The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech.

Early detection is crucial for managing incurable disorders, particularly autism spectrum disorder (ASD). Unfortunately, a considerable number of individuals with ASD receive a late diagnosis or remain undiagnosed. Speech holds a critical role in ASD, as a significant number of affected individuals experience speech impairments or remain non-verbal.

Facial Emotion Recognition of 16 Distinct Emotions From Smartphone Videos: Comparative Study of Machine Learning and Human Performance.

Background: The development of automatic emotion recognition models from smartphone videos is a crucial step toward the dissemination of psychotherapeutic app interventions that encourage emotional expressions. Existing models focus mainly on the 6 basic emotions while neglecting other therapeutically relevant emotions. To support this research, we introduce the novel Stress Reduction Training Through the Recognition of Emotions Wizard-of-Oz (STREs WoZ) dataset, which contains facial videos of 16 distinct, therapeutically relevant emotions.

Navigating disruption in the PID landscape: embracing opportunities and anticipating threats in the next ten years.

Introduction: The International Patient Organisation for Primary Immunodeficiencies (IPOPI) held its third edition of the Global Multi-Stakeholders' Summit, gathering key primary immunodeficiencies (PID) stakeholders and experts to discuss and foment global collaboration.

Methods: This edition focused on the impact of genomic medicine in PID treatment, the role of digital health, including artificial intelligence, in PID care, and how to anticipate and minimise risks to ensure optimal patient access to care.

Results: These discussions aimed to examine current hurdles and brainstorm feasible solutions and priorities for the PID community in these areas in the next ten years.

Quality in science communication with communicative artificial intelligence: A principle-based framework.

The rapid advancement of communicative artificial intelligence (ComAI) is profoundly impacting science communication, offering new opportunities for easier and more audience-oriented communication. However, it also poses several challenges for its practice. Based on a narrative review of literature on science communication and ComAI quality, this article develops a framework of quality principles for science communication with ComAI.

Digital interventions in mental health: An overview and future perspectives.

As e-health offerings rapidly expand, they are transforming and challenging traditional mental health care systems globally, presenting both promising opportunities and significant risks. This article critically examines the potential and pitfalls of integrating digital technologies into mental health care, particularly in the realms of diagnosis, prevention, and treatment. It explores current advancements and evidence-based practices, and provides a vision for how future technologies can evolve responsibly to meet mental health needs.

Bridging acute and chronic stress effects on inflammation: protocol for a mixed-methods intensive longitudinal study.

Acute stress triggers adaptive physiological responses-including transient increases in inflammatory cytokines-while chronic stress is associated with sustained inflammatory activity that may underlie the development of various disorders. Despite extensive research on each stress type individually, the transition and interaction between them remain underexplored. This study aims to address this gap by employing an intensive longitudinal measurement burst design.

FedKDC: Consensus-Driven Knowledge Distillation for Personalized Federated Learning in EEG-Based Emotion Recognition.

Federated learning (FL) has gained prominence in electroencephalogram (EEG)-based emotion recognition because of its ability to enable secure collaborative training without centralized data. However, traditional FL faces challenges due to model and data heterogeneity in smart healthcare settings. For example, medical institutions have varying computational resources, which creates a need for personalized local models.

Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care.

New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder (MDD) during and after treatment. To test this hypothesis, we collected 550 speech samples from telephone-based clinical interviews with 267 individuals in routine care. With this data, we trained and evaluated a machine learning system to identify the absence/presence of a MDD diagnosis (as assessed with the Structured Clinical Interview for DSM-IV) from paralinguistic speech characteristics.

Explainable Depression Classification Based on EEG Feature Selection From Audio Stimuli.

With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have been widely proposed. However, existing studies have mostly focused on the accuracy of depression recognition, ignoring the association between features and models. Additionally, there is a lack of research on the contribution of different features to depression recognition.

An ecological momentary assessment study assessing repetitive negative thinking as a predictor for psychopathology.

Repetitive negative thinking (RNT), an important transdiagnostic process, is commonly assessed using trait questionnaires. While these instruments ask respondents to estimate their general tendency towards RNT, ecological momentary assessment (EMA) allows to assess how much individuals actually engage in RNT in their daily lives. In a sample of N =  1,176 adolescents and young adults, we investigated whether average levels of RNT assessed via EMA predicted psychopathological symptoms.

Brief use of behavioral activation features predicts benefits of self-help app on depression symptoms: Secondary analysis of a selective prevention trial in young people.

Objective: To explore which cognitive behavioral therapy (CBT) self-help app usage predicted depression during a selective prevention trial.

Method: A recent controlled trial (ECoWeB-PREVENT) randomized young people aged 16-22, at increased risk for depression because of elevated worry/rumination, negative appraisals, and/or rejection sensitivity but without past or current history of major depression, to apps that provided self-monitoring, self-monitoring plus CBT self-help, or self-monitoring plus emotional competency self-help. Self-help included coping strategies for moment-by-moment use (Tools) and self-learning/planning exercises (Challenges).

Towards Predicting Menstrual Cycle Phases Exploiting Paralinguistic Features.

As a growing number of people focus on understanding their bodies, the menstrual cycle and its impact on reproduction are gaining attention. Several studies have shown that the voice changes during the menstrual cycle. However, existing research primarily employs comparative analysis to detect these differences.

Personalised Speech-Based PTSD Prediction Using Weighted-Instance Learning.

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples' daily life and can affect their mental, physical, or social wellbeing, which is why a timely and professional treatment is required. In this paper, we propose a personalised speech-based PTSD prediction approach using a newly collected dataset which consists of 15 participants, including speech recordings from people with PTSD and healthy controls.

CBAM_SAUNet: A novel attention U-Net for effective segmentation of corner cases.

U-Net has been demonstrated to be effective for the task of medical image segmentation. Additionally, integrating attention mechanism into U-Net has been shown to yield significant benefits. The Shape Attentive U-Net (SAUNet) is one such recently proposed attention U-Net that also focuses on interpretability.

Emotion Recognition from Speech Signals by Mel-Spectrogram and a CNN-RNN.

Speech emotion recognition (SER) in health applications can offer several benefits by providing insights into the emotional well-being of individuals. In this work, we propose a method for SER using time-frequency representation of the speech signals and neural networks. In particular, we divide the speech signals into overlapping segments and transform each segment into a Mel-spectrogram.

Automated Autism Assessment With Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework.

Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents a transformative Robot-Enhanced Therapy (RET) framework, leveraging an intricate amalgamation of an Adaptive Boosted 3D biomarker approach and Saliency Maps generated through Kernel Density Estimation. By seamlessly integrating these methodologies through majority voting, the framework pioneers a new frontier in automating the assessment of ASD levels and Autism Diagnostic Observation Schedule (ADOS) scores, offering unprecedented precision and efficiency.

Automating airborne pollen classification: Identifying and interpreting hard samples for classifiers.

Deep-learning-based classification of pollen grains has been a major driver towards automatic monitoring of airborne pollen. Yet, despite an abundance of available datasets, little effort has been spent to investigate which aspects pose the biggest challenges to the (often black-box- resembling) pollen classification approaches. To shed some light on this issue, we conducted a sample-level difficulty analysis based on the likelihood for one of the largest automatically-generated datasets of pollen grains on microscopy images and investigated the reason for which certain airborne samples and specific pollen taxa pose particular problems to deep learning algorithms.

Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation.

Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.

Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.

A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification.

Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.

Prosody-focused feedback enhances the efficacy of anti-depressive self-statements in depressed individuals - A randomized controlled trial.

This study was aimed to evaluate whether the efficacy of invoking anti-depressive self-statements to cope with depressed mood can be enhanced for depressed individuals by systematically guiding them to amplify the expression of conviction in their voice. Accordingly, we recruited N = 144 participants (48 clinically depressed individuals, 48 sub-clinically depressed individuals, and 48 non-depressed individuals). Participants were randomly assigned to an experimental or control condition.

An On-Board Executable Multi-Feature Transfer-Enhanced Fusion Model for Three-Lead EEG Sensor-Assisted Depression Diagnosis.

The development of affective computing and medical electronic technologies has led to the emergence of Artificial Intelligence (AI)-based methods for the early detection of depression. However, previous studies have often overlooked the necessity for the AI-assisted diagnosis system to be wearable and accessible in practical scenarios for depression recognition. In this work, we present an on-board executable multi-feature transfer-enhanced fusion model for our custom-designed wearable three-lead Electroencephalogram (EEG) sensor, based on EEG data collected from 73 depressed patients and 108 healthy controls.

Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study.

Background: The field of mental health technology presently has significant gaps that need addressing, particularly in the domain of daily monitoring and personalized assessments. Current noninvasive devices such as wristbands and smartphones are capable of collecting a wide range of data, which has not yet been fully used for mental health monitoring.

Objective: This study aims to introduce a novel dataset for personalized daily mental health monitoring and a new macro-micro framework.