidence for high-quality features for mental states remains limited. DPMH presents a great perspective for future research, mainly to reach the needed maturity for applications in clinical settings.

A computer vision-based mobile tool for assessing human posture: A validation study.

BACKGROUND AND OBJECTIVE: Non-invasive methods for postural assessment are tools used for tracking and monitoring the progression of postural deviations. Different computer-based methods have been used to assess human posture, including mobile applications based on images and sensors. However, such solutions still require manual identification of anatomical points. This study aims to present and validate the NLMeasurer, a mobile application for postural assessment. This application takes advantage of the PoseNet, a solution based on computer vision and machine learning used to estimate human pose and identify anatomical points. From the identified points, NLMeasurer calculates postural measures. METHODS: Twenty participants were photographed in front view while using surface markers over anatomical landmarks. Then, the surface markers were removed, and new photos were taken. The photos were analyzed by two examiners, and six postural measurements were computed with NLMeasurer and a validated biophotogrammetry software. One-sample t-test and Bland Altman procedure were used to assess agreement between the methods, and Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-rater reliability. RESULTS: Postural measurements calculated using the NLMeasurer were in agreement with the biophotogrammetry software. Furthermore, there was good inter- and intra-rater reliability for most photos without surface markers. CONCLUSIONS: NLMeasurer demonstrated to be a valid tool method to assess postural measurements in the frontal view. The use of surface markers on specific anatomical landmarks (i.e., ears, iliac spines and ankles) can facilitate the digital identification of these landmarks and improve the reliability of the postural measurements performed with NLMeasurer.

Dempster-Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing.

The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). In this paper, we investigate the identification and treatment of uncertainty in CEP-based IoT applications. We propose the DST-CEP, an approach that uses the Dempster-Shafer Theory to treat uncertainties. By using this theory, our solution can combine unreliable sensor data in conflicting situations and detect correct results. DST-CEP has an architectural model for treating uncertainty in events and its propagation to processing rules. We describe a case study using the proposed approach in a multi-sensor fire outbreak detection system. We submit our solution to experiments with a real sensor dataset, and evaluate it using well-known performance metrics. The solution achieves promising results regarding Accuracy, Precision, Recall, F-measure, and ROC Curve, even when combining conflicting sensor readings. DST-CEP demonstrated to be suitable and flexible to deal with uncertainty.

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals.

Traditionally, mental health specialists monitor their patients' social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson's correlation coefficient >70%) with individuals' social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

Time estimation exposure modifies cognitive aspects and cortical activity of attention deficit hyperactivity disorder adults.

AIM: This study investigated whether time-estimation task exposure influences the severity of Attention Deficit Hyperactivity Disorder (ADHD), as well as theta band activity in the dorsolateral prefrontal cortex and ventrolateral prefrontal cortex. MATERIAL AND METHODS: Twenty-two patients with ADHD participated in a crossover experiment with a visual time-estimation task under control conditions (without exposure to time estimation tasks) and experimental (thirty days exposure to time-estimation tasks) in association with electroencephalographic analysis of theta band. RESULTS: ADHD patients with thirty days of time-estimation task exposure presented a worse performance of the time-estimation task, as revealed by the measurements of the absolute error and relative error (p ≤ 0.05). However, our findings show the improvement of self-reported symptoms of attention, impulsivity, and emotional control in patients after the time-estimation task exposure (p = 0.0001). Moreover, the theta band oscillations in the right dorsolateral prefrontal cortex and in the ventrolateral prefrontal increased with thirty days of time-estimation task exposure (p ≤ 0.05). CONCLUSION: We propose that the decrease in EEG theta power may indicate an efficient accumulation of temporal pulses, which could be responsible for the improvement in the patient cognitive aspects as demonstrated by the current study. Time-estimation task improves ADHD cognitive symptoms, with a substantial increase in cortical areas activity related to attention and memory, suggesting its use as a tool for cognitive timing function management and non-invasive therapeutic aid in ADHD.

Methylphenidate modifies activity in the prefrontal and parietal cortex accelerating the time judgment.

Methylphenidate produces its effects via actions on cortical areas involved with attention and working memory, which have a direct role in time estimation judgment tasks. In particular, the prefrontal and parietal cortex has been the target of several studies to understand the effect of methylphenidate on executive functions and time interval perception. However, it has not yet been studied whether acute administration of methylphenidate influences performance in time estimation task and the changes in alpha band absolute power in the prefrontal and parietal cortex. The current study investigates the influence of the acute use of methylphenidate in both performance and judgment in the time estimation interpretation through the alpha band absolute power activity in the prefrontal and parietal cortex. This is a double-blind, crossover study with a sample of 32 subjects under control (placebo) and experimental (methylphenidate) conditions with absolute alpha band power analysis during a time estimation task. We observed that methylphenidate does not influence task performance (p > 0.05), but it increases the time interval underestimation by over 7 s (p < 0.001) with a concomitant decrease in absolute alpha band power in the ventrolateral prefrontal cortex and dorsolateral prefrontal cortex and parietal cortex (p < 0.001). Acute use of methylphenidate increases the time interval underestimation, consistent with reduced accuracy of the internal clock mechanisms. Furthermore, acute use of methylphenidate influences the absolute alpha band power over the dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, and parietal cortex.