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: 3.7), 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: 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: 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
items: Active Prevention of Nature-Deficit Disorder in Urban Children: An IoT-Based Interactive Ecological Intervention System Design
items: A Three-Arm Randomized Controlled Trial of Cross-Modal Digital Health System Integrated with Cognitive Behavioral Therapy for Insomnia: Neurophysiological Mechanisms and Clinical Efficacy
items: The Mapping Relationship Between Emotion and the Laws of Formal Beauty: Construction and Validation
items: Research on a Multimodal Intelligent Dressing System and Digital Age-Friendly Design Based on Natural Behavior Theory
items: Value Regeneration–Driven Eco-design for Smart Wearables
items: Dual Drivers of Experience and Trust: Exploring the Mechanisms of Elderly User Adoption of AI-HVAs from a UTAUT Perspective
items: Smart Wearable Technologies for Autonomous Mental Health Monitoring in the Elderly: A Systematic Review and Design Perspectives
items: Better Neutral: A Wearable Sound-Based Biofeedback System for Enhancing Pelvic Alignment in Pilates
items: IoMT and Data Privacy in Alzheimer’s Care for Older Adults: A Systematic Review
items: Study Results of mCARE: Developing, Deploying, and Analysing the End-to-End Results of a Mobile-Based Remote Monitoring Tool for Children with Autism Spectrum Disorder in Bangladesh
items: Exploring Diverse Features: A Thorough Survey for Anxiety Disorders
items: Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development – A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens
items: Towards PTSD Diagnosis Through ECG Anomaly Detection based on Autoencoders
items: Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning
items: Privacy-Preserving Abnormal Gait Detection Using Computer Vision and Machine Learning
items: The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models
items: SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection

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

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

Active Prevention of Nature-Deficit Disorder in Urban Children: An IoT-Based Interactive Ecological Intervention System Design
type: item
description: 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 tha…
subject: Digital Health and Product Innovation Design
  topic: section
subject: Nature-deficit disorder
  topic: keywords
subject: user research
  topic: keywords
subject: digital health
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: gamification
  topic: keywords
subject: system design
  topic: keywords
subject: preventive medicine
  topic: keywords
creator: Cheng Zhai
creator: HongChen Li
creator: Yan Chen
creator: Yuyang Zeng
creator: Shanshan Liang
rights: Copyright (c) 2026 Cheng Zhai, HongChen Li, Yan Chen, Yuyang Zeng, Shanshan Liang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-19
publication date: 2026-01-19
doi: 10.4108/eetpht.11.11036

A Three-Arm Randomized Controlled Trial of Cross-Modal Digital Health System Integrated with Cognitive Behavioral Therapy for Insomnia: Neurophysiological Mechanisms and Clinical Efficacy
type: item
description: 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 th…
subject: Art Therapy and Health Engineering
  topic: section
subject: cross-modal integration
  topic: keywords
subject: predictive coding
  topic: keywords
subject: CBT-I
  topic: keywords
subject: insomnia disorder
  topic: keywords
subject: neurophysiology
  topic: keywords
creator: Caihong He
creator: Siyu Ying
creator: Yadong Xu
creator: Fangtian Ying
rights: Copyright (c) 2026 Caihong He, Siyu Ying, Yadong Xu, Fangtian Ying https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-13
publication date: 2026-01-13
doi: 10.4108/eetpht.11.11044

The Mapping Relationship Between Emotion and the Laws of Formal Beauty: Construction and Validation
type: item
description: 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 pref…
subject: Art Therapy and Health Engineering
  topic: section
subject: Emotion
  topic: keywords
subject: Laws of Formal Beauty
  topic: keywords
subject: Emotional Design
  topic: keywords
subject: Bidirectional Mapping
  topic: keywords
subject: Pschophysiological Measurement
  topic: keywords
creator: Juan Jin
creator: Haiwen Wang
rights: Copyright (c) 2026 Juan Jin, Haiwen Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-15
publication date: 2026-01-15
doi: 10.4108/eetpht.11.11043

Research on a Multimodal Intelligent Dressing System and Digital Age-Friendly Design Based on Natural Behavior Theory
type: item
description: 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…
subject: Digital Health and Product Innovation Design
  topic: section
subject: Natural Behavior Theory
  topic: keywords
subject: Multimodal Data Fusion
  topic: keywords
subject: Intelligent Dressing System
  topic: keywords
subject: Digital Age-Friendly Design
  topic: keywords
subject: Human–Computer Interaction
  topic: keywords
creator: Ju Chen
creator: Qiuying Xu
creator: Tianyu Chen
rights: Copyright (c) 2026 Ju Chen, Qiuying Xu, Tianyu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-13
publication date: 2026-01-13
doi: 10.4108/eetpht.11.11032

Value Regeneration–Driven Eco-design for Smart Wearables
type: item
description: 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.”
subject: Digital Health and Product Innovation Design
  topic: section
subject: Eco-design
  topic: keywords
subject: Smart Wearable Devices
  topic: keywords
subject: Regeneration Principle
  topic: keywords
subject: Product-Service Systems
  topic: keywords
subject: Digital Twin
  topic: keywords
creator: Haoran Wang
creator: Guanghui Huang
rights: Copyright (c) 2026 Haoran Wang, Guanghui Huang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-21
publication date: 2026-01-21
doi: 10.4108/eetpht.11.11031

Dual Drivers of Experience and Trust: Exploring the Mechanisms of Elderly User Adoption of AI-HVAs from a UTAUT Perspective
type: item
description: INTRODUCTION: With the accelerating aging of the population and the integrated development of artificial intelligence (AI) technology, AI health voice assistants (AI-HVAs) present a novel approach for enhancing health management among older adults. However, the adoption of this technology by the elderly population still faces multiple barriers, including cognition, trust, and user experience, and its adoption mechanisms have yet to be fully elucidated. OBJECTIVES: This study aims to construct an AI-HVA adoption model applicable to China's elderly population, focusing on revealing the dual driving role of “experience (experiential rationality)” and “trust (relational rationality)” in the decision-making process of elderly users. METHODS: Integrating the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study introduces two key variables—"Perceived AI Experience (PAIE)" and "Perceived AI Trust (PAIT)"—to form a dual-path hypothesis of "internal experience-external influence." Through a questionnaire survey of 413 elderly users, structural equation modeling (SEM) was employed to analyze the data and examine the influence relationships among variables. RESULTS: (1)Internal Experience Path:PAIE significantly and positively influenced Performance Expectancy (PE) and Effort Expectancy (EE), and also directly promoted Behavioral Intention (BI). This indicates that the quality of the interaction experience is a key antecedent for elderly users forming perceptions of usefulness and ease of use . (2) External Influence Path:Social Influence (SI) did not exert a direct effect on BI but required mediation through PAIT, highlighting the pivotal bridging role of trust in the adoption decision . (3) BI and Facilitating Conditions (FC) jointly significantly promoted Usage Behavior (UB), supporting the applicability of the UTAUT model in the context of AI technology adoption among the elderly. CONCLUSION: This study extends the explanatory boundaries of the UTAUT…
subject: Digital Health and Product Innovation Design
  topic: section
subject: AI-HVAs
  topic: keywords
subject: elderly users
  topic: keywords
subject: UTAUT
  topic: keywords
subject: PAIE
  topic: keywords
subject: PAIT
  topic: keywords
subject: dual perspective
  topic: keywords
creator: Xiang Wang
creator: Jiabei Jiang
creator: Jianjun Hou
creator: Xiaoyang Zhu
rights: Copyright (c) 2026 Xiang Wang, Jia-Bei Jiang, Jian-Jun Hou, Xiao-Yang Zhu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-08
publication date: 2026-01-08
doi: 10.4108/eetpht.11.11028

Smart Wearable Technologies for Autonomous Mental Health Monitoring in the Elderly: A Systematic Review and Design Perspectives
type: item
description: INTRODUCTION: The increasing prevalence of mental health issues among older adults has generated interest in smart wearable technologies as tools for emotion recognition and depression monitoring. However, their application in ageing populations remains underexplored, and there is no established set of design guidelines tailored to the needs and contexts of older users. OBJECTIVES: This paper aims to review current research on wearable technologies for mental health monitoring in older adults and to identify key design considerations to inform future development. METHODS: A systematic literature review was conducted following PRISMA 2020 guidelines. Studies from 1988 to 2025 were included if they examined the use of smart wearables for detecting emotional or depressive states in older adults, or if broader age ranges were analysed in ways that explicitly addressed ageing-related factors or design considerations. RESULTS: The review revealed notable advances in sensor-based and contactless emotion recognition. However, most systems lacked empirical validation with older users, and usability, privacy, and ethical concerns were frequently unaddressed. Few studies adopted age-specific methodologies or considered the cognitive and physical characteristics of older adults. CONCLUSION: While wearable technologies show potential for supporting autonomous mental health care in older adults, their effectiveness depends on user-centred and ethically responsible design. This paper identifies the absence of standardised guidelines and outlines preliminary principles to inform future interdisciplinary work. Given the limited number of eligible studies involving older adults, the findings should be considered exploratory and indicative rather than generalisable across broader ageing populations.
subject: Review article
  topic: section
subject: Smart Wearables
  topic: keywords
subject: Emotion Recognition
  topic: keywords
subject: Autonomous Mental Health Monitoring
  topic: keywords
subject: Depression Detection
  topic: keywords
subject: Elderly Mental Health
  topic: keywords
creator: Niki Vogka
creator: Modestos Stavrakis
rights: Copyright (c) 2026 Niki Vogka, Modestos Stavrakis https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-07
publication date: 2026-01-07
doi: 10.4108/eetpht.11.10883

Better Neutral: A Wearable Sound-Based Biofeedback System for Enhancing Pelvic Alignment in Pilates
type: item
description: INTRODUCTION: This paper introduces *Better Neutral*, a low-cost wearable IoT system designed to enhance Pilates practice through sound-based biofeedback. The system aims to address the challenge of maintaining proper pelvic alignment—particularly during supine exercises where visual monitoring is limited—by providing intuitive auditory cues instead of visual or continuous corrective feedback. OBJECTIVES: The main objective of this research is to develop and evaluate an embodied interaction system that promotes somatic awareness and self-correction through subtle, non-intrusive auditory feedback. The goal is to support practitioners in recognizing and adjusting pelvic alignment independently during Pilates sessions. METHODS: The device employs a linear potentiometer to detect pelvic tilt and converts pressure variations into minimalist sound patterns. A user-specific calibration framework with adaptive thresholds ensures stable, individualized responses using commodity sensors. Grounded in soma design principles and informed by techniques derived from social network analysis, the system’s feedback design emphasizes interpretive engagement rather than prescriptive correction. RESULTS: A preliminary study with four participants across eight Pilates sessions generated 19,200 seconds of sensor data and qualitative reflections. Findings indicate improved pelvic control, heightened somatic awareness, and perceived support for autonomous practice. Participants recognized the auditory cues as both informative and non-distracting. CONCLUSION: This paper demonstrates how evocative sound feedback can foster embodied self-correction and engagement without overwhelming the user. This approach shows promise not only for Pilates practice but also for broader applications in posture training, rehabilitation, and preventive care.
subject: Research article
  topic: section
subject: Wearable IoT
  topic: keywords
subject: Auditory biofeedback
  topic: keywords
subject: Pilates
  topic: keywords
subject: Pelvic alignment
  topic: keywords
subject: Posture monitoring
  topic: keywords
subject: Neutral Position
  topic: keywords
subject: Somatic Interactions
  topic: keywords
creator: Eunjung Park
creator: Jeanho Cho
creator: Yongsoon Choi
rights: Copyright (c) 2024 Eunjung Park, Jeanho Cho, Yongsoon Choi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-11-10
publication date: 2025-11-10
doi: 10.4108/eetpht.11.10859

IoMT and Data Privacy in Alzheimer’s Care for Older Adults: A Systematic Review
type: item
description: INTRODUCTION: The use of medical Internet of Things (IoT) devices becomes essential in everyday healthcare routines as the older adults confront an increasing risk of cyber victimization because of Alzheimer's illness. Despite the myriad benefits of IoT devices, escalating concerns about data privacy and cybersecurity loom larger, given the cognitive and physical decline associated with Alzheimer's. OBJECTIVES: Focusing on the challenges faced by older adults, especially those with Alzheimer's, this study aims to investigate the data privacy and security risks and advocates for specialized education and user-friendly interfaces to narrow the digital divide. METHODS: Eighteen peer-reviewed articles were selected based on predefined inclusion criteria, focusing on older adults or Alzheimer's patients, IoMT in healthcare, and data privacy or security concerns. Searches were conducted in PubMed and IEEE Xplore, following PRISMA 2020 guidelines. A structured data extraction matrix informed by Grounded Theory was used to chart and analyze key themes across selected studies, including types of vulnerabilities, consequences, and proposed solutions. RESULTS: Recurring vulnerabilities included social engineering, data breaches, weak authentication, and poor access control. Effective mitigation strategies identified include patient and caregiver education, improved informed consent procedures, robust encryption, and data governance reforms. CONCLUSION: Smart home technology and the digitization of the healthcare field have shown great promise in caring for Alzheimer's patients, positively affecting their ability to live on their own safely. They also create new challenges with the need to protect the sensitive information they gather. Our research emphasizes developing strategies that can create awareness and educate patients and their caregivers to further reduce the risks related to data security.
subject: Review article
  topic: section
subject: Data Privacy
  topic: keywords
subject: Smart Health
  topic: keywords
subject: Alzhemier's
  topic: keywords
subject: Older Adults
  topic: keywords
subject: Internet of Medical Things (IoMT)
  topic: keywords
creator: Fahim Islam Anik
creator: Md Mehedi Hasan
creator: Juan Rodriguez-Cardenas
creator: Richard Beiswanger
creator: Masrura Tasnim
creator: Mary Ramos
creator: Nazmus Sakib
rights: Copyright (c) 2024 Fahim Islam Anik, Md Mehedi Hasan, Juan Rodriguez-Cardenas, Richard Beiswanger, Masrura Tasnim, Mary Ramos, Nazmus Sakib https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-09-18
publication date: 2025-09-18
doi: 10.4108/eetpht.11.6170

Study Results of mCARE: Developing, Deploying, and Analysing the End-to-End Results of a Mobile-Based Remote Monitoring Tool for Children with Autism Spectrum Disorder in Bangladesh
type: item
description: Mental health is one of the most neglected healthcare issues in developing countries. The situation is worse in the Global South due to stigma, superstitions, and many other social-cultural-financial constraints. Lack of mental health professionals and regular monitoring has deprived families raising children with Autism Spectrum Disorder (ASD) of the desired care and support. However, the overwhelming adoption of mobile phones in Bangladesh has created an unprecedented opportunity to overcome these various constraints. To leverage this opportunity, we designed, developed, and evaluated mCARE (Mobile-Based Care for Children with Autism Spectrum Disorder Using Remote Experience Sampling Method), a mobile application that integrates the Experience Sampling Method (ESM) with the local healthcare practice by putting the caregivers in the loop. mCARE collected behavioral and developmental progress parameter values from the caregivers of 300 children with ASD periodically (daily/ weekly/ biweekly/ monthly) along with sociodemographic data of the family. This paper reports: (a) the context and challenge identification phases that validate the requirement of a mobile based tool; (b) evolution of mCARE following Value Sensitive Design; (c) short term and long-term impact analysis (qualitative and quantitative) of mCARE, and; (d) broader implications of these findings for the HCI scholarship along with the impact of mCARE during COVID-19.
subject: Research article
  topic: section
subject: Autism Spectrum Disorder
  topic: keywords
subject: mHealth
  topic: keywords
subject: symptom monitoring
  topic: keywords
subject: COVID-19
  topic: keywords
creator: Md M. Haque
creator: Md Ishrak Islam
creator: Masud Rabbani
creator: Dipranjan Das
creator: Amy Schwichtenberg
creator: Naveen Bansal
creator: Tanjir Rashid Soron
creator: Shaheen Akhter
creator: Shahana Parveen
creator: Azima Begum
creator: Mohammad Shaha A Patwary
creator: Austin Schmidt
creator: Brandon Franczak
creator: Syed Ishtiaque Ahmed
creator: Sheikh Iqbal Ahamed
rights: Copyright (c) 2024 Md M. Haque, Md Ishrak Islam, Masud Rabbani, Dipranjan Das, Amy Schwichtenberg, Naveen Bansal, Tanjir Rashid Soron, Shaheen Akhter, Shahana Parveen, Azima Begum, Mohammad Shaha A Patwary, Austin Schmidt, Brandon Franczak, Syed Ishtiaque Ahmed, Sheikh Iqbal Ahamed https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-08-26
publication date: 2025-08-26
doi: 10.4108/eetpht.11.9981

Exploring Diverse Features: A Thorough Survey for Anxiety Disorders
type: item
description: INTRODUCTION: The prevalence of mental health issues, mainly anxiety disorders, has risen significantly in today’s fast paced world. Thus, giving the imperative to confront the challenges associated in identifying these issues. OBJECTIVE: The objective of this review paper is to signify the importance of different features or parameters (acoustic, prosodic, linguistic, facial, neuroimaging, psychological, and physiological) being extracted from different data modalities (audio, video, psychological, physiological, textual, and neuroimaging) that are being used to assess different kinds of mental disorders. METHODS: Considering the systematic literature review technique, a total of 102 studies have been identified in the field of anxiety disorders and mental health issues spanning the years 2015 to 2024. By considering diverse features being used to diagnose different kinds of anxiety disorders, this paper provides a foundation for future research that will help researchers to design the new strategies and techniques to handle the anxiety disorder. CONCLUSION: This comprehensive review paper outlines the details of diverse features extracted across various data modalities, contributing significantly to the prediction of a wide range of anxiety disorders.
subject: Review article
  topic: section
subject: Anxiety Disorder
  topic: keywords
subject: Mental Illness
  topic: keywords
subject: Acoustic features
  topic: keywords
subject: Disorder Identification
  topic: keywords
creator: Rakhi Nagpal
creator: Saravjeet Singh
creator: Aditi Moudgil
rights: Copyright (c) 2024 Rakhi Nagpal, Saravjeet Singh, Aditi Moudgil https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-06
publication date: 2025-06-06
doi: 10.4108/eetpht.11.5475

Crowdsourcing Remote Co-design Towards Improving the Validity and Reliability of mHealth Application Development – A Case Study on Sleep Solved: A mHealth App Designed Virtually with Teens
type: item
description: INTRODUCTION: Co-design has become a fundamental pillar of formative digital health research. Typically, this approach involves in–person workshops that involve a rich but limited amount of data. Virtually crowdsourcing co-design, however, provides the promise of rapid and vastly increased data. This is a novel, exploratory approach in mHealth design that may appease common health research concerns surrounding reliability and validity, whilst providing swifter feedback to meet product development timelines. OBJECTIVES: The objective of this exploratory single case study was to explore the virtual, crowdsourced, co-design of Sleep Solved, an educational mHealth sleep app designed with teens. In doing so, we wished to learn which virtual methods were used to engage teens in the co-design and to explore how these virtual co-design methods can be adapted for large-scale ideation and testing. METHODS: We conducted an enquiry-based iterative case study utilising the Bayazit 3-stage model. 85 teens participated over 11 months. Data was thematically analysed over several design iterations. RESULTS: Rapid virtual feedback allowed for quick pivots in a short time frame. Four stages of feedback from teens led to iterative changes to scientific information contextualisation and user experience, from lo-fidelity mock-ups through to a coded app beta. CONCLUSION: The co-design of Sleep Solved exemplified the potential of virtually crowdsourcing teens in mHealth. Key to this evolution will be the ability to leverage big data utilising AI and machine learning approaches to data collation and synthesization, such that meaningful and contextual findings can be applied in line with software development timelines.
subject: Pervasive Health 2024
  topic: section
subject: mHealth
  topic: keywords
subject: digital health citizens
  topic: keywords
subject: co-design
  topic: keywords
subject: crowdsourcing
  topic: keywords
subject: user experience
  topic: keywords
subject: person-based design
  topic: keywords
subject: sleep
  topic: keywords
subject: teens
  topic: keywords
creator: Anthony Duffy
creator: Sarah E Bennett
creator: Lucy Yardley
creator: Sylvain Moreno
rights: Copyright (c) 2024 Anthony Duffy, Sarah E Bennett, Lucy Yardley, Sylvain Moreno https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-02
publication date: 2025-06-02
doi: 10.4108/eetpht.11.9416

Towards PTSD Diagnosis Through ECG Anomaly Detection based on Autoencoders
type: item
description: INTRODUCTION: Post-Traumatic Stress Disorder (PTSD) is a debilitating mental health condition that can develop after exposure to traumatic events, often resulting in symptoms that severely impair daily functioning. Current diagnostic methods largely rely on subjective assessments, highlighting the need for objective, non-invasive tools to improve diagnostic precision. OBJECTIVES: This study aims to develop and validate an innovative deep learning approach using autoencoder neural networks to detect PTSD through analysis of electrocardiography (ECG) signals. The goal is to provide a reliable and sophisticated diagnostic method that bridges computational and clinical domains. METHODS: We employed autoencoder neural networks to analyze ECG data collected from wearable heart zone sensors. This unsupervised learning model was trained to detect subtle anomalies in the ECG signals that may serve as biomarkers for PTSD. The methodology was evaluated using data collected from individuals with and without PTSD symptoms. RESULTS: The proposed model demonstrated strong potential as an objective diagnostic tool, successfully identifying patterns in ECG signals associated with PTSD. The analysis confirmed the model’s ability to distinguish PTSD-related anomalies with 83% accuracy. CONCLUSION: This research introduces a novel, non-invasive diagnostic methodology for PTSD using deep learning and wearable ECG data. The findings support the model's value as a potential objective biomarker, contributing to more precise psychiatric diagnostics and expanding the role of machine learning in healthcare.
subject: Pervasive Health 2024
  topic: section
subject: PTSD Diagnosis
  topic: keywords
subject: autoencoder
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: ECG
  topic: keywords
subject: deep learning in healthcare
  topic: keywords
creator: Vasileios Skaramagkas
creator: Ioannis Kyprakis
creator: Georgia Karanasiou
creator: Dimitrios Fotiadis
creator: Manolis Tsiknakis
rights: Copyright (c) 2024 Vasileios Skaramagkas, Ioannis Kyprakis, Georgia Karanasiou, Dimitrios Fotiadis, Manolis Tsiknakis https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-06-02
publication date: 2025-06-02
doi: 10.4108/eetpht.11.9463

Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning
type: item
description: Human Gait Analysis is crucial in healthcare applications, with numerous research works focusing on machine learning and deep learning approaches for tasks such as abnormal gait detection and gait quality assessment. However, developing such models requires collecting and sharing a significant amount of patient data, raising privacy concerns. In this study, we introduce the world’s first technique for constructing a deep neural network model to stratify patients’ pain levels based on video recordings of timed up-and-go activities, while ensuring privacy preservation through modern federated learning algorithms. Our experimental results demonstrate the effectiveness of this technique in accurately stratifying LBP levels without the need for data sharing among local clients to maintain privacy.
subject: BDTA 2024
  topic: section
subject: Federated Learning
  topic: keywords
subject: Attention Model
  topic: keywords
subject: Gait Analysis
  topic: keywords
subject: Healthcare
  topic: keywords
creator: Liyun Gong
creator: Miao Yu
creator: Saeid Pourroostaei Ardakani
rights: Copyright (c) 2024 Liyun Gong, Miao Yu, Saeid Pourroostaei https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-17
publication date: 2025-04-17
doi: 10.4108/eetpht.11.9109

Privacy-Preserving Abnormal Gait Detection Using Computer Vision and Machine Learning
type: item
description: Gait analysis plays a pivotal role in diagnosing a spectrum of neurological and musculoskeletal disorders. Variations in gait patterns often serve as early indicators of underlying health conditions, underscoring the importance of precise and timely analysis for effective intervention and treatment. In recent years, computer vision techniques have emerged as robust tools for automated gait analysis, offering non-invasive, costeffective, and scalable solutions. However, existing approaches often overlook the critical aspect of privacy preservation. In this study, we propose the world’s pioneering computer vision-based abnormal gait detection system with a privacy-preserving mechanism. Specifically, we extract 2D skeletons from encrypted images using a deep neural network model, which is facilitated by an optical system incorporating a custom-made refractive optical element. These extracted features are then fed into machine learning models for the detection of normal versus abnormal gait patterns. Evaluations across various models including random forest, decision tree, K-nearest neighbor, support vector machine, neural network, and convolutional neural network reveal that the random forest model attains the highest classification performance based on 2D skeletons extracted from encrypted images.
subject: BDTA 2024
  topic: section
subject: Computer Vision
  topic: keywords
subject: Gait Analysis
  topic: keywords
subject: Abnormal Gait Detection
  topic: keywords
creator: Afreen Naz
creator: Pandey Shourya Prasad
creator: Sheldon Mccall
creator: Chan Chi Leung
creator: Ifeoma Ochi
creator: Liyun Gong
creator: Miao Yu
rights: Copyright (c) 2024 Afreen Naz, Pandey Shourya Prasad, Sheldon Mccall, Chan Chi Leung, Ifeoma Ochi, Liyun Gong, Miao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-16
publication date: 2025-04-16
doi: 10.4108/eetpht.11.9094

The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models
type: item
description: The research paper aims to assess ML models for video-recorded gaits with an aim of classifying people into high or low risks to fall groups. Several ML algorithms were tried employing OpenPose for CV, with RF showing the best outcomes: 93% accuracy along with F1-score as well as balanced sensitivity (93.50%) as well as specificity (92.50%). Some important determining factors were speed per unit distance, angle among other statistical measures. In comparison to wearables-based DL approaches plus traditional fall detection methods, this study’s approach showed higher accuracy and adaptability within health care settings.
subject: BDTA 2024
  topic: section
subject: Deep Learning
  topic: keywords
subject: Fall
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: OpenPose
  topic: keywords
creator: Shina Samuel Kolawole
creator: Gautam Siddharth Kashyap
creator: Olamide Emmanuel Kolawole
creator: Miao Yu
rights: Copyright (c) 2024 Shina Samuel Kolawole, Gautam Siddharth Kashyap, Olamide Emmanuel Kolawole, Miao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-02
publication date: 2025-04-02
doi: 10.4108/eetpht.11.9013

SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
type: item
description: This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medical images (3-D chest CT scans) for Covid-19 detection. Our approach includes a combination of vision-language models that segment the CT scans, which are then fed to a deep neural architecture, named RACNet, for Covid-19 detection. In particular, a novel framework, named SAM2CLIP2SAM, is introduced for segmentation that leverages the strengths of both Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to accurately segment the right and left lungs in CT scans, subsequently feeding these segmented outputs into RACNet for classification of COVID-19 and non-COVID-19 cases. At first, SAM produces multiple part-based segmentation masks for each slice in the CT scan; then CLIP selects only the masks that are associated with the regions of interest (ROIs), i.e., the right and left lungs; finally SAM is given these ROIs as prompts and generates the final segmentation mask for the lungs. Experiments are presented across two Covid-19 annotated databases which illustrate the improved performance obtained when our method has been used for segmentation of the CT scans.
subject: BDTA 2024
  topic: section
subject: RACNet
  topic: keywords
subject: SAM
  topic: keywords
subject: CLIP
  topic: keywords
subject: segmentation
  topic: keywords
subject: classification
  topic: keywords
subject: Covid-19 detection
  topic: keywords
subject: COV-19 CT-DB
  topic: keywords
creator: Dimitrios Kollias
creator: Anastasios Arsenos
creator: James Wingate
creator: Stefanos Kollias
rights: Copyright (c) 2024 Dimitrios Kollias, Anastasios Arsenos, James Wingate, Stefanos Kollias https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2025-04-02
publication date: 2025-04-02
doi: 10.4108/eetpht.11.9010