Research Review on Age-Friendly Interaction Design in Smart Home Environments: From Home Textile Interfaces to Mobile Feedback
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Abstract
With the in-depth promotion of the home-based elderly care model, the aging-adaptive design of human-computer interaction systems for smart homes has become an interdisciplinary frontier in engineering and design. However, embedded sensing technology for household textiles and aging-adaptive mobile interface design have long been advanced separately by different disciplinary communities, lacking an integrated perspective. Based on the collaborative design framework of “input interface + feedback interface”, this paper systematically sorts out the sensing mechanisms and aging-adaptive advantages of three types of household textile interfaces, including bedding, sofas & seating, and wearable textiles. It analyzes the integration and anomaly recognition processing paths of multi-source data from textile terminals in smart home systems, summarizes the laws of aging-adaptive mobile feedback design for elderly users and family caregivers, and reviews the methodological evolution of aging-adaptive human-computer interaction research. The review shows that: bedding textile interfaces have formed relatively mature engineering implementation approaches in sleep monitoring and vital sign collection, but low-amplitude signal noise suppression and body motion artifact separation remain the core bottlenecks restricting their popularization; sofa and seating interfaces exhibit significant potential in standing posture recognition and fall warning, and the problem of multi-sensor crosstalk urgently needs to be solved with individualized calibration mechanisms; wearable textile interfaces present outstanding all-weather sensing advantages, while washing durability and flexible connection reliability constitute the main engineering obstacles to long-term deployment. At the mobile feedback layer, semantic presentation for elderly users and multi-dimensional trend analysis for caregivers should be defined in a differentiated and collaborative manner at the system architecture stage, instead of sharing a single data view. These findings reveal that the collaborative design framework of textile-terminal sensing and mobile-terminal feedback is the key path to promote home-based elderly care systems from functional realization to in-depth user adaptation, and provide a theoretical reference for subsequent systematic design research and engineering practice.
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References
Y. Yan and H. Liu, “Artificial intelligence empowering home-based elderly care: Models, dilemmas, and pathways-An analysis based on the “technology-scenario-governance” frame-work,” Business Administration and Management, vol. 8, no. 1, 2026.
Y. Wang, B. Xin, W. Peng, et al., “AI investment decision in digital home-based elderly care service: A mean field game analysis,” Technovation, vol. 154, Art. no. 103549, 2026, doi: 10.1016/j.technovation.2026.103549.
C. Chokphukhiao, W. Tun T S, P. Duankhan, et al., “IoT-based health monitoring and social welfare access for Thailand’s older adults,” Frontiers in Digital Health, vol. 8, Art. no. 1696118, 2026, doi: 10.3389/fdgth.2026.1696118.
R. Jaberi, S. Kalhori N R, F. Bahador, et al., “Influencing factors and acceptance levels of robotic and smart home health technologies among older adults: A systematic review,” Health informatics journal, vol. 32, no. 2, Art. no. 14604582261436632, 2026, doi: 10.1177/14604582261436632.
Y. Wang, H. Sun, S. Xu, et al., “Smart Home Technologies for Enhancing Independence of Living and Reducing Care Dependence in Older Adults: A Systematic Review,” Journal of advanced nursing, vol. 81, no. 6, pp. 2885-2912, 2024, doi: 10.1111/jan.16569.
Y. Hsu L, C. Yi H, S. Lee C, et al., “RSSI-Based Localization of Smart Mattresses in Hospital Settings,” Journal of Low Power Electronics and Applications, vol. 16, no. 1, pp. 4, 2026, doi: 10.3390/jlpea16010004.
H, T. S, C. Hyein, L. Dayeon, et al., “Factors Associated With the Adoption of Smart Mattress in Family Caregivers: Crosssectional Study,” CIN: Computers, Informatics, Nursing, 2025.
G. Abir, C. Rob, and G. Rachael, “Development of a Smart Home Interface With Older Adults: Multi-Method Co-Design Study,” JMIR aging, vol. 6, Art. no. e44439, 2023, doi: 10.2196/44439.
Y. Zeng, S. Li, Y. Lu, et al., “Non-Intrusive Sleep Monitoring Mattress Based on Optical-Fiber Michelson Interferometer,” Photonics, vol. 12, no. 9, pp. 880, 2025, doi: 10.3390/photonics12090880.
L. Marco, A. Lucia, C. Nicola, et al., “A Smart Bed for Non-Obtrusive Sleep Analysis in Real World Context,” IEEE Access, vol. 8, pp. 45664-45673, 2020.
Z. Zicheng, J. Xinchen, W. Zechuan, et al., “A Feasibility Study on Smart Mattresses to Improve Sleep Quality,” Journal of healthcare engineering, vol. 2021, Art. no. 6127894, 2021, doi: 10.1155/2021/6127894.
Y. Song, Y. Zou, X. Shi, et al., “PANI E-skin Inspired by Microridge Structure and the Collaborative Mechanism of Mechanoreceptors for Elderly Health Monitoring and Intelligent Recognition of Abnormal Plantar Pressures,” Nano letters, 2026, doi: 10.1021/acs.nanolett.5c05640.
H. Sun, L. Li, L. Tao Q, et al., “An intelligent multifunction graphene skin patch for ear health monitoring and acoustic interaction,” Nano Energy, vol. 137, Art. no. 110790, 2025, doi: 10.1016/j.nanoen.2025.110790.
A. Vivek, K. Krutarth, M. Venkat, et al., “Development of Ti3C2Tx/NiSe2 Nanohybrid-Based Large-Area Pressure Sensors as a Smart Bed for Unobtrusive Sleep Monitoring,” Advanced Materials Interfaces, vol. 8, no. 18, 2021.
L. Zefeng, “Design of a Smart Home System for Independent Elderly Integrating Environmental Sensing and AI-Based Safety Monitoring,” International Journal of Healthcare Information Systems and Informatics (IJHISI), vol. 20, no. 1, pp. 1-21, 2025, doi: 10.4018/IJHISI.389199.
K. Chenxi, C. Yilun, Z. Tao, et al., “Research on intelligent mattress based on improved SMS structure sensing fiber,” Journal of Physics: Conference Series, vol. 1802, no. 2, Art. no. 022023, 2021, doi: 10.1088/1742-6596/1802/2/022023.
B. Mathunjwa M, R. Kor J Y, W. Ngarnkuekool, et al., “A Comprehensive Review of Home Sleep Monitoring Technologies: Smartphone Apps, Smartwatches, and Smart Mattresses,” Sensors, vol. 25, no. 6, pp. 1771, 2025, doi: 10.3390/s25061771.
Y. Feiying, C. Huiying, S. Yajuan, et al., “Preventing postoperative moderate-and high-risk pressure injuries with artificial intelligence-powered smart decompression mattress on in middle-aged and elderly patients: a retrospective cohort analysis,” British Journal of Hospital Medicine, vol. 85, no. 8, pp. 1-13, 2024, doi: 10.12968/hmed.2024.0112.
T, “S, D,” K, Divya M B, et al. Smart mattress integrated with pressure sensor and IoT functions for sleep apnea detection. Measurement: Sensors, pp. 24, 2022, doi: 10.1016/j.measen.2022.100450.
Z. Chengmin, H. Ting, L. Xin, et al., “Recognition and Analysis of an Age-Friendly Intelligent Sofa Design Based on Skeletal Key-Points,” International Journal of Environmental Research and Public Health, vol. 19, no. 18, Art. no. 11522, 2022, doi: 10.3390/ijerph191811522.
K. Hyunsoo, J. Jin S, L. Dong H, et al., “Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study,” JMIR formative research, vol. 7, Art. no. e47325, 2023, doi: 10.2196/47325.
S. Peng, K. Xu, S. Bao, et al., “Flexible Electrodes based Smart Mattress for Monitoring Physiological Signals of Heart and Autonomic Nerves in A Non-Contact Way,” IEEE Sensors Journal, 2020, doi: 10.1109/JSEN.2020.3012697.
C. Yao, L. Tao, and S. Liming, “Method and finite element verification of indentation calculation for a novel air-spring mattress: for estimating spinal alignment in sleep postures,” Engineering Computations, vol. 40, no. 9-10, pp. 2409-2431, 2023, doi: 10.1108/EC-05-2023-0192.
Z. Mi, J. Wenbin, and W. Jinfeng, “Design and manufacture of intelligent fabric-based insoles for disease prevention by monitoring plantar pressure,” Materials Today Communications, pp. 37, 2023, doi: 10.1016/j.mtcomm.2023.107646.
P. Wu, J. Gu, X. Liu, et al., “A Robust Core-Shell Nanofabric with Personal Protection, Health Monitoring and Physical Comfort for Smart Sportswear,” Advanced materials (Deerfield Beach, Fla.), vol. 36, no. 47, Art. no. e2411131, 2024, doi: 10.1002/adma.202411131.
T. Jian, W. YuTing, M. ShiDong, et al., “Fabricating a smart clothing system based on strain-sensing yarn and novel stitching technology for health monitoring,” Science China Technological Sciences, vol. 67, no. 2, pp. 587-596, 2024, doi: 10.1007/s11431-023-2442-9.
Z. Tao, R. Michael A, C. Hui, et al., “A Step Forward for Smart Clothes— Fabric-Based Micro-fluidic Sensors for Wearable Health Monitoring,” ACS sensors, vol. 7, no. 12, pp. 3857-3866, 2022, doi: 10.1021/acssensors.2c01827.
S. Fernandes, A. Ramos, M. Barbas V, et al., “Smart Textile Technology for the Monitoring of Mental Health,” Sensors, vol. 25, no. 4, pp. 1148, 2025, doi: 10.3390/s25041148.
Z. Zhiyuan, P. Maoqiu, and X. Zisheng, “Sleep monitoring based on triboelectric nanogenerator: wearable and washable approach,” Frontiers in psychiatry, vol. 14, Art. no. 1163003, 2023.
M, “L A, P,” I M, Raul F. A Breathable and Washable Smart Fabric for Pressure Sensing. Materials Proceedings, vol. 8, no. 1, pp. 37, 2022, doi: 10.3390/materproc2022008037.
A. Cruz M, M. Figeys, Y. Ahmed, et al., “High-Accuracy Indoor Positioning and Smart Home Technologies for Assessing and Monitoring Frailty in Older Adults,” Sensors, vol. 26, no. 1, pp. 113, 2025, doi: 10.3390/s26010113.
P. Gorce and J. Bret J, “Fall Detection in Elderly People: A Systematic Review of Ambient Assisted Living and Smart Home-Related Technology Performance,” Sensors, vol. 25, no. 21, pp. 6540, 2025, doi: 10.3390/s25216540.
M. Li and Y. Zhao, “Research on Age-Friendly Design of Smart Home in the Context of Home-Based Elderly Care Mode,” Journal of Management and Social Development, vol. 2, no. 4, 2025, doi: 10.62517/jmsd.202512413.
M. Islam R, M. Patwary O, and S. Islam A M, “AI-Driven Internet of Things (IoT) dataset for remote health monitoring and fall detection in elderly people,” Data in Brief, vol. 66, Art. no. 112641, 2026, doi: 10.1016/j.dib.2026.112641.
V. Thavavel, L. Laxmi E, S. Yacin M, et al., “Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare,” IEEE ACCESS, vol. 9, pp. 113879-113888, 2021.
L. Bu, C. Hua, W. Pan, et al., “Aging-Friendly Interactive Design for Smart Homes to Enhance Positive User Experiences for Older Adults,” Experimental aging research, pp. 1-26, 2026, doi: 10.1080/0361073X.2026.2626231.
Y. Lu, L. Zhou, A. Zhang, et al., “Research on Designing Context-Aware Interactive Experiences for Sustainable Aging-Friendly Smart Homes,” Electronics, vol. 13, no. 17, pp. 3507, 2024, doi: 10.3390/electronics13173507.
X. Liu, “User Interface Interaction Design of Age-Appropriate Smart Home Products,” Computer Informatization and Mechanical System, vol. 6, no. 2, pp. 69-74, 2023.
Y. Wang, M. Li, N. Chen, et al., “Older Adults’ Perspectives on Adopting Smart Home Technology for ‘Proactive Health’: A Qualitative Study,” Journal of advanced nursing, vol. 82, no. 1, pp. 606-616, 2025, doi: 10.1111/jan.16964.
Y. Dong, J. Sun, J. Boliboun, et al., “Identifying Knowledge Barriers for Smart Home Blood Pressure Monitoring Device Usage Among Minority Older Adults,” Innovation in Aging, vol. 9, no. Supplement_2, 2025, doi: 10.1093/geroni/igaf122.3342.
G. Abir, C. Rob, and G. Rachael, “Older adults’ perspectives of smart home technology: Are we developing the technology that older people want? International Journal of Human-Computer Studies,” vol. 147, Art, 2021. no. 102571.
Z. Chengmin, Z. Wenjing, H. Ting, et al., “An empirical study on the collaborative usability of age-appropriate smart home interface design,” Frontiers in Psychology, vol. 14, Art. no. 1097834, 2023.
W. Hongrui, L. Aihua, C. Ting, et al., “Study on behavioral risk for aging based on smart mattress monitoring data,” Procedia Computer Science, vol. 221, pp. 1276-1283, 2023.
K. Daejin, B. Hongyi, C. K C, et al., “In-Home Monitoring Technology for Aging in Place: Scoping Review,” Interactive journal of medical research, vol. 11, no. 2, Art. no. e39005, 2022, doi: 10.2196/39005.