EAI Endorsed Transactions on Pervasive Health and Technology
type: channel
description: EAI Endorsed Transactions on Pervasive Health and Technology is open access, a peer-reviewed scholarly journal focused on personal electronic health assistants, health crowdsourcing, data mining, knowledge management, IT applications to the needs of patients, disease prevention, and awareness, electronic and mobile health platforms including design and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2023, the journal follows a continuous publication model. INDEXING: Scopus (CiteScore: 3.6), Compendex, DOAJ, ProQuest, EBSCO, DBLP
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Pervasive Health and Technology
issn: 2411-7145
copyright: This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
items: Expression of Concern Notice: Wearable Sports Smart Glasses Real-time Monitoring and Feedback Mechanism in Physical Education
items: Expression of Concern Notice: Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students
items: Expression of Concern Notice: Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices
items: Expression of Concern Notice: Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration
items: Expression of Concern Notice: Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes
items: Retraction Notice: Intelligent ECG Classification Based on Improved Swin Transformer Model
items: ECG Signal Classification Method Based on Structural Risk Minimization
items: RETRACTED: Intelligent ECG Classification Based on Improved Swin Transformer Model
items: Biomorphic Design of Age-Friendly Wearable Devices: Flexible Circuits and Self-Luminescent Display Interfaces Based on Vascular Bionic Structures
items: Design and Implementation of Intelligent Treatment Plan Recommendation System Based on Big Data of Orthodontic Cases
items: An Interpretable Stacking Ensemble Model with SHAP for Geriatric Depression Prediction: Analysis of the NHANES 2005-2023 Database
items: Enhancing Medical Question-Answering Systems with Knowledge Graph-Integrated Large Language Models: A Comparative Analysis
items: RAG-TPC: Retrieval Augmented Generation for Teenager Psychological Counseling Using DeepSeek
items: Fusion of Radiomics and Deep Learning Features for Enhanced Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer
items: Transformer-based Mobile Health Text Analytics System: Intelligent Symptom Monitoring and Alert for Pervasive Healthcare Environments
items: User-Centered Design of a Wearable Gait Analysis Tool for Knee Endoprosthesis Rehabilitation
items: The Dual-Dimension of Quality of Life: A Philosophical Value Theory Approach to Reconstructing Health Engineering Goals
items: MellowLoop: A Reconfigurable Audio-Light Interaction System for Irritability Mitigation
items: Optimized Temporal Scaffolding: Rhythmic Micro-Variations Selectively Enhance Visual Working Memory Precision
items: Computational Design of Therapeutic Digital Environments: A Deep Learning Approach to Personalized Mental Well-being Intervention
items: Personalized Sleep Microclimate Intervention for Chronic Insomnia: An IoT-Driven Environmental Design and Validation Study
items: From Passive Observation to Active “Deduction”: A Study on the Therapeutic Effects of Directive Embodied Interaction
items: FootSense: An AI-Augmented Foot-Tactile System for Emotion and Social Regulation in Pervasive Health Contexts
items: Gender Differences in Human Trust and Contextual Perception of Robots
items: Easy Assistant: An App Design Study to Alleviate Daily Work Anxiety Among College Faculty
items: The Emotional Healing Mechanisms and Design Principles of Low-Interaction Products in the Digital-Intelligent Era
items: Bio-Aesthetic Resonance: Design and Evaluation of a Physiological Signal-Driven Immersive Art Therapy System for Personalized Stress Reduction
items: https://publications.eai.eu/index.php/phat/article/view/11054
items: The Construction of Therapeutic Space in Emotional Wellness Apps: An Interaction Design Study Based on the Theory of the Production of Space
items: FFIS: Design and Development of a Domestic Fruit Freshness Recognition System

Expression of Concern Notice: Wearable Sports Smart Glasses Real-time Monitoring and Feedback Mechanism in Physical Education
type: item
description: We, the publisher, are issuing an expression of concern about Zhongchen Zhang, Xiaomei Wang (2024) Wearable Sports Smart Glasses Real-time Monitoring and Feedback Mechanism in Physical Education.https://doi.org/10.4108/eetpht.10.5531 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
subject: Expressions of concern
  topic: section
creator: Zhongchen Zhang
creator: Xiaomei Wang
rights: Copyright (c) 2026 Zhongchen Zhang, Xiaomei Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetpht.11.12632

Expression of Concern Notice: Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students
type: item
description: We, the publisher, are issuing an expression of concern about Jing Wang (2024) Research on Intelligent Analysis Method for the Impact of Running APP Software on Physical Fitness Indicators of College Students. https://doi.org/10.4108/eetpht.10.5506 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
subject: Expressions of concern
  topic: section
creator: Jing Wang
rights: Copyright (c) 2026 Jing Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetpht.11.12631

Expression of Concern Notice: Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices
type: item
description: We, the publisher, are issuing an expression of concern about Binbin Han et al. (2024) Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices. https://doi.org/10.4108/eetpht.10.5163 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
subject: Expressions of concern
  topic: section
creator: Bin Bin Han
creator: Fuliang Zhang
creator: Lin Zhao
rights: Copyright (c) 2026 Bin Bin Han, Fuliang Zhang, Lin Zhao https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetpht.11.12630

Expression of Concern Notice: Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration
type: item
description: We, the publisher, are issuing an expression of concern about Wei Zhu (2024) Basketball Anterior and Posterior Portal Veins Doppler Imaging of Sports Medicine Technique Exploration. https://doi.org/10.4108/eetpht.10.5152 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
subject: Expressions of concern
  topic: section
creator: Wei Zhu
rights: Copyright (c) 2026 Wei Zhu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetpht.11.12627

Expression of Concern Notice: Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes
type: item
description: We, the publisher, are issuing an expression of concern about Bin Wu (2024) Real Time Monitoring Research on Rehabilitation Effect of Artificial Intelligence Wearable Equipment on Track and Field Athletes. https://doi.org/10.4108/eetpht.10.5150 There is a concern over the integrity of the peer review review process and suspected manipulation of the editorial process. The investigation is ongoing and we advise scholars to exercise caution when referencing this manuscript.
subject: Expressions of concern
  topic: section
creator: Bin Wu
rights: Copyright (c) 2026 Bin Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-16
publication date: 2026-04-16
doi: 10.4108/eetpht.11.12624

Retraction Notice: Intelligent ECG Classification Based on Improved Swin Transformer Model
type: item
description: We, the Publisher, have retracted the following article: Bin Xu et al. (2025). Intelligent ECG Classification Based on Improved Swin Transformer Model. EAI Endorsed Scal Inf Syst. https://doi.org/10.4108/eetpht.11.11675 The article has been retracted due to authorship dispute. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
subject: Retractions
  topic: section
creator: Bin Xu
creator: Zongzhen Yue
creator: Shoucheng Ji
creator: Ning Sun
creator: Jianyong Zhen
rights: Copyright (c) 2026 Bin Xu, Zongzhen Yue, Shoucheng Ji, Ning Sun, Jianyong Zhen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-26
publication date: 2026-03-26
doi: 10.4108/eetpht.11.12296

ECG Signal Classification Method Based on Structural Risk Minimization
type: item
description: Arrhythmia stands as a primary contributor to cardiovascular disease-associated mortality. Therefore, the classification and monitoring of abnormal electrocardiogram (ECG) signals are of paramount importance for preventive purposes. Although deep - learning - based ECG classification methods have yielded promising outcomes, they frequently encounter challenges in optimizing performance across diverse patient datasets. To overcome these limitations, this research endeavors to enhance the generalization ability of deep - learning models for ECG signal classification. It achieves this by integrating structural risk minimization principles and incorporating RR interval information into the classification process. A convolutional neural network (CNN) founded on structural risk minimization is proposed. Instead of employing the traditional cross-entropy loss, this study adopts a loss function inspired by support vector machine (SVM) classifiers to optimize the CNN. Moreover, the RR interval information, which is often lost during beat segmentation, is manually extracted and integrated into the CNN network to improve classification accuracy. The proposed method attains an accuracy, specificity, and sensitivity of 88.2% respectively, demonstrating superior performance when compared to traditional and existing methods. This improvement underscores the efficacy of the structural risk minimization approach and the integration of RR interval information in enhancing the model's generalization across patient datasets. The method's convenience and effectiveness render it particularly well-suited for real-time application in wearable devices, facilitating the early detection of abnormal ECG patterns and potentially preventing cardiovascular disease-related fatalities.
subject: Research article
  topic: section
subject: classification of ECG signals
  topic: keywords
subject: separating hyperplane
  topic: keywords
subject: structural risk minimization
  topic: keywords
subject: convolutional neural network
  topic: keywords
creator: Feng Hong
creator: Hengyang Fang
creator: Ruru Liu
rights: Copyright (c) 2026 Feng Hong, Hengyang Fang, Ruru Liu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-28
publication date: 2026-01-28
doi: 10.4108/eetpht.11.11676

RETRACTED: Intelligent ECG Classification Based on Improved Swin Transformer Model
type: item
description: RETRACTED: The article has been retracted due to authorship dispute. The retraction notice can be found here: https://doi.org/10.4108/eetpht.11.12296
subject: Research article
  topic: section
subject: ECG
  topic: keywords
subject: Swim Transformer
  topic: keywords
subject: FPN
  topic: keywords
subject: Multi-scale Feature Fusion
  topic: keywords
creator: Bin Xu
creator: Zongzhen Yue
creator: Shoucheng Ji
creator: Ning Sun
creator: Jianyong Zheng
rights: Copyright (c) 2026 Bin Xu, Zongzhen Yue, Shoucheng Ji, Ning Sun, Jianyong Zheng https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-02
publication date: 2026-02-02
doi: 10.4108/eetpht.11.11675

Biomorphic Design of Age-Friendly Wearable Devices: Flexible Circuits and Self-Luminescent Display Interfaces Based on Vascular Bionic Structures
type: item
description: With the accelerating aging of the global population, the importance of health monitoring and assistive technologies for the elderly is becoming increasingly prominent. This study aims to propose and validate a biomorphic design framework based on vascular biomimetic structures to address the challenges of mechanical adaptability, signal stability, and cognitive interaction in age-friendly wearable devices. Using microfluidic lithography and differential capillary self-assembly, a liquid metal flexible circuit with a fractal topology mimicking human microvasculature is constructed on a SEBS/TPU composite substrate. A biomimetic microhinge array is used to disperse stress, and micropits are laser-engraved on the surface to enhance breathability. The display interface, based on a poly(3,4-ethylenedioxythiophene) microcapsule array, combines electrochromic and photoluminescent effects to map parameters such as blood pressure and blood oxygen into dynamic fractal growth patterns. Experimental data show that the biomimetic circuit exhibits a resistance change of only 11.72% under a strain of only 15%, and the substrate stiffness pressure is 0.82±0.07 kPa, ensuring signal stability and comfortable wear during exercise. The user experience is significantly improved, with a low discomfort feedback rate of 7.2%. The conclusion shows that biomorphic design based on vascular bionic structure provides a suitable and systematic solution for the physiological adaptation and cognitive interaction of elderly-friendly wearable devices.
subject: Research article
  topic: section
subject: Vascular Bionics
  topic: keywords
subject: Flexible Circuits
  topic: keywords
subject: Self-Luminous Displays
  topic: keywords
subject: Biomorphic Design
  topic: keywords
subject: Age-Friendly Wearable Devices
  topic: keywords
creator: Yingyu Zhang
rights: Copyright (c) 2026 Yingyu Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-02-02
publication date: 2026-02-02
doi: 10.4108/eetpht.11.11674

Design and Implementation of Intelligent Treatment Plan Recommendation System Based on Big Data of Orthodontic Cases
type: item
description: INTRODUCTION: Contemporary orthodontic treatment planning relies heavily on individual practitioner experience, leading to significant variability in clinical decisions for similar malocclusion presentations and limiting standardized evidence-based care. OBJECTIVES: This research aimed to develop an intelligent treatment recommendation system integrating medical big data analytics with specialized orthodontic knowledge extraction to enhance clinical decision-making accuracy and efficiency. METHODS: The study integrated 1,106 cases from multiple public orthodontic datasets, including ISBI 2015 Grand Challenge, GitHub repositories, PubMed Central case reports, and Kaggle dental imaging competitions. Graph Attention Networks were applied alongside collaborative filtering methods to process these cases and construct orthodontic knowledge graphs that map diagnostic data to treatment outcomes. RESULTS: When tested on extraction decisions, the hybrid system correctly identified treatment needs in 94.2% of cases, while manual evaluation achieved 78.8% accuracy. Processing required only 2.3±0.4 seconds, compared to 35-45 minutes for traditional cephalometric analysis. Different malocclusion categories showed varying results, with Class I cases reaching 96.5% accuracy and Class II Division 2 cases achieving 91.2%. Processing speed improved by 99.8%, sensitivity increased 24.7%, and clinical reliability improved by 28.3% compared to standard diagnostic procedures. CONCLUSION: Big data analytics can enhance orthodontic decision-making while preserving the personalized treatment planning that remains fundamental to achieving optimal treatment outcomes.
subject: Research article
  topic: section
subject: Orthodontic Treatment Planning
  topic: keywords
subject: Medical Big Data Analytics
  topic: keywords
subject: Graph Attention Networks
  topic: keywords
subject: Clinical Decision Support Systems
  topic: keywords
subject: Intelligent Recommendation Systems
  topic: keywords
creator: Yuzhe Huang
creator: Rasheed Abdulsalam
rights: Copyright (c) 2026 Yuzhe Huang, Rasheed Abdulsalam https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetpht.11.11672

An Interpretable Stacking Ensemble Model with SHAP for Geriatric Depression Prediction: Analysis of the NHANES 2005-2023 Database
type: item
description: OBJECTIVE: Leveraging multimodal data from the 2005-2023 National Health and Nutrition Examination Survey (NHANES) database, this study aims to develop a predictive method for the geriatric depression that combines high predictive accuracy with good interpretability, thereby providing support for in-depth exploration of the pathogenesis and risk factors of geriatric depression. METHODS: Data from 8760 participants aged 65 and older in the NHANES database from 2005-2023 are utilized to develop and validate the stacking ensemble predictive model. Depression is assessed using the Patient Health Questionnaire-9 (PHQ-9) total score meeting or exceeding 10. Before the model construction, this work employs the normalization of training data and test data, Synthetic Minority Over-sampling Technique - Random Under-Sampling (SMOTE-RUS) hybrid sampling strategy to address the class imbalance, and the recursive feature elimination method based on the random forest (RFE-RF) for feature selection. A stacking ensemble predictive framework for depression is constructed based on the primary learners (Random Forest, SVM, XGBoost, and Logistic Regression) and meta-learners (SVM and Logistic Regression). Finally, the interpretable machine learning technique SHapley Additive exPlanations (SHAP) is used to visualize the model predictive outputs. RESULTS: The XGBoost model demonstrated outstanding performance on the test set in terms of AUC (83.92%), while the Random Forest (RF) model excelled in sensitivity (71.05%). Subsequently, a specifically designed RFE-Stacking ensemble model, using RF and XGBoost as the primary learner and the SVM as the meta-learner, is developed. In comparison, this stacking ensemble model exhibits the best predictive performance with the biggest AUC (85.14%) and the highest sensitivity (78.71%). The SHAP interpretation reveals that general health condition, frequency of oral pain in the past year, marital status, history of mental health consultations in the pa…
subject: Research article
  topic: section
subject: Geriatric Depression
  topic: keywords
subject: Predictive Model
  topic: keywords
subject: Stacking Ensemble Learning
  topic: keywords
subject: SHAP
  topic: keywords
creator: Manman Cui
creator: Xian Li
creator: Wei Gong
rights: Copyright (c) 2026 Manman Cui, Xian Li, Wei Gong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-28
publication date: 2026-01-28
doi: 10.4108/eetpht.11.11671

Enhancing Medical Question-Answering Systems with Knowledge Graph-Integrated Large Language Models: A Comparative Analysis
type: item
description: This study investigates the impact of integrating knowledge graph prompt engineering (KGPE) with large language models in the context of medical question answering. The Hugging Face MedQA dataset (N = 5,000) was utilised for the extraction of key medical entities via the implementation of named entity recognition, and the construction of SPARQL-based relational prompts from the knowledge base of Wikipedia to guide the reasoning process. Two models, Llama-2-7B-chat-hf and Qwen-2-7B-Instruct, are evaluated through a weighted aggregation of BLEU, ROUGE, and cosine similarity metrics. The findings demonstrate that Qwen-2-7B-Instruct attains substantial enhancements under KGPE—BLEU escalating from 0.366 to 0.531 (+0.165) and cosine similarity rising from 0.763 to 0.820 (+0.057). Conversely, Llama-2-7B-chat-hf exhibits a modest decrease, signifying divergent responsiveness to structured knowledge. These findings demonstrate that integrating structured knowledge through KGPE enhances factual accuracy and semantic coherence in medical reasoning without modifying model architecture
subject: Research article
  topic: section
subject: Knowledge Graph Prompt Engineering
  topic: keywords
subject: Medical QA
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: SPARQL
  topic: keywords
subject: Wikidata
  topic: keywords
creator: Jiaxu Lin
creator: Silin Ouyang
rights: Copyright (c) 2026 Jiaxu Lin, Silin Ouyang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-28
publication date: 2026-01-28
doi: 10.4108/eetpht.11.11670

RAG-TPC: Retrieval Augmented Generation for Teenager Psychological Counseling Using DeepSeek
type: item
description: The rising prevalence of adolescent mental health issues underscores the limitations of traditional counselling services in terms of scalability, timeliness, and accessibility. This paper presents RAG-TPC, a Retrieval- Augmented Generation framework built upon the DeepSeek language model for teenage psychological counselling. The system incorporates intent classification, semantic retrieval, and structured prompt-based generation to produce safe, empathetic, and contextually appropriate responses. We construct a domain- specific dataset spanning general distress, mental illness, and SOS emergencies, and employ LoRA-based fine-tuning to enhance intent recognition. Experimental results show that RAG-TPC consistently outperforms competitive LLMs in both classification and response quality. Evaluations by psychological professionals further validate the system’s practical effectiveness and ethical reliability, highlighting its potential for scalable AI-assisted mental health support.
subject: Research article
  topic: section
subject: Large Language Models
  topic: keywords
subject: Retrieval Augmented Generation
  topic: keywords
subject: DeepSeek
  topic: keywords
subject: Fine-tuning
  topic: keywords
subject: Psychological Counseling
  topic: keywords
creator: Yanling Li
creator: Yibo Gao
creator: Fangying Quan
creator: Xudong Luo
rights: Copyright (c) 2026 Yanling Li, Yibo Gao, Fangying Quan, Xudong Luo https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetpht.11.11669

Fusion of Radiomics and Deep Learning Features for Enhanced Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer
type: item
description: INTRODUCTION: Pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) is a validated surrogate endpoint for long-term survival in breast cancer patients. However, conventional biomarkers exhibit limited predictive accuracy, with approximately 60-80% of patients failing to achieve pCR. Dynamic contrast-enhanced MRI (DCE-MRI) provides high-resolution information on tumor vascularization and heterogeneity, but prior radiomics models have predominantly relied on single-feature paradigms, which may not fully capture complex tumor phenotypes. METHODS: We developed a multimodal deep-learning radiomics (DLR) pipeline using the publicly available ACRIN 6657/I-SPY1 dataset (n=163). After rigorous preprocessing (bias-field correction, isotropic resampling, Z-score normalization), we extracted a comprehensive set of 1,702 standardized radiomics features compliant with the Image Biomarker Standardization Initiative (IBSI), which quantitatively capture tumor morphology, texture, and intensity patterns. Additionally, 8,576 deep learning features were derived from five convolutional neural networks (ResNet50, DenseNet-169, InceptionV3, InceptionResNetV2, EfficientNetB0), enabling the model to learn complex, data-driven representations beyond human-defined features. The fusion of these complementary feature types provides a more holistic characterization of tumor phenotype, significantly enhancing predictive performance compared to single-modality approaches. A two-stage feature-selection strategy utilizing univariate analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was applied, followed by linear signature construction. Ten classifiers were evaluated under stratified cross-validation and independent testing. RESULTS: The fusion of handcrafted radiomics and deep learning features significantly enhanced predictive performance. The best-performing model, a multilayer perceptron (MLP), achieved an area under the receiver operating…
subject: Research article
  topic: section
subject: Breast cancer
  topic: keywords
subject: Radomics
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: DCE-MRI
  topic: keywords
subject: pCR
  topic: keywords
creator: Xupeng Lu
creator: Hazirah Bee bt Yusof Ali
creator: Junxiu Wang
rights: Copyright (c) 2026 Xupeng Lu, Hazirah Bee bt Yusof Ali, Junxiu Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetpht.11.11668

Transformer-based Mobile Health Text Analytics System: Intelligent Symptom Monitoring and Alert for Pervasive Healthcare Environments
type: item
description: Healthcare accessibility challenges disproportionately affect underserved populations, with communication barriers between patients and providers contributing to diagnostic errors and suboptimal outcomes. This study develops and validates a transformer-based lightweight mobile health text analytics system for intelligent symptom monitoring in pervasive healthcare environments. The system employs a DistilBERT-based architecture compressed to 45MB, integrated with medical knowledge graphs incorporating ICD-10 and SNOMED CT standards, and trained on 15,000 medical records from ten hospitals. A three-tier pervasive computing architecture enables cross-platform deployment across iOS, Android, and HarmonyOS, while a four-tier risk stratification framework classifies conditions into self-observation (70%), community consultation (20%), hospital evaluation (8%), and emergency intervention (2%) categories. Privacy preservation utilizes federated learning with differential privacy mechanisms. Clinical effectiveness was evaluated through a randomized controlled trial involving 1,500 participants across diverse demographics. Results demonstrated 86.8% diagnostic concordance versus 70.2% in controls, achieving 93.7% sensitivity and 98.4% specificity for critical symptoms, while reducing emergency department visits by 35.7% and achieving $847 cost savings per patient. Patient experience improvements included 82.7 System Usability Scale scores and 78.4% sustained engagement. This research establishes a paradigm for responsible AI deployment in healthcare that prioritizes clinical effectiveness and social responsibility, contributing to universal health coverage through innovative, accessible, and ethically sound technologies.
subject: Research article
  topic: section
subject: Transformer models
  topic: keywords
subject: Mobile Health
  topic: keywords
subject: Clinical decision support
  topic: keywords
subject: Federated learning
  topic: keywords
subject: Healthcare equity
  topic: keywords
creator: Lei Wang
creator: Simin Cheng
creator: Yajun Liu
creator: Lin Yang
creator: Gang Wang
creator: Yifan Meng
creator: Muxun Ji
rights: Copyright (c) 2026 Lei Wang, Simin Cheng, Yajun Liu, Lin Yang, Gang Wang, Yifan Meng, Muxun Ji https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetpht.11.11667

User-Centered Design of a Wearable Gait Analysis Tool for Knee Endoprosthesis Rehabilitation
type: item
description: With more than 193,000 cases in Germany in 2019, total knee endoprostheses (knee-TEP) are among the 20 most common surgeries performed on hospitalized patients [1]. A standard method for assessing post-operative mobility is the manual measurement of range of motion using a goniometer. This method is subjective, dependent on the clinician’s experience, and provides only a snapshot of the patient’s condition. Existing sensor-based systems for gait analysis are primarily designed for clinical use and research contexts, and are generally not intended for independent application by patients at home. To complement these solutions, we developed a mobile, wearable sensor system and a corresponding app that enables self-administered gait analysis and mobility assessment for patients with knee-TEPs in both clinical and home settings. This paper presents key insights gained from a user-centered design process employing participatory methods, highlighting implications for the development of patient centered rehabilitation technologies.
subject: Technical Article
  topic: section
subject: User-centered design
  topic: keywords
subject: Rehabilitation Tool for Patients with Knee Endoprostheses
  topic: keywords
subject: IMU-Based wearable for Gait Analysis
  topic: keywords
creator: Katharina Lorenz
creator: Hannah Friedrike Fischer
creator: Daniela Wittmann
rights: Copyright (c) 2025 Katharina Lorenz, Hannah Friedrike Fischer, Daniela Wittmann https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-12-18
publication date: 2025-12-18
doi: 10.4108/eetpht.11.11379

The Dual-Dimension of Quality of Life: A Philosophical Value Theory Approach to Reconstructing Health Engineering Goals
type: item
description: Health Engineering (HE) has significantly advanced objective health metrics, yet often overlooks the subjective, value-laden dimension of Quality of Life (QoL), creating a gap in achieving genuine human flourishing. This paper addresses this limitation by introducing a Dual-Dimension QoL Model (DD-QoL), which integrates the Objective Functional Dimension (OFD) with the Subjective Value Dimension (SVD). Based on this model, we develop a Value-Sensitive Assessment Framework (VSAF), a distinctive methodological contribution that uses conceptual engineering to shift HE’s primary goal from merely maximizing OFD to optimizing holistic QoL. Through an illustrative example of an AI-assisted care system for Alzheimer’s disease, we qualitatively demonstrate how a VSAF-guided approach can enhance SVD outcomes without compromising OFD. Our primary contribution is a novel, philosophically-grounded framework that provides a supplementary approach for reconstructing HE goals, ensuring that technological progress serves the broader, more profound aim of promoting valuable life experiences
subject: Art Therapy and Health Engineering
  topic: section
subject: Quality of Life (QoL)
  topic: keywords
subject: Health Engineering
  topic: keywords
subject: Philosophical Value Theory
  topic: keywords
subject: Goal Reconstruction
  topic: keywords
subject: Conceptual Engineering
  topic: keywords
subject: Value-Sensitive Design
  topic: keywords
creator: Jack Shirui Wen
creator: Xuesong Li
creator: Qiurui Wang
rights: Copyright (c) 2026 Jack Shirui Wen, Xuesong Li, Qiurui Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-13
publication date: 2026-01-13
doi: 10.4108/eetpht.11.11062

MellowLoop: A Reconfigurable Audio-Light Interaction System for Irritability Mitigation
type: item
description: INTRODUCTION: Irritability is common in daily life and cuts across diagnoses, yet brief, non-drug options that can be delivered through everyday environments remain scarce. We developed MellowLoop, a reconfigurable audio–light object that synchronizes warm light and gentle sound using clearly defined, comfortable dose ranges. OBJECTIVES: To test whether a single, synchronized audio–light session reduces irritability more than either modality alone or a neutral control; to examine whether subjective changes are mirrored in a coherent physiological pattern; and to assess whether a simple, reconfigurable object can deliver this protocol in a way that is practical for pervasive health deployment. METHODS: We conducted a randomized, parallel, four-arm laboratory study with 60 adults allocated to multimodal audio+light, light-only, audio-only, or a neutral control scene. Each visit followed a fixed 5-minute baseline, 20-minute exposure, and 5-minute post-rest sequence. For active arms, sound was held at 55–60 dB(A) at the ear and light at 200–300 lux of warm (3000–3500 K) illuminance at the eye, guided by equal-loudness contours and melanopic references. Self-reported irritability, valence, and calmness were collected pre- and post-session, alongside skin conductance level, perfusion index, oxygen saturation, and heart rate. Outcomes were analyzed in a prespecified mixed-effects framework with planned contrasts between each active arm and control. RESULTS: The multimodal session produced the largest reduction in irritability, with smaller but still meaningful decreases in the two single-modality arms and minimal change in control. In parallel, valence and calmness increased most in the multimodal arm. Physiological signals showed a convergent pattern: skin conductance level decreased and perfusion index increased most under multimodal stimulation, heart rate declined modestly across active arms, and oxygen saturation remained high and stable. Target exposure levels were a…
subject: Digital Health and Product Innovation Design
  topic: section
subject: audio-light interaction
  topic: keywords
subject: irritability
  topic: keywords
subject: ambient intervention
  topic: keywords
subject: equal-loudness
  topic: keywords
subject: perfusion index
  topic: keywords
subject: skin conductance level (SCL)
  topic: keywords
subject: pervasive health
  topic: keywords
creator: Junpeng Zheng
creator: Yousheng Yao
creator: Weibin Xu
creator: Junqi Zhan
creator: Lingna Tang
creator: Shihua Quan
rights: Copyright (c) 2026 Junpeng Zheng, Yousheng Yao, Weibin Xu, Junqi Zhan, Lingna Tang, Shihua Quan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-20
publication date: 2026-01-20
doi: 10.4108/eetpht.11.11064

Optimized Temporal Scaffolding: Rhythmic Micro-Variations Selectively Enhance Visual Working Memory Precision
type: item
description: Visual working memory (VWM) precision—the fidelity of stored visual representations—is a critical determinant of cognitive performance, yet the influence of subtle, non-semantic auditory features remains largely unexplored. While the effects of gross musical features on cognition are relatively well documented, the role of expressive micro-variations—minute, human-like deviations in rhythm—in shaping VWM has received little empirical attention. We employed a controlled, within-subjects color change-detection paradigm (N = 100) to measure VWM capacity (K) and precision (σ). Participants performed the task under three auditory conditions: Silence, a Mechanically Isochronous Rhythm (ME), and a Micro-Variation Rhythm (MV) consisting of quasi-isochronous drum patterns at 240 BPM with ±20 ms timing variation. VWM metrics were estimated using a standard mixture-model analysis and evaluated with Bayesian multilevel regression. Relative to both Silence and the Mechanical Rhythm, the MV condition produced a robust enhancement of VWM precision (i.e., smaller σ; e.g., β_MV−SI = −7.0, P_post(β < 0) > 0.99), while VWM capacity (K) remained statistically stable across conditions. An exploratory analysis further suggested that the magnitude of the precision benefit was positively associated with participants’ level of musical training. These findings are consistent with the idea that the natural, non-linear temporal structure embedded in expressive rhythm can serve as an Optimized Temporal Scaffold for visual cognition, providing a more effective acoustic context than perfect isochrony for supporting high-fidelity VWM representations. The work bridges research on musical expressivity and fundamental cognitive resource allocation and points to novel theoretical insights and potential applications for designing acoustic environments that support, rather than constrain, cognitive performance
subject: Art Therapy and Health Engineering
  topic: section
subject: Visual Working Memory
  topic: keywords
subject: Rhytmic Micro-Variations
  topic: keywords
subject: Temporal Scaffolding
  topic: keywords
subject: Cognitive Precision
  topic: keywords
subject: Musical Expressivity
  topic: keywords
subject: Cross-Modal Interaction
  topic: keywords
creator: Xingbo Chen
creator: Melinda Xiaoxiao Ying
creator: Kaiwen Xiao
creator: Qiurui Wang
rights: Copyright (c) 2026 Xingbo Chen, Melinda Xiaoxiao Ying, Kaiwen Xiao, Qiurui Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-12
publication date: 2026-01-12
doi: 10.4108/eetpht.11.11068

Computational Design of Therapeutic Digital Environments: A Deep Learning Approach to Personalized Mental Well-being Intervention
type: item
description: INTRODUCTION: The global rise of mental health challenges underscores the urgent need for personalized and non-pharmacological interventions. However, current digital mental health tools often depend on generalized design principles that overlook individual differences in aesthetic preference and affective response. This gap limits long-term engagement and reduces the effectiveness of affective regulation. To overcome these constraints, this study explores the integration of art-psychology principles with advanced machine learning techniques to create adaptive therapeutic environments. OBJECTIVES: The objective of this paper is to develop and evaluate a novel Computational Design Framework (CDF) capable of generating personalized Therapeutic Digital Environments (TDEs) through real-time affective feedback, thereby improving both user experience and therapeutic efficacy. METHODS: The proposed framework combines deep learning–based aesthetic generation with dynamic environment optimization. A Generative Adversarial Network (GAN) is used to produce personalized visual and auditory stimuli, while a Physiological Signal Processing (PSP) module analyzes real-time biosignals—including heart rate variability and skin conductance—to infer users’ affective states. A Deep Reinforcement Learning (DRL) model then adjusts TDE parameters based on both physiological and self-reported feedback. A controlled experiment involving 50 participants was conducted to evaluate the framework against static, generalized TDEs. RESULTS: The DRL-optimized TDEs achieved a 25.3% greater reduction in physiological stress markers compared to static TDEs and yielded higher user satisfaction. Analysis revealed key design parameters—such as specific ranges of color saturation and sound frequency bands—that consistently correlated with positive affective shifts. The findings indicate the framework’s capability to identify and personalize aesthetic variables that influence emotional regulation within the…
subject: Digital Health and Product Innovation Design
  topic: section
subject: Computational Design
  topic: keywords
subject: Mental Health
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Affective Computing
  topic: keywords
subject: Therapeutic Digital Environment
  topic: keywords
creator: Ziyu Xu
creator: Guanghui Huang
rights: Copyright (c) 2026 Ziyu Xu, Guanghui Huang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-20
publication date: 2026-01-20
doi: 10.4108/eetpht.11.11067

Personalized Sleep Microclimate Intervention for Chronic Insomnia: An IoT-Driven Environmental Design and Validation Study
type: item
description: INTRODUCTION: Chronic Insomnia Disorder (CID) is a common public health concern associated with physiological hyperarousal and impaired thermoregulation during sleep onset. Existing pharmacological and psychological treatments often overlook the importance of the sleep environment in facilitating physiological readiness for sleep. OBJECTIVES: This study aims to evaluate the efficacy of a Personalized Microclimate Control (PMC) system—an IoT-driven, adaptive bedroom environmental intervention—in improving insomnia symptoms and objective sleep outcomes in individuals with CID. METHODS: A randomized, controlled, parallel-group trial was conducted with 120 participants diagnosed with CID. Participants were assigned to either the PMC intervention group or a Standard Bedroom Environment (SBE) control group for 8 weeks. Real-time physiological data (skin temperature, HRV) were used to automatically adjust environmental parameters (temperature, light). Primary outcomes included Insomnia Severity Index (ISI) scores and objective Total Sleep Time (TST); secondary outcomes included Skin Temperature Drop Rate (STDR) and its correlation with symptom improvement. RESULTS: The PMC group demonstrated significantly greater improvements across all outcomes compared with the SBE group. ISI scores decreased by 7.47 points in the PMC group versus 2.28 points in the SBE group. TST increased by 43.65 minutes compared to 12.73 minutes in the control group. The PMC system also produced a significantly higher STDR, which was strongly correlated with ISI reduction (r = –0.712, p < 0.001). CONCLUSION: The findings provide strong evidence that IoT-based personalized environmental design is an effective and scalable non-pharmacological intervention for CID. By integrating smart technology, sleep medicine, and environmental science, the PMC system provides a user-centric and mechanistically supported approach to improving sleep in individuals with insomnia.
subject: Art Therapy and Health Engineering
  topic: section
subject: IoT
  topic: keywords
subject: Environmental Design
  topic: keywords
subject: Personalized Medicine
  topic: keywords
subject: Thermoregulation
  topic: keywords
subject: Sleep Microclimate
  topic: keywords
subject: Chronic Insomnia.
  topic: keywords
creator: Yuan Gao
creator: Beiqi luo
creator: Fangtian Ying
rights: Copyright (c) 2026 Yuan Gao, Beiqi luo, Fangtian Ying https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-15
publication date: 2026-01-15
doi: 10.4108/eetpht.11.11066

From Passive Observation to Active “Deduction”: A Study on the Therapeutic Effects of Directive Embodied Interaction
type: item
description: INTRODUCTION: Current art therapy practices struggle with disembodiment, characterized by "sensory deprivation" and "superficial interaction." A key challenge is the limited understanding of the mechanisms through which directive embodied interactions influence psychological states. OBJECTIVES: This study aims to systematically investigate the therapeutic effects and underlying psychological mechanisms of “directive embodied interaction” art installations, exemplified by “do it,” through the construction of a theoretical analytical model and the conduct of empirical experiments. METHODS: This study constructed an “input-process-output” healing model using a mixed experimental design that combined between-group comparisons (experimental vs. control group) with within-group pre- and post-tests. Quantitative assessments were conducted on 110 participants using the Positive and Negative Affect Schedule (PANAS), Flow State Scale (FSS), and Perceived Stress Scale (PSS). A structural equation model was constructed based on questionnaire data to empirically test pathways within the healing mechanism analysis. Supplementary qualitative analysis was conducted through semi-structured interviews. RESULTS: Research findings indicate: 1) Compared to passive viewing, directive embodied interaction significantly enhances positive emotions, reduces negative emotions, and induces higher levels of immersion and stress relief; 2) This emotional regulation effect exhibits specificity, primarily elevating high-arousal positive emotions (e.g., excitement) while alleviating high-arousal negative emotions (e.g., tension); 3) Its therapeutic mechanism stems from a synergistic pathway: instructions reduce cognitive load → action feedback triggers flow → action metaphors facilitate catharsis → group synchronization provides support. CONCLUSION: This study empirically validates the effectiveness of “directive embodied interaction” as an efficient, low-threshold approach to art therapy, offering…
subject: Art Therapy and Health Engineering
  topic: section
subject: Art therapy
  topic: keywords
subject: directive embodied interaction
  topic: keywords
subject: gamified interactive art
  topic: keywords
subject: flow
  topic: keywords
subject: micro-social connections
  topic: keywords
creator: Beihan Yang
creator: Hui Chen
rights: Copyright (c) 2026 Beihan Yang, Hui Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-19
publication date: 2026-01-19
doi: 10.4108/eetpht.11.11060

FootSense: An AI-Augmented Foot-Tactile System for Emotion and Social Regulation in Pervasive Health Contexts
type: item
description: INTRODUCTION: FootSense proposes a novel approach to emotional and social regulation through foot-tactile feedback in public spaces. Unlike conventional upper-body haptic systems, it utilizes the feet as a discreet, low-interference interface. By integrating rhythmic, directional, and social-cue tactile stimulation, FootSense modulates emotional states and enhances social interactions in dynamic environments. OBJECTIVES: This study aims to develop and validate a multi-mechanism foot-tactile model that facilitates emotional relief and social approach in real-world public settings. METHODS: We developed FootSense, an AI-augmented ambient intelligence system combining behavioral sensing, contextual inference, and adaptive tactile feedback. A two-week field experiment (N=200, five groups) was conducted across four public environments—mall, campus, hospital, and transit hub—to compare rhythmic, directional, and fusion tactile modes. Data were analyzed via ANOVA, mixed-effects modeling, and correlation analysis. RESULTS: Rhythmic feedback reduced state anxiety (ΔSAI = –7.5, p<.01), directional feedback increased social approach (+83% vs. control, p<.01), and fusion mode showed the strongest overall effects (ΔSAI = –9.3, p<.001; +121% approach frequency). Tactile activation frequency correlated with improvements (r=.46–.51, p<.05). Environmental factors (noise, crowd density) moderated outcomes, with greater benefits in high-stress settings. CONCLUSION: Embodied, AI-driven foot-tactile feedback offers an effective low-intrusion intervention for emotion regulation and social engagement across diverse public contexts. This work provides a theoretical and practical foundation for integrating AI-augmented haptics into pervasive health and human-centered urban design.
subject: Digital Health and Product Innovation Design
  topic: section
subject: foot-tactile interaction
  topic: keywords
subject: AI-augmented ambient intelligence
  topic: keywords
subject: emotional regulation
  topic: keywords
subject: social approach behavior
  topic: keywords
subject: multi-mechanism model
  topic: keywords
subject: wearable haptic system
  topic: keywords
subject: digital health intervention
  topic: keywords
creator: Zhulin Shi
creator: Hao Zhou
creator: Liuqing Chen
creator: Yao Chen
creator: Guanghui Huang
creator: Weiqiang Ying
rights: Copyright (c) 2026 Zhulin Shi, Hao Zhou, Liuqing Chen, Yao Chen, Guanghui Huang, Weiqiang Ying https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-14
publication date: 2026-01-14
doi: 10.4108/eetpht.11.11061

Gender Differences in Human Trust and Contextual Perception of Robots
type: item
description: INTRODUCTION: Trust is a prerequisite for safe and effective Human–Robot Interaction (HRI), yet reported gender differences are inconsistent and likely contingent on context and socio-perceptual processes. OBJECTIVES: Within a unified framework spanning four canonical HRI contexts (healthcare, education, manufacturing, security), we test whether (a) gender predicts trust, (b) context moderates gender effects, and (c) perceived warmth and perceived threat mediate gender–trust relations. METHODS: A vignette-based experiment with adults (N = 132; male/female) measured affective and cognitive trust, perceived warmth, and perceived threat on 7-point scales. Analyses followed a preregistered plan: 2×4 mixed ANOVAs (Gender × Context) and parallel mediation (PROCESS Model 4; 5,000 bootstrap resamples) with covariates (age, education, prior HRI experience). RESULTS: Gender showed a significant main effect for affective trust (females > males), but not for cognitive trust. Context effects were significant for both trust facets. Gender × Context interactions emerged: the female advantage in affective trust was concentrated in healthcare, while males reported higher cognitive trust in education and manufacturing. Mediation indicated that females’ higher perceived warmth and lower perceived threat jointly accounted for gender differences in overall trust; the direct gender effect was not significant after including mediators. Robustness checks (ANCOVAs; order effects) supported all primary findings. CONCLUSION: Gender differences in robot trust are context-dependent and arise via warmth-enhancing and threat-reducing socio-perceptual pathways. Design should emphasize empathy/assurance cues in caring roles and competence/reliability cues in task/authority roles, alongside systematic threat mitigation.
subject: Art Therapy and Health Engineering
  topic: section
subject: Gender Differences
  topic: keywords
subject: Robot Trust
  topic: keywords
subject: Perceived Warmth
  topic: keywords
subject: Perceived Threat
  topic: keywords
subject: Vignette-Based Experiment
  topic: keywords
subject: Human-Robot Interaction
  topic: keywords
creator: Yang Rao
creator: Ruquan Yang
creator: Shuangyi He
creator: Wenyi Lin
creator: Ruizhao Cai
rights: Copyright (c) 2026 Yang Rao, Ruquan Yang, Shuangyi He, Wenyi Lin, Ruizhao Cai https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-19
publication date: 2026-01-19
doi: 10.4108/eetpht.11.11058

Easy Assistant: An App Design Study to Alleviate Daily Work Anxiety Among College Faculty
type: item
description: INTRODUCTION: Faculty juggle teaching, research, administration, and student support amid unstructured email, chat, and LMS alerts. These demands are linked to increased extraneous cognitive load and are associated with work anxiety. We evaluate a CLT-guided NLP app that structures messages into tasks. OBJECTIVES: Assess technical performance and preliminary pre–post changes in anxiety and workload. METHODS: Stage I validated a four-stage pipeline (cleaning; intent/entity recognition; probability calibration (evaluated by ECE); task structuring with priority gating). Stage II was a 4-week single-group pre–post feasibility study (N = 30) using GAD-7 and NASA-TLX. RESULTS: Intent F1 = 0.92; entity F1 = 0.89; 87% of tasks met TC ≥ 0.8. GAD-7 decreased by 2.7 points (95% CI [−3.6, −1.8]) and NASA-TLX by 16.3 (95% CI [−20.5, −12.1]); usability was high (SUS = 78.2). CONCLUSION: We observed pre–post reductions in workload and anxiety after four weeks of use; causal inference awaits controlled trials.
subject: Digital Health and Product Innovation Design
  topic: section
subject: Cognitive Load Theory
  topic: keywords
subject: Natural language Processing
  topic: keywords
subject: Occupational mental health
  topic: keywords
subject: Work anxiety
  topic: keywords
subject: Cognitive load
  topic: keywords
subject: mHealth
  topic: keywords
creator: Jiao Jiang
creator: Haoteng Chen
creator: Yousheng Yao
creator: Hao Gu
creator: Junfeng Chen
creator: Yesheng Hong
rights: Copyright (c) 2026 Jiao Jiang, Haoteng Chen, Yousheng Yao, Hao Gu, Junfeng Chen, Yesheng Hong https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-15
publication date: 2026-01-15
doi: 10.4108/eetpht.11.11052

The Emotional Healing Mechanisms and Design Principles of Low-Interaction Products in the Digital-Intelligent Era
type: item
description: INTRODUCTION: While the highly efficient interaction models of digital-intelligent technology bring convenience, they have also triggered widespread social alienation and psychological burnout, highlighting the necessity for critical examination and reshaping of digital spaces. OBJECTIVES: This paper aims to employ Lefebvre's theory of the production of space as a framework to systematically analyze the emotional healing mechanisms of low-interaction products as a "restorative micro-space," clarifying the intrinsic pathways through which their "production" process influences user psychology and emotions. METHODS: The study constructs a chained mediation model encompassing the three dimensions of spatial production (12 design elements), basic psychological needs (autonomy, competence, relatedness), and emotional healing. Through the development of low-interaction product design practices and the establishment of a mainstream calendar application as a reference group, the theoretical model was empirically examined using structural equation modeling (SEM) and multi-group analysis. RESULTS: Empirical results strongly support the theoretical model, indicating that the core advantage of low-interaction products lies in their "non-performative" and "nurturing" design. These elements effectively restore users' sense of autonomy and relatedness, thereby achieving significant emotional healing effects CONCLUSION: This research provides empirically grounded theoretical guidance for design practices focused on digital mental health, validating the effectiveness and superiority of using the theory of spatial production to guide the construction of a "restorative micro-space."
subject: Art Therapy and Health Engineering
  topic: section
subject: Theory of Spatial Production
  topic: keywords
subject: Low-Interaction Products
  topic: keywords
subject: Emotional Healing
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
subject: Restorative Micro-Space
  topic: keywords
creator: Congyao Xu
creator: Yuchun Zhang
creator: Yi Tang
rights: Copyright (c) 2026 Congyao Xu, Yuchun Zhang, Yi Tang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-08
publication date: 2026-01-08
doi: 10.4108/eetpht.11.11057

Bio-Aesthetic Resonance: Design and Evaluation of a Physiological Signal-Driven Immersive Art Therapy System for Personalized Stress Reduction
type: item
description: The escalating prevalence of chronic stress necessitates development of highly personalized non-pharmacological interventions. Traditional art therapy lacks real-time adaptability to match individuals' fluctuating physiological states. This paper introduces the Bio-Aesthetic Resonator (BAR), a novel closed-loop immersive art therapy system driven by real-time physiological feedback. The BAR integrates biosensors for continuous monitoring of Heart Rate Variability (HRV) and Galvanic Skin Response (GSR), utilizing deep reinforcement learning (DRL) to dynamically generate immersive visual and auditory artscapes. We conducted a pilot randomized controlled trial (pRCT) with 60 participants with mild-moderate anxiety. The BAR intervention significantly reduced perceived stress scores (PSS-10: Cohen's d = 0.65, 95% CI [0.22, 1.08], p = 0.003) and increased high-frequency HRV (HF-HRV: Cohen's d = 0.92, 95% CI [0.48, 1.36], p < 0.001) compared to a sham-adaptive control. These results support bio-aesthetic resonance as a viable framework for personalized digital therapeutics.
subject: Art Therapy and Health Engineering
  topic: section
subject: Immersive Art Therapy
  topic: keywords
subject: Physiological Feedback
  topic: keywords
subject: Personalized Intervention
  topic: keywords
subject: Heart Rate Variability
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
creator: Dongjun Wu
creator: Xiaoli Shen
creator: Qianzi You
creator: Jiangping Yang
creator: Lingyan Zhang
rights: Copyright (c) 2026 Dongjun Wu, Xiaoli Shen, Qianzi You, Jiangping Yang, Lingyan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetpht.11.11055

https://publications.eai.eu/index.php/phat/article/view/11054
type: item
title: A Three-Layer Cognitive Structure Theory of “Soma-Emotion-Meaning” Based on Embodied Cognition: Integrating Somatic Marker Hypothesis and Progressive Weakening-Compensation Principle — An Integrative Framework for Understanding Autism Spectrum Disorder and Depression Comorbidity
description: The comorbidity rate between Autism Spectrum Disorder (ASD) and depression reaches 50%, yet there is a lack of an integrated theoretical framework to guide digital health interventions. Existing research on sensory processing, emotion regulation, and meaning-making is fragmented, which limits the development of technology-enabled treatment solutions. This study integrates embodied cognition theory, the somatic marker hypothesis, and the principle of decreasing entropy and compensation, proposing a three-layer cognitive structure theory of "sensory awareness - emotional awareness - sense of meaning". This framework reveals that the comorbidity of ASD and depression stems from impairments in the three-layer cognitive pathway. Art therapy operates through dual mechanisms: sensory integration activates bottom-up neuroplasticity, while narrative reconstruction achieves top-down cognitive reappraisal. This theoretical model provides clear guidelines for the development of intelligent assessment tools, adaptive intervention algorithms, and data-driven personalized strategies in mobile health applications.
subject: Art Therapy and Health Engineering
  topic: section
subject: Art therapy , Autism Spectrum Disorder, Depression, Embodied cognition, Somatic marker hypothesis, Progressive weakening and compensation principle, Soma-emotion-meaning Theoretical framework
  topic: keywords
creator: Tianran Zhang
creator: Zixun Wang
creator: Xusheng Zhang
rights: Copyright (c) 2026 Tianran Zhang, Zixun Wang, Xusheng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-27
publication date: 2026-01-27
doi: 10.4108/eetpht.11.11054

The Construction of Therapeutic Space in Emotional Wellness Apps: An Interaction Design Study Based on the Theory of the Production of Space
type: item
description: INTRODUCTION: In response to the global shortage of mental health resources, the design paradigm of emotional wellness apps is shifting from “functional tools” to “immersive spaces.” However, existing research lacks a systematic framework to analyze the therapeutic mechanisms of “spatiality” in such applications. OBJECTIVES: Based on Henri Lefebvre’s theory of the production of space, this study employs an exploratory sequential mixed-methods approach to construct and preliminarily validate a model of digital therapeutic space in emotional wellness apps for empirical analysis. METHODS: The study first conducted semi-structured interviews with 24 intensive users of three representative apps, “Chao Xi,” “MoFlow,” and “Xin Guang.” Grounded theory was used for three-level coding to extract core categories and construct a theoretical model. Subsequently, based on qualitative coding results, the Spatial Narrative Experience Assessment Scale was developed and administered to 286 users. Descriptive statistics, correlation analysis, multiple linear regression, and Bootstrap methods were used to test model pathways, controlling for common method bias. RESULTS: The results show: (1) The therapeutic space of emotional wellness apps is a systematic model comprising three main categories (physical space, social space, and spiritual space) and nine subcategories. (2) Qualitative analysis revealed a carrier-rule-output spatial logic. (3) After controlling for usage duration and mental health baseline, physical space (β = 0.29, p < 0.001) and social space (β = 0.38, p < 0.001) significantly positively influenced spiritual space experience. Bootstrap tests indicated no significant difference in their effects (95% CI [-0.03, 0.21]), supporting a “Dual Engine Drive” mechanism. Common method bias was acceptable (first-factor explanation rate: 32.7%). CONCLUSION: This study validates the operationalization of the theory of the production of space in interaction design research, providing…
subject: Digital Health and Product Innovation Design
  topic: section
subject: Spatial Narrative
  topic: keywords
subject: Emotional Health Apps
  topic: keywords
subject: Interaction Design
  topic: keywords
subject: Healing Space
  topic: keywords
creator: Qi Zhang
creator: Buyu Xu
creator: Lin Zhu
creator: Yichen Wu
rights: Copyright (c) 2026 Qi Zhang, Buyu Xu, Lin Zhu, Yichen Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-08
publication date: 2026-01-08
doi: 10.4108/eetpht.11.11050

FFIS: Design and Development of a Domestic Fruit Freshness Recognition System
type: item
description: INTRODUCTION: In everyday households, fruits are often stored for too long, forgotten, or discarded early due to uncertainty about their freshness, leading to avoidable waste, unnecessary cost, and suboptimal dietary habits. A reliable and user-centered fresh produce assessment tool can help people establish a healthier and more sustainable lifestyle. The existing freshness assessment systems are mainly designed for industrial or laboratory environments and rarely meet the privacy, stability and cost requirements of family digital health products. OBJECTIVES: This study aims to design and evaluate an edge-AI system that jointly recognizes fruit category and ordinal freshness stage in real kitchens, providing a reproducible benchmark for household-oriented freshness sensing rather than a one-off engineering prototype. METHODS: We develop the Fruit Freshness Identification System (FFIS), a lightweight multi-task detector built on YOLO11n with a BiFPN neck, ACmix hybrid attention, and an IoU-based localization loss reweighted for partial occlusion. A kitchen-scene dataset is collected and stratified by household, countertop material, lighting, and clutter. The system is trained with an ordinal regression head for freshness staging and evaluated on COCO-style detection metrics, stage-wise classification metrics, and edge-device throughput and energy consumption. RESULTS: On the FFIS-Fruit test split, FFIS achieves an mAP@0.5:0.95 of 62.4 ± 0.4, mAP@0.5 of 94.1 ± 0.3, and recall of 90.8 ± 0.3, outperforming lightweight YOLO baselines as well as a two-stage detector-plus-classifier pipeline. Warm-LED and glossy-countertop subsets show consistent gains in high-reflection scenarios. On-device experiments reach ~55 FPS (INT8) on Jetson Orin Nano and ~10 FPS on Raspberry Pi 5 under a unified evaluation protocol. CONCLUSION: FFIS provides a household-oriented, privacy-preserving reference implementation for kitchen fruit monitoring, demonstrating that single-pass multi-task de…
subject: Digital Health and Product Innovation Design
  topic: section
subject: Digital health
  topic: keywords
subject: Edge AI
  topic: keywords
subject: Fruit freshness detection
  topic: keywords
subject: Multi-task learning
  topic: keywords
subject: On-device inference
  topic: keywords
subject: Energy efficiency
  topic: keywords
creator: Xiayu Liu
creator: Zhu Ke
creator: Yuanhao Ding
creator: Yingshan Tang
creator: Yuqiang Song
rights: Copyright (c) 2026 Xiayu Liu, Zhu Ke, Yuanhao Ding, Yingshan Tang, Yuqian Song https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-16
publication date: 2026-01-16
doi: 10.4108/eetpht.11.11042