EAI Endorsed Transactions on Pervasive Health and Technology
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: 3.7), Compendex, DOAJ, ProQuest, EBSCO, DBLP
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Retraction Notice: Intelligent ECG Classification Based on Improved Swin Transformer Model
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 https://doi.org/10.4108/eetpht.11.11675 The article has been retracted due to authorship dispute.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 The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 Bin Xu, Zongzhen Yue, Shoucheng Ji, Ning Sun, Jianyong Zhen https://creativecommons.org/licenses/by-nc-sa/4.0
ECG Signal Classification Method Based on Structural Risk Minimization
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.
Copyright (c) 2026 Feng Hong, Hengyang Fang, Ruru Liu https://creativecommons.org/licenses/by-nc-sa/4.0
RETRACTED: Intelligent ECG Classification Based on Improved Swin Transformer Model
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
Copyright (c) 2026 Bin Xu, Zongzhen Yue, Shoucheng Ji, Ning Sun, Jianyong Zheng https://creativecommons.org/licenses/by-nc-sa/4.0
Biomorphic Design of Age-Friendly Wearable Devices: Flexible Circuits and Self-Luminescent Display Interfaces Based on Vascular Bionic Structures
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.
Copyright (c) 2026 Yingyu Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Implementation of Intelligent Treatment Plan Recommendation System Based on Big Data of Orthodontic Cases
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.
Copyright (c) 2026 Yuzhe Huang, Rasheed Abdulsalam https://creativecommons.org/licenses/by-nc-sa/4.0
An Interpretable Stacking Ensemble Model with SHAP for Geriatric Depression Prediction: Analysis of the NHANES 2005-2023 Database
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 past year, and frequency of urine leakage are the top five most influential factors in predicting the depression risk. CONCLUSION: This stacking ensemble model enhances the performance of both the primary learners and the meta-learners. This verifies the feasibility and effectiveness of the proposed model in predicting the geriatric depression. This work integrating the stacking ensemble model with SHAP offers valuable clinical references for assessing the risk of depressive symptoms, which is beneficial to develop the personalized depression interventions and preventions in the elderly.
Copyright (c) 2026 Manman Cui, Xian Li, Wei Gong https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Medical Question-Answering Systems with Knowledge Graph-Integrated Large Language Models: A Comparative Analysis
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
Copyright (c) 2026 Jiaxu Lin, Silin Ouyang https://creativecommons.org/licenses/by-nc-sa/4.0
RAG-TPC: Retrieval Augmented Generation for Teenager Psychological Counseling Using DeepSeek
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.
Copyright (c) 2026 Yanling Li, Yibo Gao, Fangying Quan, Xudong Luo https://creativecommons.org/licenses/by-nc-sa/4.0
Fusion of Radiomics and Deep Learning Features for Enhanced Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer
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 characteristic curve (AUC) of 0.98 on the independent test set, with an accuracy of 95.92%, sensitivity of 92.86%, and specificity of 97.14%. Logistic regression also demonstrated strong performance (AUC = 0.980). Decision curve analysis confirmed the clinical utility of all models across a wide range of threshold probabilities. CONCLUSIONS: The integration of radiomics and deep learning features within a machine learning framework provides a robust, non-invasive tool for predicting pCR to NAC in breast cancer. This multimodal approach outperforms single-modality models and offers potential for clinical translation to personalize treatment strategies and avoid ineffective chemotherapy. Further multi-center validation is warranted to confirm its generalizability.
Copyright (c) 2026 Xupeng Lu, Hazirah Bee bt Yusof Ali, Junxiu Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Transformer-based Mobile Health Text Analytics System: Intelligent Symptom Monitoring and Alert for Pervasive Healthcare Environments
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.
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
User-Centered Design of a Wearable Gait Analysis Tool for Knee Endoprosthesis Rehabilitation
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.
Rehabilitation Tool for Patients with Knee Endoprostheses
Copyright (c) 2025 Katharina Lorenz, Hannah Friedrike Fischer, Daniela Wittmann https://creativecommons.org/licenses/by-nc-sa/4.0
The Dual-Dimension of Quality of Life: A Philosophical Value Theory Approach to Reconstructing Health Engineering Goals
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
Copyright (c) 2026 Jack Shirui Wen, Xuesong Li, Qiurui Wang https://creativecommons.org/licenses/by-nc-sa/4.0
MellowLoop: A Reconfigurable Audio-Light Interaction System for Irritability Mitigation
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 achieved with narrow variation, sessions were well tolerated, and no adverse events occurred. CONCLUSION: A single, well-calibrated audio–light session delivered by a reconfigurable everyday object can acutely ease irritability while remaining comfortable and feasible. The combination of a dose-defined protocol, multi-arm randomized design, and low-cost sensing chain offers a practical blueprint for future pervasive health systems that seek to embed irritability support into ordinary rooms and routines.
Digital Health and Product Innovation Design
Copyright (c) 2026 Junpeng Zheng, Yousheng Yao, Weibin Xu, Junqi Zhan, Lingna Tang, Shihua Quan https://creativecommons.org/licenses/by-nc-sa/4.0
Optimized Temporal Scaffolding: Rhythmic Micro-Variations Selectively Enhance Visual Working Memory Precision
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
Copyright (c) 2026 Xingbo Chen, Melinda Xiaoxiao Ying, Kaiwen Xiao, Qiurui Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Computational Design of Therapeutic Digital Environments: A Deep Learning Approach to Personalized Mental Well-being Intervention
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 current experimental scope. CONCLUSION: This research establishes a replicable, data-driven methodology for designing therapeutic interventions that bridge subjective aesthetic experience with objective physiological outcomes. The proposed CDF advances cross-disciplinary innovation at the intersection of art, psychology, and technology, suggesting promising directions for personalized healthcare and computationally driven design practices.
Copyright (c) 2026 Ziyu Xu, Guanghui Huang https://creativecommons.org/licenses/by-nc-sa/4.0
Personalized Sleep Microclimate Intervention for Chronic Insomnia: An IoT-Driven Environmental Design and Validation Study
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.
Copyright (c) 2026 Yuan Gao, Beiqi luo, Fangtian Ying https://creativecommons.org/licenses/by-nc-sa/4.0
From Passive Observation to Active “Deduction”: A Study on the Therapeutic Effects of Directive Embodied Interaction
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 a novel pathway for promoting public mental health and well-being. The findings not only provide critical theoretical foundations and actionable strategies for designing therapeutic installations and spaces but also broaden the conceptual framework for digital art therapy. Furthermore, they deepen our understanding of the pivotal role digital media plays in influencing human perception, cognition, and behavior.
Copyright (c) 2026 Beihan Yang, Hui Chen https://creativecommons.org/licenses/by-nc-sa/4.0
FootSense: An AI-Augmented Foot-Tactile System for Emotion and Social Regulation in Pervasive Health Contexts
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.
Copyright (c) 2026 Zhulin Shi, Hao Zhou, Liuqing Chen, Yao Chen, Guanghui Huang, Weiqiang Ying https://creativecommons.org/licenses/by-nc-sa/4.0
Gender Differences in Human Trust and Contextual Perception of Robots
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.
Copyright (c) 2026 Yang Rao, Ruquan Yang, Shuangyi He, Wenyi Lin, Ruizhao Cai https://creativecommons.org/licenses/by-nc-sa/4.0
Easy Assistant: An App Design Study to Alleviate Daily Work Anxiety Among College Faculty
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.
Copyright (c) 2026 Jiao Jiang, Haoteng Chen, Yousheng Yao, Hao Gu, Junfeng Chen, Yesheng Hong https://creativecommons.org/licenses/by-nc-sa/4.0
The Emotional Healing Mechanisms and Design Principles of Low-Interaction Products in the Digital-Intelligent Era
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."
Copyright (c) 2026 Congyao Xu, Yuchun Zhang, Yi Tang https://creativecommons.org/licenses/by-nc-sa/4.0
Bio-Aesthetic Resonance: Design and Evaluation of a Physiological Signal-Driven Immersive Art Therapy System for Personalized Stress Reduction
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.
Copyright (c) 2026 Dongjun Wu, Xiaoli Shen, Qianzi You, Jiangping Yang, Lingyan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
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
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.
Art therapy , Autism Spectrum Disorder, Depression, Embodied cognition, Somatic marker hypothesis, Progressive weakening and compensation principle, Soma-emotion-meaning Theoretical framework
Copyright (c) 2026 Tianran Zhang, Zixun Wang, Xusheng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
The Construction of Therapeutic Space in Emotional Wellness Apps: An Interaction Design Study Based on the Theory of the Production of Space
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 a systematic theoretical model and empirical basis for the construction and evaluation of “digital therapeutic space.” The derived “carrier-rule-output” framework offers actionable insights for the design of emotionally resonant digital environments.
Copyright (c) 2026 Qi Zhang, Buyu Xu, Lin Zhu, Yichen Wu https://creativecommons.org/licenses/by-nc-sa/4.0
FFIS: Design and Development of a Domestic Fruit Freshness Recognition System
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 detection can meet domestic deployment constraints on low-cost hardware. Rather than directly claiming reductions in household food waste, the findings establish a technical foundation for integrating freshness signals into downstream digital-health workflows such as waste-aware reminders and dietary planning.
Copyright (c) 2026 Xiayu Liu, Zhu Ke, Yuanhao Ding, Yingshan Tang, Yuqian Song https://creativecommons.org/licenses/by-nc-sa/4.0
Active Prevention of Nature-Deficit Disorder in Urban Children: An IoT-Based Interactive Ecological Intervention System Design
INTRODUCTION: Modern urban lifestyles have fundamentally transformed children's growth environments, indirectly contributing to the proliferation of Nature-Deficit Disorder (NDD). This condition has elevated the prevalence of childhood obesity, attention deficit disorders, and mental health issues to epidemic levels. Existing interventions face significant limitations, particularly in their inability to seamlessly integrate into the daily routines of urban children. OBJECTIVES: Grounded in Socio-Ecological Theory, Self-Determination Theory (SDT), and the Technology Acceptance Model(TAM), this study aims to propose and design a novel IoT-based interactive ecological intervention system. The goal is to create a unified framework that effectively bridges the gap between urban living and nature engagement. METHODS: The system incorporates modular hardware design and a gamified task-driven mechanism, organically integrating outdoor exploration with indoor planting. Through user surveys involving 100 urban children aged 6–12, we constructed and applied partial least squares structural equation modeling (PLS-SEM) to evaluate the intervention's effectiveness. This analysis examined the impact paths of perceived ease of use(PEOU)and gamification experience(GAM)on nature connectedness (NC)and pro-nature behavioral intention(BI), while testing the mediating role of gamification experience. Field observations were also conducted with representative users. CONCLUSION: The structural equation modelling (SEM)results indicate strong explanatory power for the model, with R²values for key variables ranging from 0. 396 to 0. 477. The study confirms that gamification experience plays a critical and significant mediating role between perceived ease of use and nature connectedness (indirect effect β=0. 241, p=0. 003). A more noteworthy finding is that the primary driver of children's future pro-nature behavioral intention is gamification experience itself (β=0. 376, p<0. 001), rather than the nature connectedness it fosters. This research thus concludes that, in digital health interventions for children, intrinsic enjoyment derived from gamification experience serves as a more pivotal long-term motivator for sustained engagement than the educational value of nature connectedness. These insights not only validate a promising prototype intervention system but, more importantly, offer essential theoretical and practical guidance for designing more effective child-focused intervention products in the future.
Copyright (c) 2026 Cheng Zhai, HongChen Li, Yan Chen, Yuyang Zeng, Shanshan Liang https://creativecommons.org/licenses/by-nc-sa/4.0
A Three-Arm Randomized Controlled Trial of Cross-Modal Digital Health System Integrated with Cognitive Behavioral Therapy for Insomnia: Neurophysiological Mechanisms and Clinical Efficacy
INTRODUCTION: Insomnia Disorder is a global public health problem. Cognitive behavioral therapy for insomnia (CBT-I), as the gold standard for combating insomnia, still has limitations such as low patient adherence and inability to directly intervene in physiological hyperarousal. Traditional sensory interventions lack precise, mechanism-driven designs, making it difficult to effectively suppress this hyperarousal. This study aims to address these limitations by developing a non-pharmacological intervention based on predictive coding theory (PCT) and multi-sensory integration. OBJECTIVES: This study developed a Cross-Modal Digital Health System (CMDH-I) that combines CBT-I principles with personalized, synchronized auditory, visual, and vibro-tactile stimulation, and dynamically modulates the intervention process through a closed-loop control mechanism driven by real-time heart rate variability (HRV) biofeedback. The primary objectives include evaluating the clinical efficacy of CMDH-I combined with CBT-I on objective sleep latency (SL) and subjective sleep quality (PSQI). Furthermore, the study aims to explore the underlying neurophysiological mechanisms, particularly the regulatory role of heart rate variability (HRV-RMSSD) and changes in electroencephalogram (EEG) power spectral density. METHODS: A 6-week, double-blind, three-arm randomized controlled trial (RCT) was conducted on 90 patients with primary insomnia. Participants were randomly assigned to one of three groups: (1) CBT-I + True CMDH-I; (2) CBT-I + Sham CMDH-I (stimulus asynchrony); and (3) CBT-I standard control group. The primary outcomes were objective sleep latency (SL) and subjective sleep quality (PSQI). Secondary outcomes included neurophysiological parameters: electroencephalogram power spectral density (δ/σ wave) and heart rate variability (HRV-RMSSD). RESULTS: The reduction in SL and PSQI scores in the True CMDH-I group was significantly greater than that in the other two groups, exceeding the lowest clinically significant difference (MCID) (p < 0.001). More importantly, mediation analysis showed that the improvement in HRV-RMSSD was one of the main mechanisms by which CMDH-I improved sleep quality, accounting for 58.1% of the total effect. In addition, the increase in frontal lobe EEG delta wave power was closely associated with the increase in HRV-RMSSD (r=0.68), which validated the hypothesis of the vagus-thalamus-cortex pathway proposed in this study. CONCLUSION: CMDH-I is a closed-loop, cross-modal digital health system based on PCT. As a non-pharmacological intervention for sleep disorders, this system outperforms standard CBT-I in clinical efficacy. The research results provide empirical evidence that the system's therapeutic effect is achieved through enhanced parasympathetic activity (increased HRV-RMSSD), thus validating its precise neurophysiological mechanism of sleep regulation. This study establishes a clearly defined digital treatment system, providing objective physiological indicators for personalized sleep medicine and representing a significant advancement
Copyright (c) 2026 Caihong He, Siyu Ying, Yadong Xu, Fangtian Ying https://creativecommons.org/licenses/by-nc-sa/4.0
The Mapping Relationship Between Emotion and the Laws of Formal Beauty: Construction and Validation
INTRODUCTION: The intrinsic mapping mechanism between emotion and the laws of formal beauty is crucial for emotional design and human-computer interaction. However, existing research lacks a systematic construction and empirical validation of the dynamic, bidirectional mapping relationship between emotional dimensions (valence, arousal) and formal beauty features (symmetry, proportion, rhythm, complexity). OBJECTIVES: This study aims to construct a bidirectional mapping theoretical framework between emotion and the laws of formal beauty, and to empirically validate it through a multi-modal psychophysiological experiment. The goal is to quantify the driving effect of emotional states on formal beauty preferences and the reverse regulatory effect of formal beauty on emotional states. METHODS: A within-subjects experimental design of 2 (valence) × 2 (arousal) × 4 (formal beauty features) was employed, inducing emotions through dual stimuli of images and music. Behavioral preference scores, Galvanic Skin Response (GSR), and Photoplethysmography (PPG) data were synchronously collected from 100 participants. Data analysis was conducted using repeated-measures Analysis of Variance (ANOVA), multiple regression analysis, and Structural Equation Modeling (SEM). RESULTS: A significant main effect of emotional state on graphic preference scores was found (<!-- [if gte msEquation 12]><m:oMath><m:r>F</m:r><m:r>=5.62,</m:r><m:r>p</m:r><m:r>=0.001</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> ). High-valence emotions were significantly associated with a preference for high symmetry (<!-- [if gte msEquation 12]><m:oMath><m:r>β</m:r><m:r>=0.38,</m:r><m:r>p</m:r><m:r>=0.001</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> ) and high proportional harmony. High-arousal emotions were linked to a preference for forms with high rhythm and complexity. Physiological data confirmed a physiological matching mechanism between emotional arousal levels and formal beauty preferences. Furthermore, designs with high symmetry and proportional harmony demonstrated a significant regulatory effect on negative emotions (low valence-high arousal) (<!-- [if gte msEquation 12]><m:oMath><m:r>valence</m:r><m:r> </m:r><m:r>t</m:r><m:r>=4.52,</m:r><m:r>p</m:r><m:r><0.001</m:r></m:oMath><![endif]--> <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 2.5pt; mso-text-raise: -2.5pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> ). CONCLUSION: This study systematically constructs and empirically validates a bidirectional mapping model between emotion and the laws of formal beauty. It reveals the driving mechanism of emotional dimensions on formal beauty preferences and confirms the positive regulatory function of formal beauty on emotional states. The findings provide a precise scientific basis for emotional design, user interface optimization, and art-form-based psychological interventions.
Copyright (c) 2026 Juan Jin, Haiwen Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on a Multimodal Intelligent Dressing System and Digital Age-Friendly Design Based on Natural Behavior Theory
INTRODUCTION: With global population aging, everyday dressing decisions for older adults have evolved from simple garment choices into complex tasks that must simultaneously satisfy health, safety, social etiquette, and subjective comfort constraints. Experience-based and intuitive dressing strategies struggle to cope with rapidly changing weather, fluctuating health conditions, and frequent switches between daily events, often leading to misalignment between clothing choices and healthy aging goals. OBJECTIVES: This study proposes an intelligent dressing recommendation system for older adults based on natural behavior theory. The system adopts a WEHT (Wearable–Event–Health–Thermal environment) situational modeling framework and multimodal data fusion to provide safe, comfortable, socially appropriate, and easy-to-understand dressing recommendations within a digital age-friendly design context. METHODS: The system constructs an elderly-oriented clothing knowledge graph and functional matrix, and fuses multimodal inputs including weather indices, individual health constraints, and calendar events. A cross-modal attention mechanism with adaptive weights is introduced to capture direct and indirect couplings among clothing, events, health status, and thermal environment. Under a predefined hierarchical decision rule that prioritizes health and safety, the system employs multi-objective optimization and fuzzy rules to generate explainable and executable dressing plans. Recommendations are presented through natural interactions combining large graphical icons, step-by-step guidance, and voice prompts. A user study with 100 participants aged ≥65 years was conducted, including usability testing and controlled comparison with traditional self-decision dressing. Outcome measures covered health risk avoidance, scenario adaptation accuracy, decision time, and subjective satisfaction. RESULTS: Compared with the traditional experience-based dressing strategy, the proposed system significantly improved health risk avoidance and scenario adaptation accuracy, shortened dressing decision time, and increased subjective satisfaction across multiple scenarios. Older participants were able to understand and follow the system’s recommendations with relatively low cognitive load, benefiting especially in complex or health-sensitive situations. CONCLUSION: This study integrates natural behavior theory with multimodal intelligent algorithms under a digital age-friendly design paradigm, and proposes a “behavior–context–environment coupling” human–AI co-decision framework for intelligent dressing. The results demonstrate that the WEHT-based multimodal dressing support system can effectively enhance safety, comfort, and contextual appropriateness of clothing decisions for older adults, while providing a theoretically grounded and practically feasible pathway for intelligent life-assistance systems in healthy aging.
Copyright (c) 2026 Ju Chen, Qiuying Xu, Tianyu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Value Regeneration–Driven Eco-design for Smart Wearables
The explosive growth of smart wearable devices has created a severe electronic-waste challenge. Existing eco-design research largely focuses on material circulation, with few studies—under a cross-disciplinary design lens—deeply integrating digital technologies and business models to achieve sustained “regeneration” of product value. Going beyond the traditional 4R framework, this study proposes a new eco-design paradigm for smart wearables that takes Regenerate as its core driving principle. The framework creatively integrates Product–Service Systems and Digital Twin technologies across the full design life cycle to realize data-driven value co-creation, extending the physical lifespan of the product while continuously enhancing its intangible digital value. Using a smartwatch as a case, we develop and evaluate three innovative design schemes. Results show that the Regenerate scheme—combining PSS and DT—reduces disassembly time by 73%, increases the reusable parts ratio to 78.5%, raises willingness to pay by 34.3%, and lowers global warming potential by 34.5% compared with the original design. This research provides a forward-looking theoretical framework and practical pathway for the sustainable development of smart wearables, and reveals the critical role of digital technologies in driving eco-design’s shift from “material circulation” to “value regeneration.”
Copyright (c) 2026 Haoran Wang, Guanghui Huang https://creativecommons.org/licenses/by-nc-sa/4.0