Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

AI-powered Digital Therapeutics (DTx) hold potential for enhancing stress prevention by promoting the scalability of P5 Medicine, which may offer users coping skills and improved self-management of mental wellbeing. However, adoption rates remain low, often due to insufficient user and stakeholder involvement during the design phases. This study explores the human-centered design potentials of SHIVA, a DTx integrating virtual reality and AI with the SelfHelp+ intervention, aiming to understand stakeholder views and expectations that could influence its adoption. Using the SHIVA example, we detail design opportunities involving AI techniques for stress prevention across modeling, personalization, monitoring, and simulation dimensions. Workshops with 12 stakeholders—including target users, digital health designers, and mental health experts—addressed four key adoption aspects through peer interviews: AI data processing, wearable device roles, deployment scenarios, and the model's transparency, explainability, and accuracy. Stakeholders perceived AI-based data processing as beneficial for personalized treatment in a secure, privacy-preserving environment. While wearables were deemed essential, concerns about compulsory use and VR headset costs were noted. Initial human facilitation was favored to enhance engagement and prevent dropouts. Transparency, explainability, and accuracy were highlighted as crucial for the stress detection model. Stakeholders recognized AI-driven opportunities as crucial for SHIVA's adoption, facilitating personalized solutions tailored to user needs. Nonetheless, challenges persist in developing a transparent, explainable, and accurate stress detection model to ensure user engagement, adherence, and trust.

Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, and memory retention. This study aims to design a robust and comprehensive evaluation scale, the Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC), using the eDelphi method to address this gap. Methods: A panel of 16 experts in psychology, artificial intelligence, human-computer interaction, and digital therapeutics participated in two iterative eDelphi rounds. The process focused on refining dimensions and items based on qualitative and quantitative feedback. Initial validation, conducted after assembling the final version of the scale, involved 49 participants using the CES-LCC to evaluate an LLM-powered chatbot delivering Self-Help Plus (SH+), an Acceptance and Commitment Therapy-based intervention for stress management. Results: The final version of the CES-LCC features 27 items grouped into nine dimensions: Understanding Requests, Providing Helpful Information, Clarity and Relevance of Responses, Language Quality, Trust, Emotional Support, Guidance and Direction, Memory, and Overall Satisfaction. Initial real-world validation revealed high internal consistency (Cronbach’s alpha = 0.94), although minor adjustments are required for specific dimensions, such as Clarity and Relevance of Responses. Conclusions: The CES-LCC fills a critical gap in the evaluation of LLM-powered counseling chatbots, offering a standardized tool for assessing their multifaceted capabilities. While preliminary results are promising, further research is needed to validate the scale across diverse populations and settings.

The widespread adoption of wearable health monitoring devices has highlighted the need for robust algorithms to detect and mitigate motion artefacts in photoplethysmographic (PPG) signals. This paper introduces a novel approach to motion artifact segmentation, supported by the creation of a new dataset. PPG signals from publicly available repositories were curated and enhanced with precise segmentation masks, enabling rigorous model training and evaluation. Three deep learning architectures —UNet, LSTM-UNet, and Atrous-UNet— were assessed using a comprehensive set of metrics, including Dice Score, Intersection over Union, and Average Hausdorff Distance, to highlight the model performance beyond standard evaluations. This multi-metric approach underscored the importance of addressing diverse aspects such as boundary precision and overall robustness. Atrous-UNet emerged as the most effective model, offering a balance of high performance and computational efficiency suitable for real-time deployment. The models were validated on real-world data from open-source wearable devices, EmotiBit and Bangle.js 2 (accuracy above 80% for both), demonstrating generalizability across varying hardware and environmental conditions. Comparisons with expert and non-expert human annotators revealed that the models significantly outperformed the two groups in reliability and detection consistency. The models were optimized using quantization techniques to enable deployment on resource-constrained devices. Although this introduced some performance losses, the models retained robust artefact detection capabilities with an accuracy of over 78%.