Polysemy and brevity versus frequency in language

The pioneering research of G. K. Zipf on the relationship between word frequency and other word features led to the formulation of various linguistic laws. The most popular is Zipf's law for word frequencies. Here we focus on two laws that have been studied less intensively: the meaning-frequency law, i.e. the tendency of more frequent words to be more polysemous, and the law of abbreviation, i.e. the tendency of more frequent words to be shorter. In a previous work, we tested the robustness of these Zipfian laws for English, roughly measuring word length in number of characters and distinguishing adult from child speech. In the present article, we extend our study to other languages (Dutch and Spanish) and introduce two additional measures of length: syllabic length and phonemic length. Our correlation analysis indicates that both the meaning-frequency law and the law of abbreviation hold overall in all the analyzed languages

Eye gaze correlates of motor impairment in VR observation of motor actions

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Methodologies, Models and A lgorithms for Patients Rehabilitation”. Objective: Identify eye gaze correlates of motor impairment in a virtual reality motor observation task in a study with healthy participants and stroke patients. Methods: Participants consisted of a group of healthy subjects (N = 20) and a group of stroke survivors (N = 10). Both groups were required to observe a simple reach-and-grab and place-and-release task in a virtual environment. Additionally, healthy subjects were required to observe the task in a normal condition and a constrained movement condition. Eye movements were recorded during the observation task for later analysis.info:eu-repo/semantics/publishedVersio

GABA deficiency in NF1: a multimodal [11C]-Flumazenil and spectroscopy study

Objective: To provide a comprehensive investigation of the GABA system in patients with Neurofibromatosis type 1 (NF1) that allows understanding the nature of the GABA imbalance in humans at pre- and post-synaptic levels. Methods: In this cross-sectional study, we employed multimodal imaging and spectroscopy measures to investigate GABAA receptor binding, using [11C]- Flumazenil positron emission tomography (PET), and GABA concentration, using magnetic resonance spectroscopy (MRS). 14 adult patients with NF1 and 13 matched controls were included in the study. MRS was performed in the occipital cortex and in a frontal region centered in the functionally localized frontal-eye fields. PET and MRS acquisitions were performed in the same day. Results: Patients with NF1 have reduced concentration of GABA+ in the occipital cortex (P = 0.004) and frontal-eye fields (P = 0.026). PET results showed decreased binding of GABAA receptors in patients in the parietooccipital cortex, midbrain and thalamus, which are not explained by decreased grey matter levels. Conclusions: Abnormalities in the GABA system in NF1 involve both GABA concentration and GABAA receptor density suggestive of neurodevelopmental synaptopathy with both pre- and post-synaptic involvement

Impact Of Two Distinct Dental Anesthesia Simulation Models On The Perception Of Learning By Students

We evaluated an infiltration anesthesia simulation model (IAM) and a conduction anesthesia simulation model (CAM) on the perception of learning by Mexican dental students. Our aim was to compare the perception of learning by dental students trained with two distinct dental anesthesia simulation model (DASM) with dental students who were not trained with a DASM. 3 groups participated in the study: G1 (N=12 students) learned to block the mental nerve (BMN) by participating in a theoretical lecture (stage 1) and a clinical demonstration (stage 2); G2 (N=12 students) learned the BMN by participating in the stage 1, stage 2, and training with the CAM; G3 (N=12 students) learned the BMN by participating in the stage 1, stage 2, and training with the IAM. The groups performed the BMN in a clinical exercise. Working-time of all participants was timed. Perception of learning for all participants was evaluated with a 5-point Likert Scale. The results showed that statistically significant differences were found between score of G1 and score of G2 and score of G3 (P\u3c0.05). No statistically significant differences were found between scores of G2 and scores of G3. G1, G2 and G3 showed an average working-time of 12:42 minutes, 9.75 minutes and 8:03 minutes, respectively (P\u3c0.05). We concluded that the IAM and CAM showed a positive impact on the perception of learning, and the students trained with the IAM showed a shorter working time compared with the students trained with the CAM

A análise de sensibilidade do Twitter numa instituição de ensino superior utilizando Power BI

Twitter, as a social network, acquires special importance in academic institutions, for example in Higher Education Institutions (HEI). This tool allows institutions to not only publicize their work and get feedback from the community about it, but also to keep in touch with their aluminum network and foster conversations between the academic community. In this work, a dashboard was created with the last 39 Tweets, related to this HEI, using the Power BI data visualization tool. From this analysis, we can assess users do not use Twitter do to debate ideas and collaborate, instead this platform is used mainly for topics related to academic leisure activities. This dashboard allows us to understand what is being said about this HEI on Twitter and how to improve the use of this communication platform.info:eu-repo/semantics/acceptedVersio

An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises

Technology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient’s quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.info:eu-repo/semantics/publishedVersio

Towards personalized interaction and corrective feedback of a socially assistive robot for post-stroke rehabilitation therapy

A robotic exercise coaching system requires the capability of automatically assessing a patient’s exercise to in teract with a patient and generate corrective feedback. However, even if patients have various physical conditions, most prior work on robotic exercise coaching systems has utilized generic, pre-defined feedback. This paper presents an interactive approach that combines machine learning and rule-based models to automatically assess a patient’s rehabilitation exercise and tunes with patient’s data to generate personalized corrective feedback. To generate feedback when an erroneous motion occurs, our approach applies an ensemble voting method that leverages predictions from multiple frames for frame-level assessment. According to the evaluation with the dataset of three stroke rehabilitation exercises from 15 post-stroke subjects, our interactive approach with an ensemble voting method supports more accurate frame level assessment (p < 0.01), but also can be tuned with held-out user’s unaffected motions to significantly improve the perfor mance of assessment from 0.7447 to 0.8235 average F1-scores over all exercises (p < 0.01). This paper discusses the value of an interactive approach with an ensemble voting method for personalized interaction of a robotic exercise coaching system.info:eu-repo/semantics/publishedVersio