Jeanne Tan

In dementia care, multisensory stimulation is a widely adopted nonpharmacological intervention that supports emotional wellbeing, engagement, and social interaction, especially in later stages of the disease. However, existing sensory tools often stimulate only a single sense and are not specifically designed with the needs of older adults in mind. To address this gap, this study presents the design and development of a novel interactive textile tool, multisensory illuminated cushions (MICs), using a multidisciplinary codesign process involving textile designers, engineers, and occupational therapists (OTs). The MICs are portable cushion-based devices that integrate smart textiles with capacitive touch sensing, RGB illumination via polymeric optical fibers (POFs), and audio playback. Developed with human-centered design principles, they offer intuitive, multisensory engagement through light, sound, and tactile interaction, tailored for individuals with late-stage dementia. Four MIC prototypes were evaluated in clinical settings by OTs during sensory stimulation sessions. Results showed that participants demonstrated significantly higher engagement levels with MICs compared with a market-available sensory cushion, as measured by the observational measurement of engagement. Positive emotional and behavioral responses were observed from all users. The key innovation of this study lies in combining scalable textile manufacturing with embedded interactive electronics in a washable, modular, and user-comfort-oriented form. Unlike previous handcrafted or fixed installations, MICs offer a practical, adaptable, and production-ready solution for dementia care environments. This research contributes a novel framework for integrating smart textile technology into dementia-focused design and highlights the value of collaborative, human-centered methods for developing inclusive healthcare tools.

The integration of artificial intelligence (AI) into textile design enhances functionality, automation, and user interaction. While gesture recognition has been explored in smart textiles, contactless interactive systems for healthcare remain underdeveloped. This study presents a human-centered co-design approach to the development of an AI-integrated gesture recognition system embedded in illuminative textile wall panels, aimed at enhancing spatial engagement in healthcare environments. The research was conducted in three key stages. First, a co-design workshop was conducted to explore user preferences in textile materials, graphic design, and gesture interaction. Second, intelligent illuminative textiles were developed by knitting polymeric optical fiber into base wool yarns to enable illumination. A camera was embedded and integrated with a computer vision-based deep learning model for detecting landmarks on the hands, shoulders, and head. The recognized gestures and body movements triggered specific pre-programmed color changes on the textile surface through edge-integrated light-emitting diodes. Finally, a prototype was fabricated and installed in a government-established District Health Centre in Hong Kong to support physical activity and rehabilitation for elderly users. Semi-structured interviews with stakeholders – including co-designers, users, and occupational therapists – were conducted to evaluate usability and inform design refinements. Stakeholders reported high levels of satisfaction, emphasizing the system’s ability to enhance community connection, therapeutic engagement, intuitive usability, and compelling visual feedback. These findings suggest that AI-driven interactive textiles present promising opportunities for rehabilitation, therapeutic environments, and the promotion of elderly well-being.

Background: The rapid expansion of the global aging population, combined with caregiver shortages and rising care demands, highlights the urgent need for innovative, non-intrusive, and user-centered solutions in elderly healthcare.

Heating electronic textiles (e-textiles) are widely used for thermal comfort and energy conservation, but prolonged heating raises concerns about heat-related illnesses, especially in the elderly. Despite advancements, achieving universal user satisfaction remains difficult due to diverse thermal needs. This paper introduces an intelligent thermochromic heating e-textile with an artificial intelligence (AI)-based temperature control system for optimized personal comfort and color indicators for elderly caregivers. The fabric integrates conductive yarn, temperature-induced discoloration yarn (TIDY), and polymeric optical fiber (POF) to visualize temperature changes, ensuring efficiency and comfort. Equipped with microcontrollers, ambient sensors, and Bluetooth connectivity, the system offers comprehensive intelligent heating solutions. An AI model, trained on data from 50 wearability test subjects, determines optimal heating temperatures (40-50 °C) with 5.083 mean squared error (MSE), showing a high correlation between predicted and actual comfort levels. This concept enhances thermal comfort and mitigates overheating risks, promising for wearable healthcare applications.

Human thermal comfort, crucial for well-being and productivity, is often improved by personal comfort systems that offer tailored control over environmental conditions while promoting energy efficiency. Previous studies have explored various textile technologies in thermoregulation systems according to user preferences. However, limited research has focused on temperature prediction by artificial intelligence to maximize thermal comfort for varied users. This study proposes a design approach to optimize thermal comfort in electric heating textiles using artificial intelligence, considering user preferences related to age and gender differences. A fuzzy logic model is established as a proof of concept for temperature regulation by varying ambient temperature, followed by developing an artificial neural network model to predict the optimal temperature for maximum comfort. Subsequently, a smart electric heating jacket is fabricated to assess preferred heating temperatures among 50 subjects with varying ages and genders. Results from the artificial neural network model show promising temperature prediction, while subject tests reveal significant differences in skin temperatures based on gender. This emphasizes the need for artificial intelligence-based heating e-textiles to accommodate diverse user needs. The study’s findings are expected to contribute to intelligent temperature regulation in thermal textiles and wearables, benefitting both the industry and consumers through customized heating products.

In the field of human-computer interaction (HCI), recent years have witnessed an increasing interest in smart textiles with gesture recognition functions. This paper reports usability evaluations of a prototype gesture-controlled illuminative textile developed by the authors. This is an interactive woven POF textile system with computer vision-enabled mid-air gesture recognition. For the usability test, 25 participants (11 males and 14 females) aged between 19 and 56 (M = 30.85, SD = 10.8) were randomly recruited. The study was conducted by the means of the user experience questionnaire (UEQ) and the system usability scale (SUS). Results show that participants responded positively to our gesture-controlled illuminative textile in terms of its attractiveness, perspicuity and stimulation but were less positive about its efficiency. The results will be used to make targeted improvements to the existing product design.

For the integration of conductive yarns in e-textiles, knitting offers structural versatility and malleability for wider product applications in the contexts of wearables and interiors. To enable mass adoption of conductive materials, it is imperative for users to be able to launder these materials as part of product maintenance. Interactive textiles knitted from polymeric optical fibres (POFs) and silver-coated conductive yarns are able to illuminate and change colours via integrated touch sensor systems. Current research only focuses on the washability and abrasion resistance of conductive yarns solely and not both POF and conductive yarn within the same fabric structure. This study is novel as it investigates the washability and abrasion resistance of POF and silver-coated conductive yarn integrated knitted textiles with different loop structures and the impact to their illuminative function. POFs were knitted within the same fabric structure by the inlay method using a 7-gauge industrial hand-operated flatbed knitting machine. This study examined how washing and abrasion affect POFs and silver-coated conductive yarn in five different knit structures, and the illuminative function of the knitted textiles. Washing and abrasion affected the resistance of conductive yarns. Scratches and bent POFs were observed after 20 gentle washing cycles. However, washing had minimal impact on the illuminative function of the knitted e-textiles examined in this study. The experiments provide evidence that e-textiles knitted with POFs and conductive yarns in the same fabric structure withstand washing and abrasion and thus have the potential for mass market adoption in fashion and interior applications.

Thermal electronic textiles (e-textiles) are increasingly common in the market and aim to provide thermal comfort in cold environments. Currently, however, there is a paucity of information on the relative effectiveness of the different types of thermal e-textile. This study sets out to compare the effectiveness of a range of such textiles reported in the relevant literature. The study addresses the issue of heating effectiveness in terms of gender and particular body regions. Among the results reported, three primary categories emerged for investigation: (a) metabolic response, comprising metabolic rate and heart rate; (b) respiratory response i.e. mean skin temperature; and (c) perceptual response, including thermal sensation and comfort sensation. Eight eligible studies with a total of 83 subjects aged 23.4 ± 1.49 years, 40.96% male and 37.35% female and 21.69% in which gender was not reported, were investigated for the meta-analysis. Results show a significant improvement in changes in mean skin temperature and thermal sensation between the control and experimental groups via heating intervention ( p < 0.05), but results were not significant for changes in metabolic rate, heart rate, and comfort sensation ( p > 0.05). Regarding gender, thermal e-textiles significantly enhanced the changes in mean skin temperature in males and thermal sensation in both males and females ( p < 0.05). With regard to different body regions being heated, there were significant effects on changes in mean skin temperature, thermal sensation, and comfort sensation in heating the upper torso ( p < 0.05) and on changes in thermal sensation in heating the feet and toes ( p < 0.05). The results reported here may serve as reference points for the further development of smart heating textile technology.