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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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
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
Dual Drivers of Experience and Trust: Exploring the Mechanisms of Elderly User Adoption of AI-HVAs from a UTAUT Perspective
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 model in the field of digital technology adoption by older adults, revealing the complex psychological processes underlying their acceptance of AI-HVAs. On a practical level, the findings provide important insights for the age-friendly design, trust-building, and promotion strategies of AI health products.
Copyright (c) 2026 Xiang Wang, Jia-Bei Jiang, Jian-Jun Hou, Xiao-Yang Zhu https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Wearable Technologies for Autonomous Mental Health Monitoring in the Elderly: A Systematic Review and Design Perspectives
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.
Copyright (c) 2026 Niki Vogka, Modestos Stavrakis https://creativecommons.org/licenses/by-nc-sa/4.0
Better Neutral: A Wearable Sound-Based Biofeedback System for Enhancing Pelvic Alignment in Pilates
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.
Copyright (c) 2024 Eunjung Park, Jeanho Cho, Yongsoon Choi https://creativecommons.org/licenses/by-nc-sa/4.0
IoMT and Data Privacy in Alzheimer’s Care for Older Adults: A Systematic Review
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.
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
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
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.
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
Exploring Diverse Features: A Thorough Survey for Anxiety Disorders
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.
Copyright (c) 2024 Rakhi Nagpal, Saravjeet Singh, Aditi Moudgil https://creativecommons.org/licenses/by-nc-sa/4.0
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
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.
Copyright (c) 2024 Anthony Duffy, Sarah E Bennett, Lucy Yardley, Sylvain Moreno https://creativecommons.org/licenses/by-nc-sa/4.0
Towards PTSD Diagnosis Through ECG Anomaly Detection based on Autoencoders
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.
Copyright (c) 2024 Vasileios Skaramagkas, Ioannis Kyprakis, Georgia Karanasiou, Dimitrios Fotiadis, Manolis Tsiknakis https://creativecommons.org/licenses/by-nc-sa/4.0
Privacy-Preserving Human Motion Analysis for Lower Back Pain Stratification through Federated Learning
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.
Copyright (c) 2024 Liyun Gong, Miao Yu, Saeid Pourroostaei https://creativecommons.org/licenses/by-nc-sa/4.0
Privacy-Preserving Abnormal Gait Detection Using Computer Vision and Machine Learning
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.
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
The Future of Fall Prevention: Integrating OpenPose with Cutting-Edge ML Models
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.
Copyright (c) 2024 Shina Samuel Kolawole, Gautam Siddharth Kashyap, Olamide Emmanuel Kolawole, Miao Yu https://creativecommons.org/licenses/by-nc-sa/4.0
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
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.
Copyright (c) 2024 Dimitrios Kollias, Anastasios Arsenos, James Wingate, Stefanos Kollias https://creativecommons.org/licenses/by-nc-sa/4.0