The global trend toward longer life spans presents an opportunity to promote active and healthy aging. Physical exercises like Qigong support holistic well-being by integrating physical, cognitive, and emotional health. However, traditional programs lack adaptability to accommodate age-related changes in physical and cognitive abilities, which can limit accessibility and engagement for older adults. Virtual Reality (VR) offers a novel solution by creating immersive, customizable environments. In our study, we designed a VR-based physical exercise tutorial (VRPET) system and assessed the efficacy of using an adaptive self-avatar (i.e., a virtual representation of the user) to enhance user exercise performance and attitudes, while examining its impact on perceived workload. We conducted a two-phase mixed-methods investigation: (1) A formative phase involving focus group interviews (n=14), a consultation with a Qigong master on movement standardization, and a heuristic evaluation (n=5) to establish design requirements; (2) A user study (n = 30) that compared the self-avatar versus non-self-avatar conditions to assess their effects on perceived workload, exercise performance, and attitude metrics. Despite a significant dip in exercise performance ( p =0.03) and a non-significant increase in perceived workload ( p =0.58), participants expressed a preference for the self-avatar’s real-time feedback when scaffolded appropriately. Multi-modal analysis revealed auditory cues as most effective, followed by tactile and visual feedback. Based on these findings, we propose the ACT Framework (Adaptive, Cultural, Targeted) for developing age-appropriate VR exercise systems. Furthermore, we distill our iterative process into a tripartite validation workflow, advocating for a methodology that harmonizes user desirability, expert safety, and HCI usability. These evidence-based insights advance the design of therapeutic VR interventions that can support healthy aging populations.

Sarcopenia, characterized by progressive loss of muscle mass and function, significantly impacts the quality of life in aging populations. Early detection and personalized intervention are crucial yet challenging due to the limited accessibility and scalability of traditional diagnostic methods. Building upon our previous work on gait-based assessment, this study presents a novel framework for early sarcopenia-prone risk detection using the five-time sit-to-stand (5TSTS) test, embodying Healthcare Industry 5.0’s vision of mass personalization with human-centered technology. Utilizing the Internet of Things (IoT)-enabled wearable inertial measurement units (IMUs) and advanced analytics, our system segments 5TSTS into four biomechanically significant submotions [standing up (StU), standing transition (StT), sitting down (SiD), and sitting transition (SiT)]. This granular segmentation allows mass personalization in diagnostic evaluations by capturing individual-specific biomechanical profiles via wavelet-based feature extraction and machine learning (ML) techniques. Our framework employs big data analytics tools, including the extreme gradient boosting (XGBoost)-based feature selection and support vector machine synthetic minority oversampling technique (SVMSMOTE), to handle class imbalance and optimize individualized predictive accuracy. Tested on data from 52 elderly participants (aged 65–84 years), the system achieves outstanding personalized classification accuracy—up to 97.97% for multiclass risk stratification and 99.28% for binary (healthy versus sarcopenia-prone) classification—highlighting its potential for precise, patient-specific clinical decision-making. Furthermore, the wireless capability of our IoT-enabled wearable IMUs, coupled with minimal setup requirements, facilitates seamless data integration into cloud-based healthcare systems. This integration supports real-time remote monitoring and personalized health management. By leveraging advanced sensing, analytics, and connectivity technologies, our approach significantly advances personalized, accessible, and scalable sarcopenia-prone risk assessment, thereby contributing directly to the vision of Healthcare Industry 5.0.

A machine-learning system is constructed to alleviate chronic knee pain through exercise and muscle strengthening. Three user-focused features are offered: video-based exercise demonstrations, real-time posture analysis and feedback, and performance and progress tracking. This system, which functions as an artificially-intelligent “technological surrogate physiotherapist,” applies human-computer incentive compatibility and joint learning-by-doing to reify and strengthen motivation, trust and acceptance and to increase effectiveness and efficacy, initial exacerbation of knee pain notwithstanding. In a 3-week experiment involving 60 individuals carrying chronic knee pain, positive and statistically significant outcomes were recorded regarding the Western Ontario and McMaster Universities Osteoarthritis Index physical function (p = 0.001), quality of life (EQ-5D-5L: < 0.001; EQ VAS: p = 0.004), exercise engagement (p < 0.001), system usability, and system acceptance. Technology-based solutions hold significant promise for improving future clinical practice by reducing professional resource demand and increasing the accessibility and caregiver-patient incentive compatibility under physiological healthcare.

Sarcopenia is a progressive condition characterized by age-related losses in muscle mass and strength, and irreversible in its advanced stages. While sarcopenia negatively impacts daily living, accurately, quickly and economically assessing its effects can be challenging due to individual variability in activity levels. This study introduced a novel approach for assessing the risk of sarcopenia-prone using machine learning and wearable Internet of Things (IoT) sensors. A total of 53 community-dwelling older adults aged 65+ underwent gait analysis using dual sensors. Nineteen gait features were extracted from each cycle and used to train classification algorithms to categorize participants as healthy, risk level 1, risk level 2, or risk level 3 for sarcopenia. Binary classification of healthy versus sarcopenic-prone achieved 97.41% accuracy on average, while four-class classification averaged 94.67%. Notably, the research discovered worsening gait symmetry with increasing sarcopenia-prone severity. These results indicate IoT sensor-assessed gait may serve as a sensitive indicator for daily sarcopenia-prone screening. Accurate assessment of sarcopenia-prone individuals can be achieved through only a 4-m walking test, significantly reducing the burden for older adults. This approach offers a cost-effective, convenient, and accurate method for early sarcopenia risk detection and intervention, potentially improving quality of life for older adults. This system could also aid in creating widely applicable monitoring products for assessing sarcopenia risk, supporting IoT, and thereby enabling early identification and intervention for individuals at risk of this condition.

Teleoperation systems find many applications from earlier search-and-rescue to more recent daily tasks. It is widely acknowledged that using external sensors can decouple the view of the remote scene from the motion of the robot arm during manipulation, facilitating the control task. However, this design requires the coordination of multiple operators or may exhaust a single operator as s/he needs to control both the manipulator arm and the external sensors. To address this challenge, our work introduces a viewpoint prediction model, the first data-driven approach that autonomously adjusts the viewpoint of a dynamic camera to assist in telemanipulation tasks. This model is parameterized by a deep neural network and trained on a set of human demonstrations. We propose a contrastive learning scheme that leverages viewpoints in a camera trajectory as contrastive data for network training. We demonstrated the effectiveness of the proposed viewpoint prediction model by integrating it into a real-world robotic system for telemanipulation. User studies reveal that our model outperforms several camera control methods in terms of control experience and reduces the perceived task load compared to manual camera control. As an assistive module of a telemanipulation system, our method significantly reduces task completion time for users who choose to adopt its recommendation.

OBJECTIVE: The study sought to examine the effects of technology-supported exercise programs on the knee pain, physical function, and quality of life of individuals with knee osteoarthritis and/or chronic knee pain by a systematic review and meta-analysis of randomized controlled trials. MATERIALS AND METHODS: We searched MEDLINE, EMBASE, CINAHL Plus, and the Cochrane Library from database inception to August 2020. A meta-analysis and subgroup analyses, stratified by technology type and program feature, were conducted. RESULTS: Twelve randomized controlled trials were reviewed, all of which implemented the programs for 4 weeks to 6 months. Telephone, Web, mobile app, computer, and virtual reality were used to deliver the programs. The meta-analysis showed that these programs were associated with significant improvements in knee pain (standardized mean difference [SMD] = -0.29; 95% confidence interval [CI], -0.48 to -0.10; P = .003) and quality of life (SMD = 0.25; 95% CI, 0.04 to 0.46; P = .02) but not with significant improvement in physical function (SMD = 0.22; 95% CI, 0 to 0.43; P = .053). Subgroup analyses showed that some technology types and program features were suggestive of potential benefits. CONCLUSIONS: Using technology to deliver the exercise programs appears to offer benefits. The technology types and program features that were associated with health values have been identified, based on which suggestions are discussed for the further research and development of such programs.

OBJECTIVE: With the advent of personal health records and other patient-focused health technologies, there is a growing need to better understand factors that contribute to acceptance and use of such innovations. In this study, we employed the Unified Theory of Acceptance and Use of Technology as the basis for determining what predicts patients' acceptance (measured by behavioral intention) and perceived effective use of a web-based, interactive self-management innovation among home care patients. DESIGN: Cross-sectional secondary analysis of data from a randomized field study evaluating a technology-assisted home care nursing practice with adults with chronic cardiac disease. MEASUREMENT AND ANALYSIS: A questionnaire was designed based on validated measurement scales from prior research and was completed by 101 participants for measuring the acceptance constructs as part of the parent study protocol. Latent variable modeling with item parceling guided assessment of patients' acceptance. RESULTS: Perceived usefulness accounted for 53.9% of the variability in behavioral intention, the measure of acceptance. Together, perceived usefulness, health care knowledge, and behavioral intention accounted for 68.5% of the variance in perceived effective use. Perceived ease of use and subjective norm indirectly influenced behavioral intention, through perceived usefulness. Perceived ease of use and subjective norm explained 48% of the total variance in perceived usefulness. CONCLUSION: The study demonstrates that perceived usefulness, perceived ease of use, subjective norm, and healthcare knowledge together predict most of the variance in patients' acceptance and self-reported use of the web-based self-management technology.

A systematic literature review was performed to identify variables promoting consumer health information technology (CHIT) acceptance among patients. The electronic bibliographic databases Web of Science, Business Source Elite, CINAHL, Communication and Mass Media Complete, MEDLINE, PsycArticles, and PsycInfo were searched. A cited reference search of articles meeting the inclusion criteria was also conducted to reduce misses. Fifty-two articles met the selection criteria. Among them, 94 different variables were tested for associations with acceptance. Most of those tested (71%) were patient factors, including sociodemographic characteristics, health- and treatment-related variables, and prior experience or exposure to computer/health technology. Only ten variables were related to human-technology interaction; 16 were organizational factors; and one was related to the environment. In total, 62 (66%) were found to predict acceptance in at least one study. Existing literature focused largely on patient-related factors. No studies examined the impact of social and task factors on acceptance, and few tested the effects of organizational or environmental factors on acceptance. Future research guided by technology acceptance theories should fill those gaps to improve our understanding of patient CHIT acceptance, which in turn could lead to better CHIT design and implementation.